Heart rate variability (HRV) is a useful clinical tool for autonomic function assessment and cardiovascular diseases diagnosis. It is traditionally calculated from a dedicated medical electrocardiograph (ECG). In this paper, we demonstrate that HRV can also be extracted from photoplethysmograms (PPG) obtained by the camera of a smartphone. Sixteen HRV parameters, including time-domain, frequency-domain, and nonlinear parameters, were calculated from PPG captured by a smartphone for 30 healthy subjects and were compared with those derived from ECG. The statistical results showed that 14 parameters (AVNN, SDNN, CV, RMSSD, SDSD, TP, VLF, LF, HF, LF/HF, nLF, nHF, SD1, and SD2) from PPG were highly correlated (r > 0.7, P < 0.001) with those from ECG, and 7 parameters (AVNN, TP, VLF, LF, HF, nLF, and nHF) from PPG were in good agreement with those from ECG within the acceptable limits. In addition, five different algorithms to detect the characteristic points of PPG wave were also investigated: peak point (PP), valley point (VP), maximum first derivative (M1D), maximum second derivative (M2D), and tangent intersection (TI). The results showed that M2D and TI algorithms had the best performance. These results suggest that the smartphone might be used for HRV measurement.

Abstract-Because it is more convenient to measure photoplethysmography (PPG) than ECG, PPG is supposed as a surrogate of ECG for heart rate variability (HRV) analysis. In this study, we measured the spectrum of pulse rate variability (PRV) from PPG and its square coherence spectrum with HRV from ECG before and after exercise. The results showed that the spectrum of PRV corresponds almost well with HRV, especially for the healthy subjects at rest. However, stimuli conditions such as exercise will decrease the correlation, especially for the high frequency (HF) component. It indicates that whether PRV can be used as an alternative to HRV depends on the applications and conditions.