A novel method for accurate estimation of HRV from smartwatch PPG signals

Photoplethysmography(PPG) as a non-invasive tool for monitoring various cardiovascular parameters, has become popular due to the ease of wearable integration and pervasive nature. Due to unobtrusive nature of sensor placement at wrist, smartwatches and wrist based fitness bands have gained popularity. However, any movement of the wrist along with frequent loose contacts significantly corrupts the PPG signal. Reliable peak detection from the corrupted PPG signal is essential for any further processing, as many physiological quantities such as heart rate variability (HRV) depends on the peak-to-peak distances in the PPG signal, known as the RR Series. This paper attempts to provide a robust algorithm for peak detection in noise & motion artefact corrupted PPG signals. The algorithm consists of steps to remove the baseline drift in the PPG signal using wavelet filtering and trend removal and subsequent peak detection using autocorrelation for each pseudo-periodic segment of the signal. The validation of the method is done by comparing the PPG peaks detected by the algorithm with RR series extracted from simultaneously captured ECG signal.