@article{
 title = {Variability in estimating shunt from single pulse oximetry measurements},
 type = {article},
 year = {2015},
 identifiers = {[object Object]},
 keywords = {corresponding author,of arterial oxygen saturation,oximeter accuracy on estimations,the impact of pulse},
 id = {423cbc61-3e92-3050-bec9-ab0a672cff7b},
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 authored = {true},
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 bibtype = {article},
 author = {Karlen, Walter and Petersen, Christian L and Dumont, Guy A and Ansermino, J Mark},
 journal = {Physiological measurement}
}

@article{
 title = {A cohort study of morbidity, mortality and health seeking behavior following rural health center visits by children under 12 in Southwestern Uganda},
 type = {article},
 year = {2015},
 identifiers = {[object Object]},
 pages = {e0118055},
 volume = {10},
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 file_attached = {true},
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 citation_key = {Wiens2014a},
 bibtype = {article},
 author = {Wiens, Matthew O and Gan, Heng and Barigye, Celestine and Zhou, Guohai and Kumbakumba, Elias and Kabakyenga, Jerome and Kissoon, Niranjan and Ansermino, J Mark and Karlen, Walter and Larson, Charles P and MacLeod, Stuart M},
 journal = {PLoS ONE},
 number = {1}
}

@article{
 title = {Estimation of Respiratory Rate from Photoplethysmographic Imaging Videos Compared to Pulse Oximetry},
 type = {article},
 year = {2015},
 identifiers = {[object Object]},
 pages = {online first},
 websites = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7101812},
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 bibtype = {article},
 author = {Karlen, Walter and Garde, Ainara and Myers, Dorothy and Scheffer, Cornie and Ansermino, J. Mark and Dumont, Guy A},
 journal = {IEEE Journal of Biomedical and Health Informatics}
}

@article{
 title = {Efficiency of respiratory rate measurements: Comment on Black et al ., 2015 : ' Can simple mobile phone applications provide reliable counts of respiratory rates in sick infants and children? An initial evaluation of three new applications'},
 type = {article},
 year = {2015},
 identifiers = {[object Object]},
 pages = {1279-80},
 volume = {52},
 websites = {http://dx.doi.org/10.1016/j.ijnurstu.2015.03.014},
 publisher = {Elsevier Ltd},
 id = {54a0e229-5aba-3faf-bf50-8cfde6250331},
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 starred = {false},
 authored = {true},
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 hidden = {false},
 citation_key = {Karlen2015},
 bibtype = {article},
 author = {Karlen, Walter and Dunsmuir, Dustin and Ansermino, J Mark},
 journal = {International Journal of Nursing Studies},
 number = {7}
}

@inBook{
 title = {Medical Devices and Information Communication Technologies for the Base of the Pyramid},
 type = {inBook},
 year = {2015},
 identifiers = {[object Object]},
 pages = {113-118},
 websites = {http://link.springer.com/10.1007/978-3-319-16247-8,http://link.springer.com/10.1007/978-3-319-16247-8_11},
 publisher = {Springer International Publishing},
 chapter = {11},
 editors = {[object Object],[object Object],[object Object]},
 id = {b9769477-3aec-3a57-9fb6-05155c9597c0},
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 author = {Friedman, Zach and Karlen, Walter},
 book = {Technologies for Development. What is Essential?}
}

@article{
 title = {Improving the accuracy and efficiency of respiratory rate measurements in children using mobile devices.},
 type = {article},
 year = {2014},
 identifiers = {[object Object]},
 pages = {e99266},
 volume = {9},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/24919062,http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4053345&tool=pmcentrez&rendertype=abstract},
 month = {1},
 id = {e539d539-50c7-33ec-839f-cb90fa144f45},
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 accessed = {2014-08-07},
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 last_modified = {2015-08-19T14:23:28.000Z},
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 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2014},
 abstract = {The recommended method for measuring respiratory rate (RR) is counting breaths for 60 s using a timer. This method is not efficient in a busy clinical setting. There is an urgent need for a robust, low-cost method that can help front-line health care workers to measure RR quickly and accurately. Our aim was to develop a more efficient RR assessment method. RR was estimated by measuring the median time interval between breaths obtained from tapping on the touch screen of a mobile device. The estimation was continuously validated by measuring consistency (% deviation from the median) of each interval. Data from 30 subjects estimating RR from 10 standard videos with a mobile phone application were collected. A sensitivity analysis and an optimization experiment were performed to verify that a RR could be obtained in less than 60 s; that the accuracy improves when more taps are included into the calculation; and that accuracy improves when inconsistent taps are excluded. The sensitivity analysis showed that excluding inconsistent tapping and increasing the number of tap intervals improved the RR estimation. Efficiency (time to complete measurement) was significantly improved compared to traditional methods that require counting for 60 s. There was a trade-off between accuracy and efficiency. The most balanced optimization result provided a mean efficiency of 9.9 s and a normalized root mean square error of 5.6%, corresponding to 2.2 breaths/min at a respiratory rate of 40 breaths/min. The obtained 6-fold increase in mean efficiency combined with a clinically acceptable error makes this approach a viable solution for further clinical testing. The sensitivity analysis illustrating the trade-off between accuracy and efficiency will be a useful tool to define a target product profile for any novel RR estimation device.},
 bibtype = {article},
 author = {Karlen, Walter and Gan, Heng and Chiu, Michelle and Dunsmuir, Dustin and Zhou, Guohai and Dumont, Guy A and Ansermino, J Mark},
 journal = {PLoS ONE},
 number = {6}
}

The recommended method for measuring respiratory rate (RR) is counting breaths for 60 s using a timer. This method is not efficient in a busy clinical setting. There is an urgent need for a robust, low-cost method that can help front-line health care workers to measure RR quickly and accurately. Our aim was to develop a more efficient RR assessment method. RR was estimated by measuring the median time interval between breaths obtained from tapping on the touch screen of a mobile device. The estimation was continuously validated by measuring consistency (% deviation from the median) of each interval. Data from 30 subjects estimating RR from 10 standard videos with a mobile phone application were collected. A sensitivity analysis and an optimization experiment were performed to verify that a RR could be obtained in less than 60 s; that the accuracy improves when more taps are included into the calculation; and that accuracy improves when inconsistent taps are excluded. The sensitivity analysis showed that excluding inconsistent tapping and increasing the number of tap intervals improved the RR estimation. Efficiency (time to complete measurement) was significantly improved compared to traditional methods that require counting for 60 s. There was a trade-off between accuracy and efficiency. The most balanced optimization result provided a mean efficiency of 9.9 s and a normalized root mean square error of 5.6%, corresponding to 2.2 breaths/min at a respiratory rate of 40 breaths/min. The obtained 6-fold increase in mean efficiency combined with a clinically acceptable error makes this approach a viable solution for further clinical testing. The sensitivity analysis illustrating the trade-off between accuracy and efficiency will be a useful tool to define a target product profile for any novel RR estimation device.

@inProceedings{
 title = {Sharing Vital Signs between Mobile Phone Applications},
 type = {inProceedings},
 year = {2014},
 pages = {3646-9},
 id = {2c896f34-5f39-381f-aedb-9283543bec23},
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 citation_key = {Karlen2014a},
 bibtype = {inProceedings},
 author = {Karlen, Walter and Dumont, Guy A and Scheffer, Cornie},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society.}
}

@book{
 title = {Mobile Point-of-Care Monitors and Diagnostic Device Design},
 type = {book},
 year = {2014},
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 websites = {http://www.crcpress.com/product/isbn/9781466589292},
 publisher = {CRC Press},
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 citation_key = {Karlen2014b},
 bibtype = {book},
 author = {}
}

@inProceedings{
 title = {Sleep Stage Classification in Children Using Photoplethysmogram Pulse Rate Variability},
 type = {inProceedings},
 year = {2014},
 pages = {297-300},
 id = {65e137f6-83e7-3632-85b9-836b8fa69d8c},
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 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Dehkordi2014},
 bibtype = {inProceedings},
 author = {Dehkordi, Parastoo and Garde, Ainara and Karlen, Walter and Wensley, David and Ansermino, J. Mark and Dumont, Guy A},
 booktitle = {Computing in Cardiology Conference (CinC)}
}

@inProceedings{
 title = {Design of an interactive medical guideline application for community health workers.},
 type = {inProceedings},
 year = {2014},
 identifiers = {[object Object]},
 pages = {1366-1369},
 id = {ff4b2fa6-6f1b-3e6b-ab3f-8f0e762b6978},
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 last_modified = {2015-08-24T08:10:07.000Z},
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 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2014k},
 abstract = {Clinical guidelines, such as the Integrated Management of Childhood Illness (IMCI), are used worldwide to support community health workers in the assessment of severely ill children. These guidelines are distributed in paper form, complicating their use at the point-of-care. We have developed a framework for building advanced clinical guideline applications for the Android mobile phone OS. The framework transfers clinical guidelines into a flexible and interactive electronic format using an XML interpreter. The resulting application supports intuitive navigation of guidelines while assessing the patient, easy integration of patient management tools, and logging of performed assessments and treatments. The novel approach transforms clinical guidelines from a mere paper dictionary into a working tool that integrates into the daily workflow of community health workers and simplifies their task at the care and administrative levels.},
 bibtype = {inProceedings},
 author = {Karlen, Walter and Scheffer, Cornie},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society.}
}

Clinical guidelines, such as the Integrated Management of Childhood Illness (IMCI), are used worldwide to support community health workers in the assessment of severely ill children. These guidelines are distributed in paper form, complicating their use at the point-of-care. We have developed a framework for building advanced clinical guideline applications for the Android mobile phone OS. The framework transfers clinical guidelines into a flexible and interactive electronic format using an XML interpreter. The resulting application supports intuitive navigation of guidelines while assessing the patient, easy integration of patient management tools, and logging of performed assessments and treatments. The novel approach transforms clinical guidelines from a mere paper dictionary into a working tool that integrates into the daily workflow of community health workers and simplifies their task at the care and administrative levels.

@inProceedings{
 title = {Respiratory Rate Assessment from Photoplethysmographic Imaging},
 type = {inProceedings},
 year = {2014},
 pages = {5397-400},
 publisher = {IEEE Engineering in Medicine and Biology Society},
 city = {Chicago,IL,USA},
 id = {fc5fa8f0-0c68-3516-b9af-b3fdca4fa6bd},
 created = {2014-08-20T17:55:28.000Z},
 file_attached = {true},
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 last_modified = {2014-12-08T11:42:19.000Z},
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 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2014c},
 bibtype = {inProceedings},
 author = {Karlen, Walter and Garde, Ainara and Myers, Dorothy and Scheffer, Cornie and Ansermino, J Mark and Dumont, Guy A},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society}
}

@inProceedings{
 title = {Oxygen Saturation Resolution Influences Regularity Measurements},
 type = {inProceedings},
 year = {2014},
 identifiers = {[object Object]},
 pages = {2257-60},
 publisher = {IEEE Engineering in Medicine and Biology Society},
 city = {Chicago},
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 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Garde2014c},
 bibtype = {inProceedings},
 author = {Garde, Ainara and Karlen, Walter and Dehkordi, Parastoo and Ansermino, J Mark and Dumont, Guy A},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society}
}

@article{
 title = {Development of a screening tool for sleep disordered breathing in children using the phone oximeter™.},
 type = {article},
 year = {2014},
 identifiers = {[object Object]},
 pages = {e112959},
 volume = {9},
 websites = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4234680&tool=pmcentrez&rendertype=abstract},
 month = {1},
 id = {3c7e454c-b9f2-32ad-b462-e4b69e0b94fc},
 created = {2014-09-19T20:26:30.000Z},
 accessed = {2014-11-26},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2015-06-03T20:55:19.000Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Garde2014b},
 abstract = {BACKGROUND: Sleep disordered breathing (SDB) can lead to daytime sleepiness, growth failure and developmental delay in children. Polysomnography (PSG), the gold standard to diagnose SDB, is a highly resource-intensive test, confined to the sleep laboratory. AIM: To combine the blood oxygen saturation (SpO2) characterization and cardiac modulation, quantified by pulse rate variability (PRV), to identify children with SDB using the Phone Oximeter, a device integrating a pulse oximeter with a smartphone. METHODS: Following ethics approval and informed consent, 160 children referred to British Columbia Children's Hospital for overnight PSG were recruited. A second pulse oximeter sensor applied to the finger adjacent to the one used for standard PSG was attached to the Phone Oximeter to record overnight pulse oximetry (SpO2 and photoplethysmogram (PPG)) alongside the PSG. RESULTS: We studied 146 children through the analysis of the SpO2 pattern, and PRV as an estimate of heart rate variability calculated from the PPG. SpO2 variability and SpO2 spectral power at low frequency, was significantly higher in children with SDB due to the modulation provoked by airway obstruction during sleep (p-value [Formula: see text]). PRV analysis reflected a significant augmentation of sympathetic activity provoked by intermittent hypoxia in SDB children. A linear classifier was trained with the most discriminating features to identify children with SDB. The classifier was validated with internal and external cross-validation, providing a high negative predictive value (92.6%) and a good balance between sensitivity (88.4%) and specificity (83.6%). Combining SpO2 and PRV analysis improved the classification performance, providing an area under the receiver operating characteristic curve of 88%, beyond the 82% achieved using SpO2 analysis alone. CONCLUSIONS: These results demonstrate that the implementation of this algorithm in the Phone Oximeter will provide an improved portable, at-home screening tool, with the capability of monitoring patients over multiple nights.},
 bibtype = {article},
 author = {Garde, Ainara and Dehkordi, Parastoo and Karlen, Walter and Wensley, David and Ansermino, J. Mark and Dumont, Guy A.},
 journal = {PLoS ONE},
 number = {11}
}

