Estimating heart rate variability using facial video photoplethysmography: a pilot validation study
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Introduction
Video photoplethysmography (vPPG) employs a digital camera to detect blood pulsations in the skin vasculature, which can be used to estimate various physiological parameters. In this study, we investigate the accuracy and precision of two heart rate variability (HRV) indices estimated using a smartphone camera and the facial vPPG technology Shen.AI Vitals.
Methods
The study group included 35 healthy volunteers (17 females) with median age 25 years (range 20–42 years). The subjects were in a sitting position, keeping their heads relatively still. A smartphone mounted on a tripod was used to acquire 1-min video recordings of participants’ faces. In parallel, a 1-lead chest electrocardiogram (ECG) was recorded to obtain reference values of two analysed time-domain HRV indices: SDNN and lnRMSSD.
Results
For SDNN, the mean absolute error (MAE) was 3.5 ms (11.0% in relative terms) and the root-mean-square error (RMSE) was 4.5 ms (15.7%). For lnRMSSD, the MAE was 0.24 (7.3%) and RMSE was 0.31 (9.9%). Correlations between the vPPG-based and ECG-based HRV values were strong, with the Pearson correlation coefficient of 0.98 for SDNN and 0.88 for lnRMSSD (P < 0.001 in both cases).
Conclusions
In a young, white population, the tested vPPG technology estimated HRV indices (SDNN and lnRMSSD) with acceptable accuracy in most subjects, with a slight systematic overestimation, especially for low values. The results should be confirmed in a larger study with greater diversity in age and skin tone.
