Towards efficient heart rate variability estimation in artifact-induced Photoplethysmography signals

Heart Rate Variability (HRV) has become a marker for various health and disease conditions. Photoplethysmography (PPG) sensors integrated in wearable/portable devices such as smart watches and phones are widely used to measure heart activities. HRV requires accurate estimation of time interval between consecutive peaks in the PPG signal. Artifacts often degrade the quality of the PPG signal, which could lead to wrong HRV estimation. In this paper, we present an adaptive real-time approach that employs Linear Prediction analysis (LPC) and Wavelet transformation techniques for estimating HRV from PPG signal recorded by wearable devices. Our algorithm outperforms two other related algorithms, especially for low PPG signal to noise ratio. By comparing the proposed algorithm to the ground truth recorded simultaneously from ECG, an average temporal resolution of 8.7 ms was achieved with a sensitivity of 82.9% and a positive predictive value of 82.7%.