BACKGROUND: Sleep disordered breathing (SDB) can lead to daytime sleepiness, growth failure and developmental delay in children. Polysomnography (PSG), the gold standard to diagnose SDB, is a highly resource-intensive test, confined to the sleep laboratory. AIM: To combine the blood oxygen saturation (SpO2) characterization and cardiac modulation, quantified by pulse rate variability (PRV), to identify children with SDB using the Phone Oximeter, a device integrating a pulse oximeter with a smartphone. METHODS: Following ethics approval and informed consent, 160 children referred to British Columbia Children's Hospital for overnight PSG were recruited. A second pulse oximeter sensor applied to the finger adjacent to the one used for standard PSG was attached to the Phone Oximeter to record overnight pulse oximetry (SpO2 and photoplethysmogram (PPG)) alongside the PSG. RESULTS: We studied 146 children through the analysis of the SpO2 pattern, and PRV as an estimate of heart rate variability calculated from the PPG. SpO2 variability and SpO2 spectral power at low frequency, was significantly higher in children with SDB due to the modulation provoked by airway obstruction during sleep (p-value [Formula: see text]). PRV analysis reflected a significant augmentation of sympathetic activity provoked by intermittent hypoxia in SDB children. A linear classifier was trained with the most discriminating features to identify children with SDB. The classifier was validated with internal and external cross-validation, providing a high negative predictive value (92.6%) and a good balance between sensitivity (88.4%) and specificity (83.6%). Combining SpO2 and PRV analysis improved the classification performance, providing an area under the receiver operating characteristic curve of 88%, beyond the 82% achieved using SpO2 analysis alone. CONCLUSIONS: These results demonstrate that the implementation of this algorithm in the Phone Oximeter will provide an improved portable, at-home screening tool, with the capability of monitoring patients over multiple nights.

@inProceedings{
 title = {Detrended fluctuation analysis of photoplethysmogram pulse rate intervals in sleep disordered breathing},
 type = {inProceedings},
 year = {2014},
 identifiers = {[object Object]},
 pages = {323-326},
 websites = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7038940},
 publisher = {IEEE Engineering in Medicine and Biology Society},
 city = {Seattle},
 id = {fc710ddb-05cc-3253-ab53-70afb92c239a},
 created = {2014-10-07T11:32:23.000Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2015-06-03T20:55:19.000Z},
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 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Dehkordi2014a},
 bibtype = {inProceedings},
 author = {Dehkordi, Parastoo and Garde, Ainara and Karlen, Walter and Petersen, Christian L. and Ansermino, Mark J. and Dumont, A. Guy},
 booktitle = {2014 IEEE Healthcare Innovation Conference (HIC)}
}

@inProceedings{
 title = {Assessing the Quality of Manual Respiratory Rate Measurements using Mobile Devices},
 type = {inProceedings},
 year = {2014},
 keywords = {2,3,be inaccurate,implications for pneumonia diagnosis,measurement confidence,ment,mobile devices,quality assess-,respiratory rate,rr is known to,the manual assessment of,this can have severe,us-},
 pages = {in press},
 publisher = {IET},
 city = {London, UK},
 id = {7889864c-c0ba-3e72-a508-89f9576e695f},
 created = {2014-10-06T10:58:32.000Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2015-08-19T14:23:27.000Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2014d},
 bibtype = {inProceedings},
 author = {Karlen, W and Wiens, M O and Gan, H and Dunsmuir, D and Chiu, M and Dumont, G A and Ansermino, J M},
 booktitle = {Appropriate Healthcare Technologies AHT'14}
}

@article{
 title = {Estimating respiratory and heart rates from the correntropy spectral density of the photoplethysmogram.},
 type = {article},
 year = {2014},
 identifiers = {[object Object]},
 pages = {e86427},
 volume = {9},
 websites = {http://dx.plos.org/10.1371/journal.pone.0086427,http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3899260&tool=pmcentrez&rendertype=abstract},
 month = {1},
 day = {22},
 editors = {[object Object]},
 id = {9cde86ba-981b-359d-b4c6-47dbffabce4f},
 created = {2014-02-02T14:15:44.000Z},
 accessed = {2014-01-24},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2014-12-28T12:40:55.000Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Garde2014},
 abstract = {The photoplethysmogram (PPG) obtained from pulse oximetry measures local variations of blood volume in tissues, reflecting the peripheral pulse modulated by heart activity, respiration and other physiological effects. We propose an algorithm based on the correntropy spectral density (CSD) as a novel way to estimate respiratory rate (RR) and heart rate (HR) from the PPG. Time-varying CSD, a technique particularly well-suited for modulated signal patterns, is applied to the PPG. The respiratory and cardiac frequency peaks detected at extended respiratory (8 to 60 breaths/min) and cardiac (30 to 180 beats/min) frequency bands provide RR and HR estimations. The CSD-based algorithm was tested against the Capnobase benchmark dataset, a dataset from 42 subjects containing PPG and capnometric signals and expert labeled reference RR and HR. The RR and HR estimation accuracy was assessed using the unnormalized root mean square (RMS) error. We investigated two window sizes (60 and 120 s) on the Capnobase calibration dataset to explore the time resolution of the CSD-based algorithm. A longer window decreases the RR error, for 120-s windows, the median RMS error (quartiles) obtained for RR was 0.95 (0.27, 6.20) breaths/min and for HR was 0.76 (0.34, 1.45) beats/min. Our experiments show that in addition to a high degree of accuracy and robustness, the CSD facilitates simultaneous and efficient estimation of RR and HR. Providing RR every minute, expands the functionality of pulse oximeters and provides additional diagnostic power to this non-invasive monitoring tool.},
 bibtype = {article},
 author = {Garde, Ainara and Karlen, Walter and Ansermino, J. Mark and Dumont, Guy A.},
 journal = {PLoS ONE},
 number = {1}
}

The photoplethysmogram (PPG) obtained from pulse oximetry measures local variations of blood volume in tissues, reflecting the peripheral pulse modulated by heart activity, respiration and other physiological effects. We propose an algorithm based on the correntropy spectral density (CSD) as a novel way to estimate respiratory rate (RR) and heart rate (HR) from the PPG. Time-varying CSD, a technique particularly well-suited for modulated signal patterns, is applied to the PPG. The respiratory and cardiac frequency peaks detected at extended respiratory (8 to 60 breaths/min) and cardiac (30 to 180 beats/min) frequency bands provide RR and HR estimations. The CSD-based algorithm was tested against the Capnobase benchmark dataset, a dataset from 42 subjects containing PPG and capnometric signals and expert labeled reference RR and HR. The RR and HR estimation accuracy was assessed using the unnormalized root mean square (RMS) error. We investigated two window sizes (60 and 120 s) on the Capnobase calibration dataset to explore the time resolution of the CSD-based algorithm. A longer window decreases the RR error, for 120-s windows, the median RMS error (quartiles) obtained for RR was 0.95 (0.27, 6.20) breaths/min and for HR was 0.76 (0.34, 1.45) beats/min. Our experiments show that in addition to a high degree of accuracy and robustness, the CSD facilitates simultaneous and efficient estimation of RR and HR. Providing RR every minute, expands the functionality of pulse oximeters and provides additional diagnostic power to this non-invasive monitoring tool.

@article{
 title = {Monitoring nociception during general anesthesia with cardiorespiratory coherence.},
 type = {article},
 year = {2013},
 identifiers = {[object Object]},
 keywords = {antinociception á analgesia,arrhythmia  nociception,cardiorespiratory coherence  heart rate,variability  respiratory sinus},
 pages = {551-60},
 volume = {27},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/23568315},
 month = {10},
 day = {9},
 id = {f1198b30-d988-3c97-bae1-e20d0b6cefa3},
 created = {2013-04-12T14:12:01.000Z},
 accessed = {2013-09-26},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2014-11-28T11:16:47.000Z},
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 hidden = {false},
 citation_key = {Brouse2013a},
 abstract = {A novel wavelet transform cardiorespiratory coherence (WTCRC) algorithm has been developed to measure the autonomic state. WTCRC may be used as a nociception index, ranging from 0 (no nociception, strong coherence) to 100 (strong nociception, low coherence). The aim of this study is to estimate the sensitivity of the algorithm to nociception (dental dam insertions) and antinociception (bolus doses of anesthetic drugs). WTCRC's sensitivity is compared to mean heart rate (HRmean) and mean non-invasive blood pressure (NIBPmean), which are commonly used clinical signs. Data were collected from 48 children receiving general anesthesia during dental surgery. The times of dental dam insertion and anesthetic bolus events were noted in real-time during surgeries. 42 dental dam insertion and 57 anesthetic bolus events were analyzed. The change in average WTCRC, HRmean, and NIBPmean was calculated between a baseline period before each event and a response period after. A Wilcoxon rank-sum test was used to compare changes. Dental dam insertion changed the WTCRC nociception index by an average of 14 (82 %) [95 % CI from 7.4 to 19], HRmean by 7.3 beats/min (8.1 %) [5.6-9.6], and NIBPmean by 8.3 mmHg (12 %) [4.9-13]. A bolus dose of anesthetics changed the WTCRC by -15 (-50 %) [-21 to -9.3], HRmean by -4.8 beats/min (4.6 %) [-6.6 to -2.9], and NIBPmean by -2.6 mmHg (3.4 %) [-4.7 to -0.50]. A nociception index based on cardiorespiratory coherence is more sensitive to nociception and antinociception than are HRmean or NIBPmean. The WTCRC algorithm shows promise for noninvasively monitoring nociception during general anesthesia.},
 bibtype = {article},
 author = {Brouse, Chris J and Karlen, Walter and Dumont, Guy A and Myers, Dorothy and Cooke, Erin and Stinson, Jonathan and Lim, Joanne and Ansermino, J Mark},
 journal = {Journal of clinical monitoring and computing},
 number = {5}
}

A novel wavelet transform cardiorespiratory coherence (WTCRC) algorithm has been developed to measure the autonomic state. WTCRC may be used as a nociception index, ranging from 0 (no nociception, strong coherence) to 100 (strong nociception, low coherence). The aim of this study is to estimate the sensitivity of the algorithm to nociception (dental dam insertions) and antinociception (bolus doses of anesthetic drugs). WTCRC's sensitivity is compared to mean heart rate (HRmean) and mean non-invasive blood pressure (NIBPmean), which are commonly used clinical signs. Data were collected from 48 children receiving general anesthesia during dental surgery. The times of dental dam insertion and anesthetic bolus events were noted in real-time during surgeries. 42 dental dam insertion and 57 anesthetic bolus events were analyzed. The change in average WTCRC, HRmean, and NIBPmean was calculated between a baseline period before each event and a response period after. A Wilcoxon rank-sum test was used to compare changes. Dental dam insertion changed the WTCRC nociception index by an average of 14 (82 %) [95 % CI from 7.4 to 19], HRmean by 7.3 beats/min (8.1 %) [5.6-9.6], and NIBPmean by 8.3 mmHg (12 %) [4.9-13]. A bolus dose of anesthetics changed the WTCRC by -15 (-50 %) [-21 to -9.3], HRmean by -4.8 beats/min (4.6 %) [-6.6 to -2.9], and NIBPmean by -2.6 mmHg (3.4 %) [-4.7 to -0.50]. A nociception index based on cardiorespiratory coherence is more sensitive to nociception and antinociception than are HRmean or NIBPmean. The WTCRC algorithm shows promise for noninvasively monitoring nociception during general anesthesia.

