Robust estimation and tracking of heart rate by PPG signal analysis

Photoplethysmography (PPG) is a widely used technology, routinely employed for heart rate measurement in low-cost medical devices. Monitoring is notoriously more difficult during physical exercise, since motion artifacts may considerably degrade PPG signals. The approach discussed in this paper estimates human heart rate and reliably tracks its charges by a robust algorithm, whose main steps include denoising by joint principal component analysis, Fourier-based heart rate measurement and, finally, smoothing and tracking by a Kalman filter. To illustrate overall performance, experimental results are presented using publicly available real-life PPG traces.

[1]  E. Hari Krishna,et al.  A Novel Approach for Motion Artifact Reduction in PPG Signals Based on AS-LMS Adaptive Filter , 2012, IEEE Transactions on Instrumentation and Measurement.

[2]  P. Laguna,et al.  Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during non-stationary conditions , 2010, Physiological measurement.

[3]  V Jindal,et al.  An adaptive deep learning approach for PPG-based identification. , 2016, Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference.

[4]  Zhilin Zhang,et al.  Combining Nonlinear Adaptive Filtering and Signal Decomposition for Motion Artifact Removal in Wearable Photoplethysmography , 2016, IEEE Sensors Journal.

[5]  Sun K. Yoo,et al.  Motion artifact reduction in photoplethysmography using independent component analysis , 2006, IEEE Transactions on Biomedical Engineering.

[6]  Michele Rossi,et al.  A supervised learning approach for the robust detection of heart beat in plethysmographic data , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[7]  Westgate Road,et al.  Photoplethysmography and its application in clinical physiological measurement , 2007 .

[8]  Md. Kamrul Hasan,et al.  A Robust Heart Rate Monitoring Scheme Using Photoplethysmographic Signals Corrupted by Intense Motion Artifacts , 2016, IEEE Transactions on Biomedical Engineering.

[9]  G. Giorgi,et al.  Efficient tracking of heart rate under physical exercise from photoplethysmographic signals , 2015, 2015 IEEE 1st International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI).

[10]  Peter Xiaoping Liu,et al.  Secondary Peak Detection of PPG Signal for Continuous Cuffless Arterial Blood Pressure Measurement , 2014, IEEE Transactions on Instrumentation and Measurement.

[11]  Eduardo Gil,et al.  Heart Rate Turbulence Analysis Based on Photoplethysmography , 2013, IEEE Transactions on Biomedical Engineering.

[12]  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.