Adaptive Change Point Detection for Respiratory Variables

TitleAdaptive Change Point Detection for Respiratory Variables
Publication TypeConference Paper
Year of Publication2005
AuthorsYang, P., G. Dumont, J. Lim, and J. M. Ansermino
Conference NameEngineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the
Pagination780 -783
Keywordsadaptive change point detection, adaptive Kalman filter, adaptive Kalman filters, CO2, CUSUM testing, dynamic linear growth model, end-tidal carbon dioxide, expectation-maximisation algorithm, expiratory minute volume, medical signal detection, medical signal processing, noise, noise covariances, patient monitoring, physiological monitoring, pneumodynamics, recursive estimation, recursive expectation-maximization method, respiratory rate, respiratory variables

Current alarm strategies for physiological monitoring depend on predetermined thresholds without consideration for the heterogeneity between patients or intraoperative variations. To improve upon this situation, we developed an adaptive change point detection scheme to automatically notify the clinician when a change of clinical significance has occurred in the respiratory variables. We modeled end-tidal carbon dioxide, expiratory minute volume, and respiratory rate using a dynamic linear growth model, whose noise covariances are estimated by an adaptive Kalman filter based on a recursive expectation-maximization method. Change points are detected by the CUSUM testing. The comparison of the results with post-hoc expert annotations demonstrates that the algorithm can accurately detect relevant changes in the respiratory signals


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