@inProceedings{
 title = {Recognition of correct finger placement for photoplethysmographic imaging},
 type = {inProceedings},
 year = {2013},
 identifiers = {[object Object]},
 keywords = {Consumer health,Emerging IT for efficient/low-cost healthcare deli,Mobile health,User experience,Wireless/ubiquitous technologies and systems},
 pages = {7480-3},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/24111475},
 month = {7},
 city = {Osaka},
 id = {cc709576-791c-3538-b46e-63e7a3ad1a15},
 created = {2013-01-17T07:41:46.000Z},
 accessed = {2013-10-23},
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 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2013},
 abstract = {In mobile health applications, non-expert users often perform the required medical measurements without supervision. Therefore, it is important that the mobile device guides them through the correct measurement process and automatically detects potential errors that could impact the readings. Camera oximetry provides a non-invasive measurement of heart rate and blood oxygen saturation using the camera of a mobile phone. We describe a novel method to automatically detect the correct finger placement on the camera lens for camera oximetry. Incorrect placement can cause optical shunt and if ignored, lead to low quality oximetry readings. The presented algorithm uses the spectral properties of the pixels to discriminate between correct and incorrect placements. Experimental results demonstrate high mean accuracy (99.06%), sensitivity (98.06%) and specificity (99.30%) with low variability. By sub-sampling pixels, the computational cost of classifying a frame has been reduced by more than three orders of magnitude. The algorithm has been integrated in a newly developed application called OxiCam where it provides real-time user feedback.},
 bibtype = {inProceedings},
 author = {Karlen, Walter and Lim, Joanne and Ansermino, J Mark and Dumont, Guy A and Scheffer, Cornie},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society.}
}

In mobile health applications, non-expert users often perform the required medical measurements without supervision. Therefore, it is important that the mobile device guides them through the correct measurement process and automatically detects potential errors that could impact the readings. Camera oximetry provides a non-invasive measurement of heart rate and blood oxygen saturation using the camera of a mobile phone. We describe a novel method to automatically detect the correct finger placement on the camera lens for camera oximetry. Incorrect placement can cause optical shunt and if ignored, lead to low quality oximetry readings. The presented algorithm uses the spectral properties of the pixels to discriminate between correct and incorrect placements. Experimental results demonstrate high mean accuracy (99.06%), sensitivity (98.06%) and specificity (99.30%) with low variability. By sub-sampling pixels, the computational cost of classifying a frame has been reduced by more than three orders of magnitude. The algorithm has been integrated in a newly developed application called OxiCam where it provides real-time user feedback.

@inProceedings{
 title = {Detection of the optimal region of interest for camera oximetry},
 type = {inProceedings},
 year = {2013},
 identifiers = {[object Object]},
 keywords = {Consumer health,Emerging IT for efficient/low-cost healthcare deli,Mobile health},
 pages = {2263-6},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/24110175},
 month = {7},
 city = {Osaka},
 id = {da143f54-9182-3f49-8c60-d11622398411},
 created = {2013-03-25T11:45:13.000Z},
 accessed = {2013-10-23},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
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 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2013f},
 abstract = {The estimation of heart rate and blood oxygen saturation with an imaging array on a mobile phone (camera oximetry) has great potential for mobile health applications as no additional hardware other than a camera and LED flash enabled phone are required. However, this approach is challenging as the configuration of the camera can negatively influence the estimation quality. Further, the number of photons recorded with the photo detector is largely dependent on the optical path length, resulting in a non-homogeneous image. In this paper we describe a novel method to automatically detect the optimal region of interest (ROI) for the captured image to extract a pulse waveform. We also present a study to select the optimal camera settings, notably the white balance. The experiments show that the incandescent white balance mode is the preferable setting for camera oximetry applications on the tested mobile phone (Samsung Galaxy Ace). Also, the ROI algorithm successfully identifies the frame regions which provide waveforms with the largest amplitudes.},
 bibtype = {inProceedings},
 author = {Karlen, Walter and Ansermino, J Mark and Dumont, Guy A and Scheffer, Cornie},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society.}
}

The estimation of heart rate and blood oxygen saturation with an imaging array on a mobile phone (camera oximetry) has great potential for mobile health applications as no additional hardware other than a camera and LED flash enabled phone are required. However, this approach is challenging as the configuration of the camera can negatively influence the estimation quality. Further, the number of photons recorded with the photo detector is largely dependent on the optical path length, resulting in a non-homogeneous image. In this paper we describe a novel method to automatically detect the optimal region of interest (ROI) for the captured image to extract a pulse waveform. We also present a study to select the optimal camera settings, notably the white balance. The experiments show that the incandescent white balance mode is the preferable setting for camera oximetry applications on the tested mobile phone (Samsung Galaxy Ace). Also, the ROI algorithm successfully identifies the frame regions which provide waveforms with the largest amplitudes.

@inProceedings{
 title = {Pulse rate variability compared with heart rate variability in children with and without sleep disordered breathing},
 type = {inProceedings},
 year = {2013},
 identifiers = {[object Object]},
 pages = {6563-6},
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 citation_key = {Dehkordi2013},
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 author = {Dehkordi, Parastoo and Garde, Ainara and Karlen, Walter and Wensley, David and Ansermino, J Mark and Dumont, Guy A},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society}
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@inProceedings{
 title = {Analysis of Oxygen Saturation in Children with Obstructive Sleep Apnea Using the Phone-Oximeter},
 type = {inProceedings},
 year = {2013},
 identifiers = {[object Object]},
 pages = {2531-4},
 volume = {4},
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 citation_key = {Garde2013},
 bibtype = {inProceedings},
 author = {Garde, Ainara and Karlen, Walter and Dehkordi, Parastoo and Wensley, David and Ansermino, J Mark and Dumont, Guy A},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society}
}

@inProceedings{
 title = {Pulse Rate Variability in Children with Disordered Breathing During Different Sleep Stages},
 type = {inProceedings},
 year = {2013},
 pages = {1015 - 8},
 volume = {40},
 websites = {http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6713552},
 month = {9},
 publisher = {IEEE},
 city = {Zaragoza},
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 citation_key = {Dehkordi2013a},
 bibtype = {inProceedings},
 author = {Dehkordi, Parastoo and Garde, Ainara and Karlen, Walter and Wensley, David and Ansermino, J. Mark and Dumont, Guy A.},
 booktitle = {Computing in Cardiology Conference (CinC)}
}

@article{
 title = {Multiparameter respiratory rate estimation from the photoplethysmogram.},
 type = {article},
 year = {2013},
 identifiers = {[object Object]},
 keywords = {Adolescent,Adult,Aged,Algorithms,Automated,Automated: methods,Child,Computer Systems,Computer-Assisted,Computer-Assisted: methods,Data Interpretation,Diagnosis,Fourier Analysis,Humans,Infant,Middle Aged,Pattern Recognition,Photoplethysmography,Photoplethysmography: methods,Preschool,Reproducibility of Results,Respiratory Rate,Respiratory Rate: physiology,Sensitivity and Specificity,Statistical,Young Adult},
 pages = {1946-53},
 volume = {60},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/23399950},
 month = {7},
 day = {8},
 id = {fc88ac6f-0327-3c7d-91c6-991d0c823b73},
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 read = {true},
 starred = {true},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2013a},
 abstract = {We present a novel method for estimating respiratory rate in real time from the photoplethysmogram (PPG) obtained from pulse oximetry. Three respiratory-induced variations (frequency, intensity, and amplitude) are extracted from the PPG using the Incremental-Merge Segmentation algorithm. Frequency content of each respiratory-induced variation is analyzed using fast Fourier transforms. The proposed Smart Fusion method then combines the results of the three respiratory-induced variations using a transparent mean calculation. It automatically eliminates estimations considered to be unreliable because of detected presence of artifacts in the PPG or disagreement between the different individual respiratory rate estimations. The algorithm has been tested on data obtained from 29 children and 13 adults. Results show that it is important to combine the three respiratory-induced variations for robust estimation of respiratory rate. The Smart Fusion showed trends of improved estimation (mean root mean square error 3.0 breaths/min) compared to the individual estimation methods (5.8, 6.2, and 3.9 breaths/min). The Smart Fusion algorithm is being implemented in a mobile phone pulse oximeter device to facilitate the diagnosis of severe childhood pneumonia in remote areas.},
 bibtype = {article},
 author = {Karlen, Walter and Raman, Srinivas and Ansermino, J Mark and Dumont, Guy A},
 journal = {IEEE transactions on bio-medical engineering},
 number = {7}
}

We present a novel method for estimating respiratory rate in real time from the photoplethysmogram (PPG) obtained from pulse oximetry. Three respiratory-induced variations (frequency, intensity, and amplitude) are extracted from the PPG using the Incremental-Merge Segmentation algorithm. Frequency content of each respiratory-induced variation is analyzed using fast Fourier transforms. The proposed Smart Fusion method then combines the results of the three respiratory-induced variations using a transparent mean calculation. It automatically eliminates estimations considered to be unreliable because of detected presence of artifacts in the PPG or disagreement between the different individual respiratory rate estimations. The algorithm has been tested on data obtained from 29 children and 13 adults. Results show that it is important to combine the three respiratory-induced variations for robust estimation of respiratory rate. The Smart Fusion showed trends of improved estimation (mean root mean square error 3.0 breaths/min) compared to the individual estimation methods (5.8, 6.2, and 3.9 breaths/min). The Smart Fusion algorithm is being implemented in a mobile phone pulse oximeter device to facilitate the diagnosis of severe childhood pneumonia in remote areas.

@inProceedings{
 title = {Empirical mode decomposition for respiratory and heart rate estimation from the photoplethysmogram},
 type = {inProceedings},
 year = {2013},
 pages = {799-802},
 volume = {40},
 websites = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6713498,http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6713498},
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 citation_key = {Garde2013a},
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 author = {Garde, A. and Karlen, W. and Dehkordi, P. and Ansermino, J. M. and Dumont, G. A.},
 booktitle = {Computing in Cardiology (CinC)}
}

@inProceedings{
 title = {Empirical mode decomposition for respiratory and heart rate estimation from the photoplethysmogram},
 type = {inProceedings},
 year = {2013},
 pages = {799-802},
 volume = {40},
 websites = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6713498},
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 booktitle = {Computing in Cardiology (CinC)}
}

@inProceedings{
 title = {The Phone Oximeter for Mobile Spot-Check},
 type = {inProceedings},
 year = {2012},
 identifiers = {[object Object]},
 pages = {S21},
 volume = {115},
 issue = {2 Suppl},
 publisher = {Anesthesia and analgesia},
 city = {West Palm Beach},
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 hidden = {false},
 citation_key = {Dunsmuir2012a},
 bibtype = {inProceedings},
 author = {Dunsmuir, Dustin and Petersen, Chris and Karlen, Walter and Lim, Joanne and Dumont, Guy A. and Ansermino, J. Mark},
 booktitle = {Abstracts of the 2012 Annual Meeting of the Society for Technology in Anesthesia (STA)}
}

@inBook{
 title = {Photoplethysmogram Processing Using An Adaptive Single Frequency Phase Vocoder Algorithm},
 type = {inBook},
 year = {2012},
 pages = {31-42},
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 bibtype = {inBook},
 author = {Karlen, Walter and Petersen, Chris and Gow, Jennifer and Ansermino, J Mark and Dumont, Guy A},
 book = {BIOSTEC 2011, CCIS 273}
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@article{
 title = {Usability testing of a prototype Phone Oximeter with healthcare providers in high- and low-medical resource environments},
 type = {article},
 year = {2012},
 identifiers = {[object Object]},
 pages = {957-67},
 volume = {67},
 websites = {http://doi.wiley.com/10.1111/j.1365-2044.2012.07196.x},
 day = {2},
 id = {79b0a170-90c5-368e-9fb4-463db36f6302},
 created = {2011-05-12T18:45:03.000Z},
 accessed = {2012-08-07},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2015-02-09T15:42:58.000Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Hudson2012},
 abstract = {To increase the use of pulse oximetry by capitalise on the wide availability of mobile phones, we have designed, developed and evaluated a prototype pulse oximeter interfaced to a mobile phone. Usability of this Phone Oximeter was tested as part of a rapid prototyping process. Phase 1 of the study (20 subjects) was performed in Canada. Users performed 23 tasks, while thinking aloud. Time for completion of tasks and analysis of user response to a mobile phone usability questionnaire were used to evaluate usability. Five interface improvements were made to the prototype before evaluation in Phase 2 (15 subjects) in Uganda. The lack of previous pulse oximetry experience and mobile phone use increased median (IQR [range]) time taken to perform tasks from 219 (160–247 [118–274]) s in Phase 1 to 228 (151–501 [111–2661]) s in Phase 2. User feedback was positive and overall usability high (Phase 1 – 82%, Phase 2 – 78%).},
 bibtype = {article},
 author = {Hudson, Jacqueline and Nguku, S. M. and Sleiman, Jules and Karlen, Walter and Dumont, G. A. and Petersen, C. and Warriner, C. B. and Ansermino, J Mark},
 journal = {Anaesthesia},
 number = {9}
}

To increase the use of pulse oximetry by capitalise on the wide availability of mobile phones, we have designed, developed and evaluated a prototype pulse oximeter interfaced to a mobile phone. Usability of this Phone Oximeter was tested as part of a rapid prototyping process. Phase 1 of the study (20 subjects) was performed in Canada. Users performed 23 tasks, while thinking aloud. Time for completion of tasks and analysis of user response to a mobile phone usability questionnaire were used to evaluate usability. Five interface improvements were made to the prototype before evaluation in Phase 2 (15 subjects) in Uganda. The lack of previous pulse oximetry experience and mobile phone use increased median (IQR [range]) time taken to perform tasks from 219 (160–247 [118–274]) s in Phase 1 to 228 (151–501 [111–2661]) s in Phase 2. User feedback was positive and overall usability high (Phase 1 – 82%, Phase 2 – 78%).

