Adaptive Change Detection in Heart Rate Trend Monitoring in Anesthetized Children

TitleAdaptive Change Detection in Heart Rate Trend Monitoring in Anesthetized Children
Publication TypeJournal Article
Year of Publication2006
AuthorsYang, P., G. Dumont, and J. M. Ansermino
JournalBiomedical Engineering, IEEE Transactions on
Pagination2211 -2219
Date Publishednov.
Keywordsadaptive change detection, adaptive filters, adaptive Kalman filter, anesthetized children, cardiovascular system, covariance analysis, cumulative sum testing, exponentially weighted moving average predictor, forgetting coefficients, Gaussian noise, haemodynamics, heart rate trend monitoring, interpatient variations, intraoperative variations, Kalman filters, noise covariance estimation, paediatrics, patient monitoring, receiver operating characteristic curve analysis, sensitivity analysis, Trigg approach, white noise

The proposed algorithm is designed to detect changes in the heart rate trend signal which fits the dynamic linear model description. Based on this model, the interpatient and intraoperative variations are handled by estimating the noise covariances via an adaptive Kalman filter. An exponentially weighted moving average predictor switches between two different forgetting coefficients to allow the historical data to have a varying influence in prediction. The cumulative sum testing of the residuals identifies the change points online. The algorithm was tested on a substantial volume of real clinical data. Comparison of the proposed algorithm with Trigg's approach revealed that the algorithm performs more favorably with a shorter delay. The receiver operating characteristic curve analysis indicates that the algorithm outperformed the change detection by clinicians in real time


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