ROBUST BEAT-TO-BEAT DETECTION ALGORITHM FOR PULSE RATE VARIABILITY ANALYSIS FROM WRIST PHOTOPLETHYSMOGRAPHY SIGNALS

Heart rate variability (HRV) from electrocardiograms (ECG) is a well-known diagnostic method for the assessment of autonomic nervous function of the heart. A more convenient approach to assess cardiac function is by using Photoplethysmography (PPG) waveforms where pulse rate variability (PRV) replaces HRV. However, the unavailability of robust detection algorithms for PPG signals has prevented the medical market from providing clinical diagnosis using PRV and from measuring biological information for wellness purposes, such as sleep stage, stress state, and fatigue. This paper provides a robust peak and onset detection algorithm for beat-to-beat (B2B) pulse interval analysis using PPG signals. We demonstrate our method through large data collection with the Analog Devices (ADI) multi-sensory watch platform with high coverage, sensitivity, and low Root Mean Square of Successive Difference (RMSSD) as compared to the B2B results from ECG signals.