@inProceedings{
 title = {Design challenges for camera oximetry on a mobile phone},
 type = {inProceedings},
 year = {2012},
 identifiers = {[object Object]},
 keywords = {Biosensing Techniques,Biosensing Techniques: methods,Cellular Phone,Humans,Oximetry,Oximetry: methods,Pulse},
 pages = {2448-51},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/23366420},
 month = {1},
 id = {82f4ed12-9531-3ebd-85e8-210a41d8b7e3},
 created = {2012-03-28T10:36:03.000Z},
 accessed = {2014-06-18},
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 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2012b},
 abstract = {The use of mobile consumer devices as medical diagnostic tools allows standard medical tests to be performed anywhere. Cameras embedded in consumer devices have previously been used as pulse oximetry sensors. However, technical limitations and implementation challenges have not been described. This manuscript provides a critical analysis of pulse oximeter technology and technical limitations of cameras that can potentially impact implementation of pulse oximetry in mobile phones. Theoretical and practical examples illustrate difficulties and recommendations to overcome these challenges.},
 bibtype = {inProceedings},
 author = {Karlen, Walter and Lim, Joanne and Ansermino, J Mark and Dumont, Guy and Scheffer, Cornie},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society.}
}

The use of mobile consumer devices as medical diagnostic tools allows standard medical tests to be performed anywhere. Cameras embedded in consumer devices have previously been used as pulse oximetry sensors. However, technical limitations and implementation challenges have not been described. This manuscript provides a critical analysis of pulse oximeter technology and technical limitations of cameras that can potentially impact implementation of pulse oximetry in mobile phones. Theoretical and practical examples illustrate difficulties and recommendations to overcome these challenges.

@article{
 title = {Photoplethysmogram signal quality estimation using repeated Gaussian filters and cross-correlation.},
 type = {article},
 year = {2012},
 identifiers = {[object Object]},
 keywords = {oximeter,photoplethysmogram signal quality estimation,photoplethysmography,pulse,repeated gaussian filters,segmentation,signal quality index},
 pages = {1617-29},
 volume = {33},
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 day = {18},
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 abstract = {Pulse oximeters are monitors that noninvasively measure heart rate and blood oxygen saturation (SpO(2)). Unfortunately, pulse oximetry is prone to artifacts which negatively impact the accuracy of the measurement and can cause a significant number of false alarms. We have developed an algorithm to segment pulse oximetry signals into pulses and estimate the signal quality in real time. The algorithm iteratively calculates a signal quality index (SQI) ranging from 0 to 100. In the presence of artifacts and irregular signal morphology, the algorithm outputs a low SQI number. The pulse segmentation algorithm uses the derivative of the signal to find pulse slopes and an adaptive set of repeated Gaussian filters to select the correct slopes. Cross-correlation of consecutive pulse segments is used to estimate signal quality. Experimental results using two different benchmark data sets showed a good pulse detection rate with a sensitivity of 96.21% and a positive predictive value of 99.22%, which was equivalent to the available reference algorithm. The novel SQI algorithm was effective and produced significantly lower SQI values in the presence of artifacts compared to SQI values during clean signals. The SQI algorithm may help to guide untrained pulse oximeter users and also help in the design of advanced algorithms for generating smart alarms.},
 bibtype = {article},
 author = {Karlen, W and Kobayashi, K and Ansermino, J M and Dumont, G A},
 journal = {Physiological measurement},
 number = {10}
}

Pulse oximeters are monitors that noninvasively measure heart rate and blood oxygen saturation (SpO(2)). Unfortunately, pulse oximetry is prone to artifacts which negatively impact the accuracy of the measurement and can cause a significant number of false alarms. We have developed an algorithm to segment pulse oximetry signals into pulses and estimate the signal quality in real time. The algorithm iteratively calculates a signal quality index (SQI) ranging from 0 to 100. In the presence of artifacts and irregular signal morphology, the algorithm outputs a low SQI number. The pulse segmentation algorithm uses the derivative of the signal to find pulse slopes and an adaptive set of repeated Gaussian filters to select the correct slopes. Cross-correlation of consecutive pulse segments is used to estimate signal quality. Experimental results using two different benchmark data sets showed a good pulse detection rate with a sensitivity of 96.21% and a positive predictive value of 99.22%, which was equivalent to the available reference algorithm. The novel SQI algorithm was effective and produced significantly lower SQI values in the presence of artifacts compared to SQI values during clean signals. The SQI algorithm may help to guide untrained pulse oximeter users and also help in the design of advanced algorithms for generating smart alarms.

@inProceedings{
 title = {Real-time cardiorespiratory coherence detects antinociception during general anesthesia.},
 type = {inProceedings},
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 pages = {3813-6},
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 websites = {http://www.ncbi.nlm.nih.gov/pubmed/23366759},
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 citation_key = {Brouse2012b},
 abstract = {Heart rate variability (HRV) may provide anesthesiologists with a noninvasive tool for monitoring nociception during general anesthesia. A novel real-time cardiorespiratory coherence (CRC) algorithm has been developed to analyze the strength of linear coupling between heart rate (HR) and respiration. CRC values range from 0 (low coherence, strong nociception) to 1 (high coherence, no nociception). The algorithm uses specially designed filters to operate in real-time, minimizing computational complexity and time delay. In the standard HRV high frequency band of 0.15 - 0.4 Hz, the real-time delay is only 5.25 - 3.25 s. We have assessed the algorithm's response to 60 anesthetic bolus events (a large dose of anesthetics given over a short time; strongly antinociceptive) recorded in 47 pediatric patients receiving general anesthesia. Real-time CRC responded strongly to bolus events, changing by an average of 30%. For comparison, three traditional measures of HRV (LF/HF ratio, SDNN, and RMSSD) responded on average by only 3.8%, 14%, and 3.9%, respectively. Finally, two traditional clinical measures of nociception (HR and blood pressure) responded on average by only 3.9% and 0.91%, respectively. CRC may thus be used as a real-time nociception monitor during general anesthesia.},
 bibtype = {inProceedings},
 author = {Brouse, Chris J and Karlen, Walter and Dumont, Guy A and Myers, Dorothy and Cooke, Erin and Stinson, Jonathan and Lim, Joanne and Ansermino, J Mark},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society}
}

Heart rate variability (HRV) may provide anesthesiologists with a noninvasive tool for monitoring nociception during general anesthesia. A novel real-time cardiorespiratory coherence (CRC) algorithm has been developed to analyze the strength of linear coupling between heart rate (HR) and respiration. CRC values range from 0 (low coherence, strong nociception) to 1 (high coherence, no nociception). The algorithm uses specially designed filters to operate in real-time, minimizing computational complexity and time delay. In the standard HRV high frequency band of 0.15 - 0.4 Hz, the real-time delay is only 5.25 - 3.25 s. We have assessed the algorithm's response to 60 anesthetic bolus events (a large dose of anesthetics given over a short time; strongly antinociceptive) recorded in 47 pediatric patients receiving general anesthesia. Real-time CRC responded strongly to bolus events, changing by an average of 30%. For comparison, three traditional measures of HRV (LF/HF ratio, SDNN, and RMSSD) responded on average by only 3.8%, 14%, and 3.9%, respectively. Finally, two traditional clinical measures of nociception (HR and blood pressure) responded on average by only 3.9% and 0.91%, respectively. CRC may thus be used as a real-time nociception monitor during general anesthesia.

@inProceedings{
 title = {Measuring Adequacy of Analgesia with Cardiorespiratory Coherence},
 type = {inProceedings},
 year = {2012},
 identifiers = {[object Object]},
 keywords = {Anesthesia,Anesthesiology,Humans,Medical Laboratory Science},
 pages = {S8},
 volume = {115},
 issue = {2 Suppl},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/22826529},
 publisher = {Anesthesia and analgesia},
 city = {West Palm Beach},
 id = {9d0b9eb2-3951-335b-bba9-517459c99c08},
 created = {2012-01-22T08:28:58.000Z},
 accessed = {2013-09-18},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2014-11-28T11:16:46.000Z},
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 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Brouse2012c},
 bibtype = {inProceedings},
 author = {Brouse, Christopher J and Karlen, Walter and Dumont, Guy A. and Myers, Dorothy and Cooke, Erin and Stinson, Jonathan and Lim, Joanne and Ansermino, J. Mark},
 booktitle = {Abstracts of the 2012 Annual Meeting of the Society for Technology in Anesthesia (STA)}
}

@inProceedings{
 title = {A Data Fusion Approach for RR estimation from PPG (STA Engineering Challenge)},
 type = {inProceedings},
 year = {2012},
 volume = {31},
 issue = {3},
 city = {West Palm Beach},
 id = {91b40cd5-a8f2-3ac4-951f-730dedc1c9d7},
 created = {2012-01-22T08:32:29.000Z},
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 read = {true},
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 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Raman2012},
 bibtype = {inProceedings},
 author = {Raman, Srinivas and Brouse, Chris J and Karlen, Walter and Dumont, Guy A and Ansermino, J. Mark},
 booktitle = {Proceedings of the 2012 Society for Technology in Anesthesia Annual Meeting}
}

@inProceedings{
 title = {Adaptive Pulse Segmentation and Artifact Detection in Photoplethysmography for Mobile Applications},
 type = {inProceedings},
 year = {2012},
 identifiers = {[object Object]},
 keywords = {Adaptive filtering,Biomedical signal classification,Signals and systems},
 pages = {3131-4},
 month = {8},
 city = {San Diego},
 id = {75cc4b1e-4557-35f5-940e-22d00aed61a4},
 created = {2012-04-01T20:55:06.000Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2014-11-28T11:16:47.000Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2012},
 abstract = {Abstract—Pulse oximeters non-invasively measure heart rate and oxygen saturation and have great potential for predicting critical illness. The photoplethysmogram (PPG) recorded from pulse oximeters is often corrupted with artifacts that can render the vital signs obtained inaccurate. We present a novel real-time algorithm for segmentation of the PPG into pulses and classification of artifacts. The line segmentation algorithm operates in the time domain and extracts morphological fea- tures of the PPG. These features are characterized as lines which are classified as pulses and artifacts using adaptive thresholds. The algorithm was evaluated using the Complex System Laboratory (CSL) Benchmark data set. A sensitivity of 98.93% and positive predictive value of 96.68% have been obtained, which compares very favorably with the benchmark algorithm. The novel algorithm is currently being implemented into mobile phone pulse oximeters},
 bibtype = {inProceedings},
 author = {Karlen, Walter and Ansermino, J Mark and Dumont, Guy A.},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society}
}

Abstract—Pulse oximeters non-invasively measure heart rate and oxygen saturation and have great potential for predicting critical illness. The photoplethysmogram (PPG) recorded from pulse oximeters is often corrupted with artifacts that can render the vital signs obtained inaccurate. We present a novel real-time algorithm for segmentation of the PPG into pulses and classification of artifacts. The line segmentation algorithm operates in the time domain and extracts morphological fea- tures of the PPG. These features are characterized as lines which are classified as pulses and artifacts using adaptive thresholds. The algorithm was evaluated using the Complex System Laboratory (CSL) Benchmark data set. A sensitivity of 98.93% and positive predictive value of 96.68% have been obtained, which compares very favorably with the benchmark algorithm. The novel algorithm is currently being implemented into mobile phone pulse oximeters

@article{
 title = {Pulse oximeter plethysmograph variation and its relationship to the arterial waveform in mechanically ventilated children.},
 type = {article},
 year = {2012},
 identifiers = {[object Object]},
 keywords = {arterial pulse pressure variation,index,pulse oximeter plethysmograph á,variation á plethysmograph variability,á plethysmograph},
 pages = {145-51},
 volume = {26},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/22407178},
 month = {6},
 day = {10},
 id = {d549f7c9-06b8-3c0b-a39c-6ae69de1b8f4},
 created = {2015-08-30T08:01:45.000Z},
 accessed = {2012-07-24},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2015-08-30T08:02:08.000Z},
 read = {true},
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 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Chandler2012},
 abstract = {The variations induced by mechanical ventilation in the arterial pulse pressure and pulse oximeter plethysmograph waveforms have been shown to correlate closely and be effective in adults as markers of volume responsiveness. The aims of our study were to investigate: (1) the feasibility of recording plethysmograph indices; and (2) the relationship between pulse pressure variation (ΔPP), plethysmograph variation (ΔPOP) and plethysmograph variability index (PVI) in a diverse group of mechanically ventilated children. A prospective, observational study was performed. Mechanically ventilated children less than 11 years of age, with arterial catheters, were enrolled during the course of their clinical care in the operating room or in the pediatric intensive care unit. Real time monitor waveforms and trend data were recorded. ΔPP and ΔPOP were manually calculated and the relationships between ΔPP, ΔPOP and PVI were compared using Bland-Altman analysis and Pearson correlations. Forty-nine children were recruited; four (8%) subjects were excluded due to poor quality of the plethysmograph waveforms. ΔPP and ΔPOP demonstrated a strong correlation (r = 0.8439, P < 0.0001) and close agreement (Bias = 1.44 ± 6.4%). PVI was found to correlate strongly with ΔPP (r = 0.7049, P < 0.0001) and ΔPOP (r = 0.715, P < 0.0001). This study demonstrates the feasibility of obtaining plethysmographic variability indices in children under various physiological stresses. These data show a similarly strong correlation to that described in adults, between the variations induced by mechanical ventilation in arterial pulse pressure and the pulse oximeter plethysmograph.},
 bibtype = {article},
 author = {Chandler, J R and Cooke, E and Petersen, C and Karlen, W and Froese, N and Lim, J and Ansermino, J M},
 journal = {Journal of clinical monitoring and computing},
 number = {3}
}

The variations induced by mechanical ventilation in the arterial pulse pressure and pulse oximeter plethysmograph waveforms have been shown to correlate closely and be effective in adults as markers of volume responsiveness. The aims of our study were to investigate: (1) the feasibility of recording plethysmograph indices; and (2) the relationship between pulse pressure variation (ΔPP), plethysmograph variation (ΔPOP) and plethysmograph variability index (PVI) in a diverse group of mechanically ventilated children. A prospective, observational study was performed. Mechanically ventilated children less than 11 years of age, with arterial catheters, were enrolled during the course of their clinical care in the operating room or in the pediatric intensive care unit. Real time monitor waveforms and trend data were recorded. ΔPP and ΔPOP were manually calculated and the relationships between ΔPP, ΔPOP and PVI were compared using Bland-Altman analysis and Pearson correlations. Forty-nine children were recruited; four (8%) subjects were excluded due to poor quality of the plethysmograph waveforms. ΔPP and ΔPOP demonstrated a strong correlation (r = 0.8439, P < 0.0001) and close agreement (Bias = 1.44 ± 6.4%). PVI was found to correlate strongly with ΔPP (r = 0.7049, P < 0.0001) and ΔPOP (r = 0.715, P < 0.0001). This study demonstrates the feasibility of obtaining plethysmographic variability indices in children under various physiological stresses. These data show a similarly strong correlation to that described in adults, between the variations induced by mechanical ventilation in arterial pulse pressure and the pulse oximeter plethysmograph.

