Estimation of heart rate and heart rate variability from pulse oximeter recordings using localized model fitting

Heart rate variability is one of the key parameters for assessing the health status of a subject's cardiovascular system. This paper presents a local model fitting algorithm used for finding single heart beats in photoplethysmogram recordings. The local fit of exponentially decaying cosines of frequencies within the physiological range is used to detect the presence of a heart beat. Using 42 subjects from the CapnoBase database, the average heart rate error was 0.16 BPM and the standard deviation of the absolute estimation error was 0.24 BPM.

[1]  N H Lovell,et al.  Signal quality measures for pulse oximetry through waveform morphology analysis , 2011, Physiological measurement.

[2]  Walter Karlen,et al.  Multiparameter Respiratory Rate Estimation From the Photoplethysmogram , 2013, IEEE Transactions on Biomedical Engineering.

[3]  Hans-Andrea Loeliger,et al.  Sparse-input Detection Algorithm with Applications in Electrocardiography and Ballistocardiography , 2015, BIOSIGNALS.

[4]  Zhilin Zhang,et al.  TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise , 2014, IEEE Transactions on Biomedical Engineering.

[5]  J M Bland,et al.  Statistical methods for assessing agreement between two methods of clinical measurement , 1986 .

[6]  W.J. Tompkins,et al.  ECG beat detection using filter banks , 1999, IEEE Transactions on Biomedical Engineering.

[7]  Gary G Berntson,et al.  Filter properties of root mean square successive difference (RMSSD) for heart rate. , 2005, Psychophysiology.

[8]  Walter Karlen,et al.  Empirical mode decomposition for respiratory and heart rate estimation from the photoplethysmogram , 2013, Computing in Cardiology 2013.

[9]  Hans-Andrea Loeliger,et al.  Deconvolution of weakly-sparse signals and dynamical-system identification by Gaussian message passing , 2015, 2015 IEEE International Symposium on Information Theory (ISIT).

[10]  Jianfeng Weng,et al.  An Improved Pre-processing Approach for Photoplethysmographic Signal , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[11]  J. Vagedes,et al.  How accurate is pulse rate variability as an estimate of heart rate variability? A review on studies comparing photoplethysmographic technology with an electrocardiogram. , 2013, International journal of cardiology.

[12]  Hans-Andrea Loeliger,et al.  Local statistical models from deterministic state space models, likelihood filtering, and local typicality , 2014, 2014 IEEE International Symposium on Information Theory.