@inProceedings{
 title = {An Adaptive Single Frequency Phase Vocoder For Low-power Heart Rate Detection},
 type = {inProceedings},
 year = {2011},
 keywords = {embedded systems,heart rate estimation,mobile phones,photoplethysmography.,pulse detection},
 pages = {30-35},
 publisher = {INSTICC Press},
 id = {ad0c951d-2080-38ca-8450-62e6bb12bf64},
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 file_attached = {true},
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 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2011},
 abstract = {Mobile phones can be used as a platform for clinical decision making in resource-poor and remote areas. Their limited battery and computational resources, however, demand efficient and low-power algorithms. We present a new algorithm for the fast and economical estimation of heart rate (HR) from the photoplethysmogram (PPG) recorded with a pulse oximeter connected to a mobile phone. The new method estimates the HR frequency by adaptively modeling the PPG wave with a sine function using a modified phase vocoder. The obtained wave is also used as an envelope for the detection of peaks in the PPG signal. HR is computed using the vocoder center frequency and using the peak intervals in a histogram. Experiments on a mobile device show comparable speed performance with other time domain algorithms. Preliminary tests show that the HR computed from the vocoder center frequency is robust to noisy PPG. The instantaneous HR calculated with the vocoder peak detection method was more sensitive to short-term HR variations. These results point to further developments using a combination of both HR estimation methods that will enable the robust implementation of adaptive phase vocoders into mobile phone applications.},
 bibtype = {inProceedings},
 author = {Karlen, Walter and Petersen, Chris and Gow, Jennifer and Ansermino, J. Mark and Dumont, Guy A.},
 booktitle = {BIODEVICES 2011 - Proceedings of the International Conference on Biomedical Electronics and Devices, Rome, Italy, January 26-29, 2011}
}

Mobile phones can be used as a platform for clinical decision making in resource-poor and remote areas. Their limited battery and computational resources, however, demand efficient and low-power algorithms. We present a new algorithm for the fast and economical estimation of heart rate (HR) from the photoplethysmogram (PPG) recorded with a pulse oximeter connected to a mobile phone. The new method estimates the HR frequency by adaptively modeling the PPG wave with a sine function using a modified phase vocoder. The obtained wave is also used as an envelope for the detection of peaks in the PPG signal. HR is computed using the vocoder center frequency and using the peak intervals in a histogram. Experiments on a mobile device show comparable speed performance with other time domain algorithms. Preliminary tests show that the HR computed from the vocoder center frequency is robust to noisy PPG. The instantaneous HR calculated with the vocoder peak detection method was more sensitive to short-term HR variations. These results point to further developments using a combination of both HR estimation methods that will enable the robust implementation of adaptive phase vocoders into mobile phone applications.

@inProceedings{
 title = {SleepPic. Hardware Developments for a Wearable On-line Sleep and Wake Discrimination System},
 type = {inProceedings},
 year = {2011},
 keywords = {ECG,context awareness,embedded intelligence.,hardware development,on-line classification,respiration,sleep and wake discrimination,wearable systems},
 pages = {132-7},
 publisher = {SciTePress},
 editors = {[object Object],[object Object],[object Object],[object Object]},
 id = {f87cf3af-b28d-3c85-b8d4-a36a63df21fe},
 created = {2010-10-08T17:17:59.000Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2014-11-28T11:16:47.000Z},
 tags = {Hardware,SleePic,wearable},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2011a},
 abstract = {The design of wearable systems comes with constraints in computational and power resources. We describe the development of customized hardware for the wearable discrimination of human sleep and wake based on cardio-respiratory signals. The device was designed for efficient and low-power computation of Fast Fourier Transforms and artificial neural networks required for the on-line classification. We discuss methods for reducing computational load and consequently power requirements of the device. The developed wearable SleePic prototype was tested for autonomy and comfort on eight healthy subjects. SleePic showed an energetic autonomy of more than 36 hours. The SleePic device will require further integration for increased comfort and improved user interaction.},
 bibtype = {inProceedings},
 author = {Karlen, Walter and Floreano, Dario},
 booktitle = {Proceedings of BIOSIGNALS 2011 - International Conference on Bio-inspired Systems and Signal Processing, Rome, Italy, January 26-29, 2011}
}

The design of wearable systems comes with constraints in computational and power resources. We describe the development of customized hardware for the wearable discrimination of human sleep and wake based on cardio-respiratory signals. The device was designed for efficient and low-power computation of Fast Fourier Transforms and artificial neural networks required for the on-line classification. We discuss methods for reducing computational load and consequently power requirements of the device. The developed wearable SleePic prototype was tested for autonomy and comfort on eight healthy subjects. SleePic showed an energetic autonomy of more than 36 hours. The SleePic device will require further integration for increased comfort and improved user interaction.

@inProceedings{
 title = {Human-centered Phone Oximeter Interface Design for the Operating Room},
 type = {inProceedings},
 year = {2011},
 identifiers = {[object Object]},
 keywords = {anesthesia,human-centered,interface design,mobile phones,photoplethysmography,pulse oximeter},
 pages = {433-8},
 websites = {http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0003335204330438},
 publisher = {SciTePress - Science and and Technology Publications},
 city = {Rome, Italy},
 editors = {[object Object],[object Object],[object Object],[object Object]},
 id = {197b5ce0-6a90-3e99-b86f-62d3737d2dbf},
 created = {2010-12-09T06:29:30.000Z},
 accessed = {2013-06-05},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2014-11-28T11:16:46.000Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2011f},
 abstract = {Mobile phones offer huge potential as platforms for clinical decision making in resource-poor and remote areas. We present methods for the development of a human-centered interface for anesthesia monitoring that is targeted to remote operating rooms in developing countries. The Phone Oximeter is compatible with major PC and mobile phone operating systems and is optimized for small phone screens. It displays vital physiological parameters in the corresponding clinical colours. Combined with an easily identifiable icon, this guarantees that accessibility is language-independent. To evaluate the acceptance and usability of the initial prototype of the Phone Oximeter, the Think Aloud process while completing a specific Task List, followed by the Mobile Phone Usability Questionnaire (MPUQ) were tested on 20 subjects with varying medical and mobile phone experience. The acceptance rate of 81.9 % from the MPUQ questionnaire clearly demonstrates the usability of the Phone Oximeter. The incorporation of the most relevant errors and complaints into the design of the next iteration of the Phone Oximeter prototype enhanced its capabilities further.},
 bibtype = {inProceedings},
 author = {Karlen, Walter and Dumont, Guy A and Petersen, Chris and Gow, Jennifer and Lim, Joanne and Sleiman, Jules and Ansermino, J Mark},
 booktitle = {Proceedings of the International Conference on Health Informatics}
}

Mobile phones offer huge potential as platforms for clinical decision making in resource-poor and remote areas. We present methods for the development of a human-centered interface for anesthesia monitoring that is targeted to remote operating rooms in developing countries. The Phone Oximeter is compatible with major PC and mobile phone operating systems and is optimized for small phone screens. It displays vital physiological parameters in the corresponding clinical colours. Combined with an easily identifiable icon, this guarantees that accessibility is language-independent. To evaluate the acceptance and usability of the initial prototype of the Phone Oximeter, the Think Aloud process while completing a specific Task List, followed by the Mobile Phone Usability Questionnaire (MPUQ) were tested on 20 subjects with varying medical and mobile phone experience. The acceptance rate of 81.9 % from the MPUQ questionnaire clearly demonstrates the usability of the Phone Oximeter. The incorporation of the most relevant errors and complaints into the design of the next iteration of the Phone Oximeter prototype enhanced its capabilities further.

@inProceedings{
 title = {Location independence in patient monitoring},
 type = {inProceedings},
 year = {2011},
 identifiers = {[object Object]},
 keywords = {Anesthesia,Anesthesiology,Computer Simulation,Humans,Medical,Technology},
 pages = {S37},
 volume = {113},
 issue = {2 Suppl},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/21788323},
 month = {8},
 publisher = {Anesthesia and Analgesia},
 city = {Las Vegas},
 id = {40c34821-695b-3af0-b409-ca02fd28398b},
 created = {2010-12-30T01:54:18.000Z},
 accessed = {2011-12-05},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2014-11-28T11:16:46.000Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2011g},
 abstract = {Introduction Hospital patients require physiological monitoring throughout their stay. Monitoring requirements depend on the hospital unit (e.g. Admission, OR, ICU, ward). Currently, monitoring devices are stationary and are connected by wires to sensors and patient. This is cumbersome for both patient and health care providers, and sensors must be disconnected when the patient is prepared for transfer between units. Further, sensors located in one unit are often incompatible with those in another.We propose a novel concept that simplifies patient monitoring throughout the hospital. Method Approach:We propose a two level wireless network (Fig. 1). A personal area network (PAN) is private to the patient and is responsible for the control of data communication. The PAN host device connects to all required sensors using a wide range of supported protocols (e.g. serial, USB,WiFi and Bluetooth), and is attached to the patient during the entire hospital stay. The PAN host then wirelessly transmits the standardized data to a local area network (LAN) that records patient health information in a database. This information can be retrieved in real time by either stationary monitoring devices or mobile devices of health care providers throughout the hospital network. Prototype: The prototype consists of two pulse oximeters (Nonin, USA) connected via Bluetooth and wired connection, respectively, to a computer with a Linux operating system that acts as the host for the PAN. The LAN consists of a server running a web-based sensor actuator network portal called Sense Tecnic [1]. AWiFi enabled mobile device is used as the monitoring display. Results & Discussion Blood oxygen saturation and heart rate trend signals are recorded and displayed in real time at a 1 Hz update rate. The web-based data portal allows platform independent, real-time monitoring. The PAN allows for easy connection of sensors to the patient and facilitates monitoring during patient movement and transportation. This approach will facilitate the use of elementary sensors without interruption throughout the hospital. Unit specific sensors can be added to the PAN when required. Future work will include geolocation by indoor triangulation using theWiFi network, and size reduction of the PAN host.},
 bibtype = {inProceedings},
 author = {Karlen, Walter and Blackstock, Mike and Ansermino, J Mark},
 booktitle = {Abstracts of the 2011 Annual Meeting of the Society for Technology in Anesthesia (STA). January 12-15, 2011. Las Vegas, Nevada, USA.}
}

Introduction Hospital patients require physiological monitoring throughout their stay. Monitoring requirements depend on the hospital unit (e.g. Admission, OR, ICU, ward). Currently, monitoring devices are stationary and are connected by wires to sensors and patient. This is cumbersome for both patient and health care providers, and sensors must be disconnected when the patient is prepared for transfer between units. Further, sensors located in one unit are often incompatible with those in another.We propose a novel concept that simplifies patient monitoring throughout the hospital. Method Approach:We propose a two level wireless network (Fig. 1). A personal area network (PAN) is private to the patient and is responsible for the control of data communication. The PAN host device connects to all required sensors using a wide range of supported protocols (e.g. serial, USB,WiFi and Bluetooth), and is attached to the patient during the entire hospital stay. The PAN host then wirelessly transmits the standardized data to a local area network (LAN) that records patient health information in a database. This information can be retrieved in real time by either stationary monitoring devices or mobile devices of health care providers throughout the hospital network. Prototype: The prototype consists of two pulse oximeters (Nonin, USA) connected via Bluetooth and wired connection, respectively, to a computer with a Linux operating system that acts as the host for the PAN. The LAN consists of a server running a web-based sensor actuator network portal called Sense Tecnic [1]. AWiFi enabled mobile device is used as the monitoring display. Results & Discussion Blood oxygen saturation and heart rate trend signals are recorded and displayed in real time at a 1 Hz update rate. The web-based data portal allows platform independent, real-time monitoring. The PAN allows for easy connection of sensors to the patient and facilitates monitoring during patient movement and transportation. This approach will facilitate the use of elementary sensors without interruption throughout the hospital. Unit specific sensors can be added to the PAN when required. Future work will include geolocation by indoor triangulation using theWiFi network, and size reduction of the PAN host.

@inProceedings{
 title = {Automated Validation of Capillary Refill Time Measurements Using Photo-Plethysmogram from a Portable Device for Effective Triage in Children},
 type = {inProceedings},
 year = {2011},
 identifiers = {[object Object]},
 pages = {66-71},
 websites = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6103610},
 month = {10},
 publisher = {IEEE},
 id = {7ebe6e87-72bf-3153-b768-317cf9f88044},
 created = {2011-05-12T18:49:41.000Z},
 accessed = {2012-01-22},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2014-12-15T12:20:50.000Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2011i},
 abstract = {Capillary refill time (CRT) is an important tool for the clinical assessment of trauma and dehydration. Indeed, it has been incorporated into advanced life support guidelines as part of the rapid assessment of critically ill patients. However, digitalized CRT techniques are not readily available and the standard assessment based on the visual inspection of CRT lacks standardization and is prone to a high inter-observer variability. We present an algorithm for the automatic validation of the CRT measurement on the finger using photo-plethysmogram recordings on a small portable device. It is based on a set of deterministic rules for the classification of finger pressure and regular plethysmographic pulses. Validation studies using the classification of 93 pediatric recordings from Canada and Uganda showed that the novel algorithm reliably detects invalid CRT measurements (sensitivity 98.4%). This includes patterns such as insufficient pressure, low perfusion signals, and artifacts. Since our device consists of widely available components already in use, the promising results suggest that the algorithm could be readily integrated in operating rooms and intensive care units around the world. This more robust assessment of CRT would produce a more powerful diagnostic tool for clinical triage in critical care settings.},
 bibtype = {inProceedings},
 author = {Karlen, Walter and Pickard, Amelia and Daniels, Jeremy and Kwizera, Arthur and Ibingira, Charles and Dumont, Guy A and Ansermino, J. Mark},
 booktitle = {2011 IEEE Global Humanitarian Technology Conference}
}

Capillary refill time (CRT) is an important tool for the clinical assessment of trauma and dehydration. Indeed, it has been incorporated into advanced life support guidelines as part of the rapid assessment of critically ill patients. However, digitalized CRT techniques are not readily available and the standard assessment based on the visual inspection of CRT lacks standardization and is prone to a high inter-observer variability. We present an algorithm for the automatic validation of the CRT measurement on the finger using photo-plethysmogram recordings on a small portable device. It is based on a set of deterministic rules for the classification of finger pressure and regular plethysmographic pulses. Validation studies using the classification of 93 pediatric recordings from Canada and Uganda showed that the novel algorithm reliably detects invalid CRT measurements (sensitivity 98.4%). This includes patterns such as insufficient pressure, low perfusion signals, and artifacts. Since our device consists of widely available components already in use, the promising results suggest that the algorithm could be readily integrated in operating rooms and intensive care units around the world. This more robust assessment of CRT would produce a more powerful diagnostic tool for clinical triage in critical care settings.

@inProceedings{
 title = {Respiratory rate estimation using respiratory sinus arrhythmia from photoplethysmography.},
 type = {inProceedings},
 year = {2011},
 identifiers = {[object Object]},
 keywords = {anesthesia,heart rate variability,photo-plethysmogram,pulse oximeter,respiratory rate,respiratory sinus arrhythmia},
 pages = {1201-4},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/22254531},
 month = {8},
 city = {Boston},
 id = {b4e1a9b0-0979-3206-8821-7cbf19e89920},
 created = {2011-05-12T18:57:10.000Z},
 accessed = {2012-01-22},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2014-11-28T11:16:47.000Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2011j},
 abstract = {Respiratory rate (RR) is an important measurement for ambulatory care and there is high interest in its detection using unobtrusive mobile devices. For this study, we investigated the estimation of RR from a photoplethysmography (PPG) signal that originated from a pulse oximeter sensor and had a sub-optimal sampling rate. We explored the possibility of estimating RR by extracting respiratory sinus arrhythmia (RSA) from the PPG-derived heart rate variability (HRV) measurement using real-time algorithms. Data from 29 children and 13 adults undergoing general anesthesia were analyzed. We compared the RSA power derived from electrocardiography (ECG) with PPG at the reference RR derived from capnography. The power of the PPG was significantly higher than that of the ECG (182.42 ± 36.75 dB vs. 162.30 ± 43.66 dB). Further, the mean RR error for PPG was lower than ECG. Both PPG and ECG RR estimation techniques were more powerful and reliable in cases of spontaneous ventilation than when pressure controlled ventilation was used. The analysis of cases containing artifacts in the PPG revealed a significant increase in RR error, a trend that was less pronounced for controlled ventilation. These results indicate that the estimation of RR from the sub-optimally sampled PPG signal is possible and more reliable than from the ECG.},
 bibtype = {inProceedings},
 author = {Karlen, Walter and Brouse, Christopher J and Cooke, Erin and Ansermino, J Mark and Dumont, Guy A},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society.}
}

Respiratory rate (RR) is an important measurement for ambulatory care and there is high interest in its detection using unobtrusive mobile devices. For this study, we investigated the estimation of RR from a photoplethysmography (PPG) signal that originated from a pulse oximeter sensor and had a sub-optimal sampling rate. We explored the possibility of estimating RR by extracting respiratory sinus arrhythmia (RSA) from the PPG-derived heart rate variability (HRV) measurement using real-time algorithms. Data from 29 children and 13 adults undergoing general anesthesia were analyzed. We compared the RSA power derived from electrocardiography (ECG) with PPG at the reference RR derived from capnography. The power of the PPG was significantly higher than that of the ECG (182.42 ± 36.75 dB vs. 162.30 ± 43.66 dB). Further, the mean RR error for PPG was lower than ECG. Both PPG and ECG RR estimation techniques were more powerful and reliable in cases of spontaneous ventilation than when pressure controlled ventilation was used. The analysis of cases containing artifacts in the PPG revealed a significant increase in RR error, a trend that was less pronounced for controlled ventilation. These results indicate that the estimation of RR from the sub-optimally sampled PPG signal is possible and more reliable than from the ECG.

@inProceedings{
 title = {Wavelet transform cardiorespiratory coherence detects patient movement during general anesthesia.},
 type = {inProceedings},
 year = {2011},
 identifiers = {[object Object]},
 pages = {6114-7},
 volume = {2011},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/22255734},
 month = {8},
 id = {c99682d7-ede3-304a-8378-2d00ce456813},
 created = {2011-05-13T00:32:24.000Z},
 accessed = {2012-01-22},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2014-11-28T11:16:47.000Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Brouse2011},
 abstract = {Heart rate variability (HRV) may provide anesthesiologists with a noninvasive tool for monitoring nociception during general anesthesia. A novel wavelet transform cardiores-piratory coherence (WTCRC) algorithm has been developed to calculate estimates of the linear coupling between heart rate and respiration. WTCRC values range from 1 (high coherence, no nociception) to 0 (low coherence, strong nociception). We have assessed the algorithm's ability to detect movement events (indicative of patient response to nociception) in 39 pediatric patients receiving general anesthesia. Sixty movement events were recorded during the 39 surgical procedures. Minimum and average WTCRC were calculated in a 30 second window surrounding each movement event. We used a 95% significance level as the threshold for detecting nociception during patient movement. The 95% significance level was calculated relative to a red noise background, using Monte Carlo simulations. It was calculated to be 0.7. Values below this threshold were treated as successful detection. The algorithm was found to detect movement with sensitivity ranging from 95% (minimum WTCRC) to 65% (average WTCRC). The WTCRC algorithm thus shows promise for noninvasively monitoring nociception during general anesthesia, using only heart rate and respiration.},
 bibtype = {inProceedings},
 author = {Brouse, Christopher J and Karlen, Walter and Myers, Dorothy and Cooke, Erin and Stinson, Jonathan and Lim, Joanne and Dumont, Guy A and Ansermino, J Mark},
 booktitle = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference}
}

Heart rate variability (HRV) may provide anesthesiologists with a noninvasive tool for monitoring nociception during general anesthesia. A novel wavelet transform cardiores-piratory coherence (WTCRC) algorithm has been developed to calculate estimates of the linear coupling between heart rate and respiration. WTCRC values range from 1 (high coherence, no nociception) to 0 (low coherence, strong nociception). We have assessed the algorithm's ability to detect movement events (indicative of patient response to nociception) in 39 pediatric patients receiving general anesthesia. Sixty movement events were recorded during the 39 surgical procedures. Minimum and average WTCRC were calculated in a 30 second window surrounding each movement event. We used a 95% significance level as the threshold for detecting nociception during patient movement. The 95% significance level was calculated relative to a red noise background, using Monte Carlo simulations. It was calculated to be 0.7. Values below this threshold were treated as successful detection. The algorithm was found to detect movement with sensitivity ranging from 95% (minimum WTCRC) to 65% (average WTCRC). The WTCRC algorithm thus shows promise for noninvasively monitoring nociception during general anesthesia, using only heart rate and respiration.

@article{
 title = {Medical intelligence article: capillary refill time: is it still a useful clinical sign?},
 type = {article},
 year = {2011},
 identifiers = {[object Object]},
 pages = {120-3},
 volume = {113},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/21519051},
 month = {7},
 publisher = {IARS},
 id = {6c7944e3-ad95-323a-ab46-0991bcd4d413},
 created = {2011-09-29T23:34:53.000Z},
 accessed = {2011-07-01},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2014-11-28T11:16:46.000Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Pickard2011a},
 abstract = {Capillary refill time (CRT) is widely used by health care workers as part of the rapid, structured cardiopulmonary assessment of critically ill patients. Measurement involves the visual inspection of blood returning to distal capillaries after they have been emptied by pressure. It is hypothesized that CRT is a simple measure of alterations in peripheral perfusion. Evidence for the use of CRT in anesthesia is lacking and further research is required, but understanding may be gained from evidence in other fields. In this report, we examine this evidence and factors affecting CRT measurement. Novel approaches to the assessment of CRT are under investigation. In the future, CRT measurement may be achieved using new technologies such as digital videography or modified oxygen saturation probes; these new methods would remove the limitations associated with clinical CRT measurement and may even be able to provide an automated CRT measurement.},
 bibtype = {article},
 author = {Pickard, Amelia and Karlen, Walter and Ansermino, J Mark},
 journal = {Anesthesia and analgesia},
 number = {1}
}

Capillary refill time (CRT) is widely used by health care workers as part of the rapid, structured cardiopulmonary assessment of critically ill patients. Measurement involves the visual inspection of blood returning to distal capillaries after they have been emptied by pressure. It is hypothesized that CRT is a simple measure of alterations in peripheral perfusion. Evidence for the use of CRT in anesthesia is lacking and further research is required, but understanding may be gained from evidence in other fields. In this report, we examine this evidence and factors affecting CRT measurement. Novel approaches to the assessment of CRT are under investigation. In the future, CRT measurement may be achieved using new technologies such as digital videography or modified oxygen saturation probes; these new methods would remove the limitations associated with clinical CRT measurement and may even be able to provide an automated CRT measurement.

@inProceedings{
 title = {Capillary Refill Time Assessment Using a Mobile Phone Application (iRefill)},
 type = {inProceedings},
 year = {2010},
 keywords = {automatic,capillary refill time,crt,mobile phone,pulse oximeter},
 pages = {A575},
 websites = {http://www.asaabstracts.com/strands/asaabstracts/printAbstract.htm;jsessionid=2971A813366416AB14723F09DA1C5AD0?year=2010&index=8&absnum=695&type=archive},
 publisher = {American Society of Anesthesiologists},
 city = {San Diego},
 id = {ed840441-cc31-364a-8112-2635d3686c13},
 created = {2010-10-21T22:19:47.000Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2014-11-28T11:16:46.000Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2010a},
 abstract = {Identifying capillary refill time (CRT) is an integral part of the clinical assessment of circulatory status and identification of dehydration in children. However, visual inspection of the finger to assess CRT has low inter-observer reliability, largely due to human limitations in estimating short time intervals. To improve precision, we have developed a mobile phone software application (iRefill) that automatically assesses CRT using a photo-plethysmogram (PPG) sensor. Commonly used to measure blood oxygen saturation and heart rate, this sensor can be adapted to replace the human eye to objectively measure CRT.},
 bibtype = {inProceedings},
 author = {Karlen, Walter and Petersen, C and Pickard, A and Dumont, Guy A and Ansermino, J. Mark},
 booktitle = {Proceedings of the 2010 Annual Meeting of the American Society Anesthesiologists}
}

Identifying capillary refill time (CRT) is an integral part of the clinical assessment of circulatory status and identification of dehydration in children. However, visual inspection of the finger to assess CRT has low inter-observer reliability, largely due to human limitations in estimating short time intervals. To improve precision, we have developed a mobile phone software application (iRefill) that automatically assesses CRT using a photo-plethysmogram (PPG) sensor. Commonly used to measure blood oxygen saturation and heart rate, this sensor can be adapted to replace the human eye to objectively measure CRT.

@inProceedings{
 title = {Enhancing pilot performance with a SymBodic system.},
 type = {inProceedings},
 year = {2010},
 identifiers = {[object Object]},
 keywords = {aerospace,fatigue management,haptic feedback,human performance,sleep / wake classification,symbodic},
 pages = {6599-602},
 volume = {1},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/21096516},
 month = {1},
 publisher = {IEEE Engineering in Medicine and Biology Society},
 city = {Buenos Aires},
 id = {eaba85fc-a736-3894-95fa-c5a93e32a69b},
 created = {2010-03-03T01:46:45.000Z},
 accessed = {2010-12-30},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2014-11-28T11:16:47.000Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2010},
 abstract = {Increased fatigue of pilots during long flights can place both humans and machine at high risk. In this paper, we describe our research on a SymBodic (SYMbiotic BODies) system designed to minimize pilot fatigue in a simulated 48 hour mission. The system detected the pilot's sleep breaks and used this information to plan future sleep breaks. When fatigue could not be prevented, the SymBodic system assisted the pilot by providing relevant flight information through a vibro-tactile vest. Experiments showed that it was difficult for the pilot to adapt to the suggested sleep schedule within the duration of the experiment, and fatigue was not avoided. However, during periods of severe sleep deprivation, the SymBodic system significantly improved piloting performance.},
 bibtype = {inProceedings},
 author = {Karlen, Walter and Cardin, Sylvain and Thalmann, Daniel and Floreano, Dario},
 booktitle = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference}
}

Increased fatigue of pilots during long flights can place both humans and machine at high risk. In this paper, we describe our research on a SymBodic (SYMbiotic BODies) system designed to minimize pilot fatigue in a simulated 48 hour mission. The system detected the pilot's sleep breaks and used this information to plan future sleep breaks. When fatigue could not be prevented, the SymBodic system assisted the pilot by providing relevant flight information through a vibro-tactile vest. Experiments showed that it was difficult for the pilot to adapt to the suggested sleep schedule within the duration of the experiment, and fatigue was not avoided. However, during periods of severe sleep deprivation, the SymBodic system significantly improved piloting performance.

@article{
 title = {Adaptive Sleep-Wake Discrimination for Wearable Devices},
 type = {article},
 year = {2010},
 identifiers = {[object Object]},
 pages = {920-6},
 volume = {58},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/21172750},
 month = {12},
 id = {198e5e95-070c-39a5-ab1c-63c9a50ba9da},
 created = {2010-12-03T00:46:03.000Z},
 accessed = {2010-12-30},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2015-01-25T13:49:55.000Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2010d},
 abstract = {Sleep/wake classification systems that rely on physiological signals suffer from inter-subject differences that make accurate classification with a single, subject-independent model difficult. To overcome the limitations of inter-subject variability we suggest a novel on-line adaptation technique that updates the sleep/wake classifier in real-time. The objective of the present study was to evaluate the performance of a newly developed adaptive classification algorithm that was embedded on a wearable sleep/wake classification system called SleePic. The algorithm processed electrocardiogram and respiratory effort signals for the classification task and applied behavioral measurements (obtained from accelerometer and press-button data) for the automatic adaptation task. When trained as a subjectindependent classifier algorithm, the SleePic device was only able to correctly classify 74.94% 6.76 of the human rated sleep/wake data. By using the suggested automatic adaptation method the mean classification accuracy could be significantly improved to 92.98% 3.19. A subject-independent classifier based on activity data only showed a comparable accuracy of 90.44% 3.57. We demonstrated that subject-independent models used for online sleep and wake classification can successfully be adapted to previously unseen subjects without the intervention of human experts or off-line calibration.},
 bibtype = {article},
 author = {Karlen, Walter and Floreano, D},
 journal = {IEEE transactions on bio-medical engineering},
 number = {4}
}

Sleep/wake classification systems that rely on physiological signals suffer from inter-subject differences that make accurate classification with a single, subject-independent model difficult. To overcome the limitations of inter-subject variability we suggest a novel on-line adaptation technique that updates the sleep/wake classifier in real-time. The objective of the present study was to evaluate the performance of a newly developed adaptive classification algorithm that was embedded on a wearable sleep/wake classification system called SleePic. The algorithm processed electrocardiogram and respiratory effort signals for the classification task and applied behavioral measurements (obtained from accelerometer and press-button data) for the automatic adaptation task. When trained as a subjectindependent classifier algorithm, the SleePic device was only able to correctly classify 74.94% 6.76 of the human rated sleep/wake data. By using the suggested automatic adaptation method the mean classification accuracy could be significantly improved to 92.98% 3.19. A subject-independent classifier based on activity data only showed a comparable accuracy of 90.44% 3.57. We demonstrated that subject-independent models used for online sleep and wake classification can successfully be adapted to previously unseen subjects without the intervention of human experts or off-line calibration.

@inProceedings{
 title = {CapnoBase: Signal database and tools to collect, share and annotate respiratory signals},
 type = {inProceedings},
 year = {2010},
 keywords = {anesthesia,capnogram,database,flow,physiological signals,pressure,recording,respiration},
 pages = {25},
 websites = {www.capnobase.org,http://capnobase.org/literature/},
 city = {West Palm Beach},
 id = {aedbe729-f18d-3d5f-89e3-c4d16c97287d},
 created = {2009-12-09T18:16:12.000Z},
 accessed = {2010-06-28},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2015-06-30T13:28:19.000Z},
 tags = {Capnogram,capnobase,database,flow,pressure,respiratory},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2010},
 abstract = {The development of reliable and robust algorithms for the processing of biomedical signals in the operating room requires a series of high resolution signals recorded under different and known conditions. For algorithm tuning and validation, large datasets containing annotated clinical scenarios are required. These scenarios can be difficult to obtain, especially in the case of rare respiratory events recorded during anesthesia (e.g. rising end-tidal carbon dioxide (EtCO2) associated with malignant hyperthermia or anaphylaxis). The collection and annotation of data is very time consuming. In addition the comparative performance of an algorithm can only be assessed using a benchmark dataset. There is currently no public benchmarking dataset for respiratory signal analysis available. CapnoBase is a collaborative research project designed to provide easy to use research tools and a database of annotated respiratory signals including a benchmark dataset.},
 bibtype = {inProceedings},
 author = {Karlen, Walter and Turner, M. and Cooke, E. and Dumont, Guy A and Ansermino, J. M.},
 booktitle = {Annual Meeting of the Society for Technology in Anesthesia (STA)}
}

The development of reliable and robust algorithms for the processing of biomedical signals in the operating room requires a series of high resolution signals recorded under different and known conditions. For algorithm tuning and validation, large datasets containing annotated clinical scenarios are required. These scenarios can be difficult to obtain, especially in the case of rare respiratory events recorded during anesthesia (e.g. rising end-tidal carbon dioxide (EtCO2) associated with malignant hyperthermia or anaphylaxis). The collection and annotation of data is very time consuming. In addition the comparative performance of an algorithm can only be assessed using a benchmark dataset. There is currently no public benchmarking dataset for respiratory signal analysis available. CapnoBase is a collaborative research project designed to provide easy to use research tools and a database of annotated respiratory signals including a benchmark dataset.

@article{
 title = {Evolutionary Selection of Features for Neural Sleep/Wake Discrimination},
 type = {article},
 year = {2009},
 identifiers = {[object Object]},
 pages = {1-10},
 volume = {2009},
 websites = {http://www.hindawi.com/journals/jaea/2009/179680.html},
 id = {a9e3b488-57ff-306f-8c76-036850715a17},
 created = {2009-05-29T19:40:48.000Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2014-11-28T11:16:47.000Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Duerr2009a},
 abstract = {In biomedical signal analysis, artificial neural networks are often used for pattern classification because of their capability for nonlinear class separation and the possibility to efficiently implement them on amicrocontroller. Typically, the network topology is designed by hand, and a gradient-based search algorithm is used to find a set of suitable parameters for the given classification task. In many cases, however, the choice of the network architecture is a critical and difficult task. For example, hand-designed networks often requiremore computational resources than necessary because they rely on input features that provide no information or are redundant. In the case of mobile applications,where computational resources and energy are limited, this is especially detrimental. Neuroevolutionarymethods which allow for the automatic synthesis of network topology and parameters offer a solution to these problems. In this paper, we use analog genetic encoding (AGE) for the evolutionary synthesis of a neural classifier for a mobile sleep/wake discrimination system. The comparison with a hand-designed classifier trained with back propagation shows that the evolved neural classifiers display similar performance to the hand-designed networks, but using a greatly reduced set of inputs, thus reducing computation time and improving the energy efficiency of themobile system.},
 bibtype = {article},
 author = {Duerr, P and Karlen, Walter and Guignard, J and Mattiussi, C},
 journal = {Journal of Artificial Evolution and Applications}
}

In biomedical signal analysis, artificial neural networks are often used for pattern classification because of their capability for nonlinear class separation and the possibility to efficiently implement them on amicrocontroller. Typically, the network topology is designed by hand, and a gradient-based search algorithm is used to find a set of suitable parameters for the given classification task. In many cases, however, the choice of the network architecture is a critical and difficult task. For example, hand-designed networks often requiremore computational resources than necessary because they rely on input features that provide no information or are redundant. In the case of mobile applications,where computational resources and energy are limited, this is especially detrimental. Neuroevolutionarymethods which allow for the automatic synthesis of network topology and parameters offer a solution to these problems. In this paper, we use analog genetic encoding (AGE) for the evolutionary synthesis of a neural classifier for a mobile sleep/wake discrimination system. The comparison with a hand-designed classifier trained with back propagation shows that the evolved neural classifiers display similar performance to the hand-designed networks, but using a greatly reduced set of inputs, thus reducing computation time and improving the energy efficiency of themobile system.

@phdthesis{
 title = {Adaptive Wake and Sleep Detection for Wearable Systems},
 type = {phdthesis},
 year = {2009},
 keywords = {adaptation,classification,context-awareness,portable,sleep,wearable},
 volume = {4391},
 websites = {http://library.epfl.ch/en/theses/?nr=4391},
 publisher = {Ecole Polytechnique Federale de Lausanne (EPFL)},
 city = {Lausanne},
 institution = {Ecole Polytechnique Federale de Lausanne (EPFL)},
 department = {STI School of Engineering},
 id = {c5c914c6-be35-3048-a28d-59475743bd22},
 created = {2009-08-25T20:29:26.000Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2014-11-28T11:16:47.000Z},
 tags = {thesis},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2009a},
 source_type = {PH.D. Thesis},
 user_context = {Ph.D Thesis},
 patent_application_number = {4391},
 abstract = {Sleep problems and disorders have a serious impact on human health and wellbeing. The rising costs for treating sleep-related chronic diseases in industrialized countries demands efficient prevention. Low-cost, wearable sleep / wake detection systems which give feedback on the wearer's "sleep performance" are a promising approach to reduce the risk of developing serious sleep disorders and fatigue. Not all bio-medical signals that are useful for sleep / wake discrimination can be easily recorded with wearable systems. Sensors often need to be placed in an obtrusive location on the body or cannot be efficiently embedded into a wearable frame. Furthermore, wearable systems have limited computational and energetic resources, which restrict the choice of sensors and algorithms for online processing and classification. Since wearable systems are used outside the laboratory, the recorded signals tend to be corrupted with additional noise that influences the precision of classification algorithms. In this thesis we present the research on a wearable sleep / wake classifier system that relies on cardiorespiratory (ECG and respiratory effort) and activity recordings and that works autonomously with minimal user interaction. This research included the selection of optimal signals and sensors, the development of a custom-tailored hardware demonstrator with embedded classification algorithms, and the realization of experiments in real-world environments for the customization and validation of the system. The processing and classification of the signals were based on Fourier transformations and artificial neural networks that are efficiently implementable into digital signal controllers. Literature analysis and empiric measurements revealed that cardiorespiratory signals are more promising for a wearable sleep / wake classification than clinically used signals such as brain potentials. The experiments conducted during this thesis showed that inter-subject differences within the recorded physiological signals make it difficult to design a sleep / wake classification model that can generalize to a group of subjects. This problem was addressed in two ways: First by adding features from another signal to the classifier, that is, measuring the behavioral quiescence during sleep using accelerometers. Conducted research on different feature extraction methods from accelerometer data showed that this data generalizes well for distinct subjects in the study group. In addition, research on user-adaptation methods was conducted. Behavioral sleep and wake measures, notably the measurement of reactivity and activity, were developed to build up a priori knowledge that was used to adapt the classification algorithm automatically to new situations. This thesis demonstrates the design and development of a low-cost, wearable hardware and embedded software for on-line sleep / wake discrimination. The proposed automatic user-adaptive classifier is advantageous compared to previously suggested classification methods that generalize over multiple subjects, because it can take changes in the wearer's physiology and sleep / wake behavior into account without adjustment from a human expert. The results of this thesis contribute to the development of smart, wearable, bio-physiological monitoring systems which require a high degree of autonomy and have only low computational resources available. We believe that the proposed sleep / wake classification system is a first promising step toward a context-aware system for sleep management, sleep disorder prevention, and reduction of fatigue.},
 bibtype = {phdthesis},
 author = {Karlen, Walter}
}

Sleep problems and disorders have a serious impact on human health and wellbeing. The rising costs for treating sleep-related chronic diseases in industrialized countries demands efficient prevention. Low-cost, wearable sleep / wake detection systems which give feedback on the wearer's "sleep performance" are a promising approach to reduce the risk of developing serious sleep disorders and fatigue. Not all bio-medical signals that are useful for sleep / wake discrimination can be easily recorded with wearable systems. Sensors often need to be placed in an obtrusive location on the body or cannot be efficiently embedded into a wearable frame. Furthermore, wearable systems have limited computational and energetic resources, which restrict the choice of sensors and algorithms for online processing and classification. Since wearable systems are used outside the laboratory, the recorded signals tend to be corrupted with additional noise that influences the precision of classification algorithms. In this thesis we present the research on a wearable sleep / wake classifier system that relies on cardiorespiratory (ECG and respiratory effort) and activity recordings and that works autonomously with minimal user interaction. This research included the selection of optimal signals and sensors, the development of a custom-tailored hardware demonstrator with embedded classification algorithms, and the realization of experiments in real-world environments for the customization and validation of the system. The processing and classification of the signals were based on Fourier transformations and artificial neural networks that are efficiently implementable into digital signal controllers. Literature analysis and empiric measurements revealed that cardiorespiratory signals are more promising for a wearable sleep / wake classification than clinically used signals such as brain potentials. The experiments conducted during this thesis showed that inter-subject differences within the recorded physiological signals make it difficult to design a sleep / wake classification model that can generalize to a group of subjects. This problem was addressed in two ways: First by adding features from another signal to the classifier, that is, measuring the behavioral quiescence during sleep using accelerometers. Conducted research on different feature extraction methods from accelerometer data showed that this data generalizes well for distinct subjects in the study group. In addition, research on user-adaptation methods was conducted. Behavioral sleep and wake measures, notably the measurement of reactivity and activity, were developed to build up a priori knowledge that was used to adapt the classification algorithm automatically to new situations. This thesis demonstrates the design and development of a low-cost, wearable hardware and embedded software for on-line sleep / wake discrimination. The proposed automatic user-adaptive classifier is advantageous compared to previously suggested classification methods that generalize over multiple subjects, because it can take changes in the wearer's physiology and sleep / wake behavior into account without adjustment from a human expert. The results of this thesis contribute to the development of smart, wearable, bio-physiological monitoring systems which require a high degree of autonomy and have only low computational resources available. We believe that the proposed sleep / wake classification system is a first promising step toward a context-aware system for sleep management, sleep disorder prevention, and reduction of fatigue.

@article{
 title = {Sleep and Wake Classification With ECG and Respiratory Effort Signals},
 type = {article},
 year = {2009},
 identifiers = {[object Object]},
 keywords = {Biomedical signal analysis,electrocardiography,neural classifier,respiratory effort,sleep and wake classification,wearable computing},
 pages = {71-8},
 volume = {3},
 id = {f6be7f1e-5996-3ac0-a2cb-ef342110226c},
 created = {2009-07-30T21:38:41.000Z},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2015-05-07T10:14:44.000Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2009},
 source_type = {article},
 abstract = {We describe a method for the online classification of sleep/wake states based on cardiorespiratory signals produced by wearable sensors. The method was conceived in view of its applicability to a wearable sleepiness monitoring device. The method uses a fast Fourier transform as the main feature extraction tool and a feedforward artificial neural network as a classifier. We show that when the method is applied to data collected from a single young male adult, the system can correctly classify, on average, 95.4% of unseen data from the same user. When the method is applied to classify data from multiple users with the same age and gender, its accuracy is reduced to 85.3%. However, receiver operating characteristic analysis shows that compared to actigraphy, the proposed method produces a more balanced correct classification of sleep and wake periods. Additionally, by adjusting the classification threshold of the neural classifier, 86.7% of correct classification is obtained.},
 bibtype = {article},
 author = {Karlen, Walter and Mattiussi, Claudio and Floreano, Dario},
 journal = {IEEE Transactions on Biomedical Circuits and Systems},
 number = {2}
}

We describe a method for the online classification of sleep/wake states based on cardiorespiratory signals produced by wearable sensors. The method was conceived in view of its applicability to a wearable sleepiness monitoring device. The method uses a fast Fourier transform as the main feature extraction tool and a feedforward artificial neural network as a classifier. We show that when the method is applied to data collected from a single young male adult, the system can correctly classify, on average, 95.4% of unseen data from the same user. When the method is applied to classify data from multiple users with the same age and gender, its accuracy is reduced to 85.3%. However, receiver operating characteristic analysis shows that compared to actigraphy, the proposed method produces a more balanced correct classification of sleep and wake periods. Additionally, by adjusting the classification threshold of the neural classifier, 86.7% of correct classification is obtained.

@inProceedings{
 title = {Improving actigraph sleep/wake classification with cardio-respiratory signals.},
 type = {inProceedings},
 year = {2008},
 identifiers = {[object Object]},
 keywords = {Algorithms,Artificial Intelligence,Automated,Automated: methods,Computer-Assisted,Electrocardiography,Electrocardiography: methods,Equipment Design,Equipment Failure Analysis,Humans,Motor Activity,Motor Activity: physiology,Pattern Recognition,Polysomnography,Polysomnography: methods,Reproducibility of Results,Sensitivity and Specificity,Signal Processing,Spirometry,Spirometry: methods,Wakefulness,Wakefulness: physiology},
 pages = {5262-5},
 websites = {http://www.embc2008.com/},
 month = {1},
 publisher = {IEEE Engineering in Medicine and Biology Society},
 city = {Vancouver},
 id = {2c7e655b-abcb-3c61-8e20-c59c8c08b239},
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 citation_key = {Karlen2008},
 source_type = {inproceedings},
 abstract = {Actigraphy for long-term sleep/wake monitoring fails to correctly classify situations where the subject displays low activity, but is awake. In this paper we propose a new algorithm which uses both accelerometer and cardio-respiratory signals to overcome this restriction. Acceleration, electrocardiogram and respiratory effort were measured with an integrated wearable recording system worn on the chest by three healthy male subjects during normal daily activities. For signal processing a Fast Fourier Transformation and as classifier a feed-forward Artificial Neural Network was used. The best classifier achieved an accuracy of 96.14%, a sensitivity of 94.65% and a specificity of 98.19%. The algorithm is suitable for integration into a wearable device for long-term home monitoring.},
 bibtype = {inProceedings},
 author = {Karlen, Walter and Mattiussi, Claudio and Floreano, Dario},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society.}
}

Actigraphy for long-term sleep/wake monitoring fails to correctly classify situations where the subject displays low activity, but is awake. In this paper we propose a new algorithm which uses both accelerometer and cardio-respiratory signals to overcome this restriction. Acceleration, electrocardiogram and respiratory effort were measured with an integrated wearable recording system worn on the chest by three healthy male subjects during normal daily activities. For signal processing a Fast Fourier Transformation and as classifier a feed-forward Artificial Neural Network was used. The best classifier achieved an accuracy of 96.14%, a sensitivity of 94.65% and a specificity of 98.19%. The algorithm is suitable for integration into a wearable device for long-term home monitoring.

@inProceedings{
 title = {Adaptive Sleep/Wake Classification Based on Cardiorespiratory Signals for Wearable Devices},
 type = {inProceedings},
 year = {2007},
 identifiers = {[object Object]},
 keywords = {EMG,EOG,actigraphy,adaptive feed-forward network,adaptive sleep-wake classification,artificial neural network,biomedical electronics,biomedical equipment,biomedical signal analysis,cardiorespiratory signal,classification,electro-oculography,electrocardiography,electromyography,fast Fourier transform,fast Fourier transforms,feature extraction,feedforward neural nets,medical signal processing,microcontrollers,neural classifier,neurophysiology,pneumodynamics,portable microcontroller device,respiratory effort,signal classification,signal classifier,sleep and wake,sleepECG,wearable computing,wearable device},
 pages = {203-6},
 websites = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4463344},
 month = {11},
 publisher = {IEEE},
 id = {9a9b4f27-e94d-3338-a96a-49e8d5b646c9},
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 citation_key = {Karlen2007},
 source_type = {article},
 abstract = {In this paper we describe a method to classify online sleep/wake states of humans based on cardiorespiratory signals for wearable applications. The method is designed to be embedded in a portable microcontroller device and to cope with the resulting tight power restrictions. The method uses a Fast Fourier Transform as the main feature extraction method and an adaptive feed-forward Artificial Neural Network as a classifier. Results show that when the network is trained on a single user, it can correctly classify on average 95.4% of unseen data from the same user. The accuracy of the method in multi-user conditions is lower (89.4%). This is still comparable to actigraphy methods, but our method classifies wake periods considerably better.},
 bibtype = {inProceedings},
 author = {Karlen, Walter and Mattiussi, Claudio and Floreano, Dario},
 booktitle = {2007 IEEE Biomedical Circuits and Systems Conference}
}

In this paper we describe a method to classify online sleep/wake states of humans based on cardiorespiratory signals for wearable applications. The method is designed to be embedded in a portable microcontroller device and to cope with the resulting tight power restrictions. The method uses a Fast Fourier Transform as the main feature extraction method and an adaptive feed-forward Artificial Neural Network as a classifier. Results show that when the network is trained on a single user, it can correctly classify on average 95.4% of unseen data from the same user. The accuracy of the method in multi-user conditions is lower (89.4%). This is still comparable to actigraphy methods, but our method classifies wake periods considerably better.

@article{
 title = {Robot-animal interaction: Perception and behavior of insbot},
 type = {article},
 year = {2006},
 pages = {093-098},
 volume = {3},
 websites = {http://www.intechweb.org/volume.php?issn=1729-8806&v=3&n=2},
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 citation_key = {Asadpour2006},
 abstract = {This paper describes hardware and behavior implementation of a miniature robot in size of a match box that simulates the behavior of cockroaches in order to establish a social interaction with them. The robot is equipped with two micro-processors dedicated to hardware processing and behavior generation. The robot can discriminate cockroaches, other robots, environment boundaries and shelters. It has also three means of communication to monitor, log, supervise the biological experiment, and detect the other robots in short range. The behavioral model of the robot is a mixture of fusion in low-level and arbitration in high-level. In arbitration level a stochastic state machine selects the proper subtask. Then in fusion level, that subtask is decomposed to a hierarchy of sub-tasks. Each sub-task generates a potential field. The resultant force is then mapped to an action.},
 bibtype = {article},
 author = {Asadpour, M and Tâche, F and Caprari, G and Karlen, Walter and Siegwart, R},
 journal = {International Journal of Advanced Robotic Systems},
 number = {2}
}

This paper describes hardware and behavior implementation of a miniature robot in size of a match box that simulates the behavior of cockroaches in order to establish a social interaction with them. The robot is equipped with two micro-processors dedicated to hardware processing and behavior generation. The robot can discriminate cockroaches, other robots, environment boundaries and shelters. It has also three means of communication to monitor, log, supervise the biological experiment, and detect the other robots in short range. The behavioral model of the robot is a mixture of fusion in low-level and arbitration in high-level. In arbitration level a stochastic state machine selects the proper subtask. Then in fusion level, that subtask is decomposed to a hierarchy of sub-tasks. Each sub-task generates a potential field. The resultant force is then mapped to an action.

@inProceedings{
 title = {Perception and behavior of InsBot : Robot-Animal interaction issues},
 type = {inProceedings},
 year = {2005},
 identifiers = {[object Object]},
 pages = {517-522},
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 abstract = {This paper describes the hardware and behavior implementation of a miniature robot, in size of a match box, that is able to interact with cockroaches. The robot is equipped with two micro-processors dedicated to hardware processing and behavior generation. It is also equipped with 12 infra-red proximity sensors, 2 light sensors, a linear camera and a battery that allows 3 hours autonomy. The robot can discriminate cockroaches, other robots, environment boundaries and shelters. It has also three means of communication: a wireless module for monitoring and logging, an IR remote receiver for fast supervision of biological experiment and a simple local communication protocol via infrared proximity sensors to detect robots in short range},
 bibtype = {inProceedings},
 author = {Tâche, F and Asadpour, M and Caprari, G and Karlen, Walter and Siegwart, R},
 booktitle = {2005 IEEE International Conference on Robotics and Biomimetics - ROBIO}
}

This paper describes the hardware and behavior implementation of a miniature robot, in size of a match box, that is able to interact with cockroaches. The robot is equipped with two micro-processors dedicated to hardware processing and behavior generation. It is also equipped with 12 infra-red proximity sensors, 2 light sensors, a linear camera and a battery that allows 3 hours autonomy. The robot can discriminate cockroaches, other robots, environment boundaries and shelters. It has also three means of communication: a wireless module for monitoring and logging, an IR remote receiver for fast supervision of biological experiment and a simple local communication protocol via infrared proximity sensors to detect robots in short range