The minimal sampling frequency of the photoplethysmogram for accurate pulse rate variability parameters in healthy volunteers

Objective: Today mobile health-monitoring devices calculate heart rate and its variability mostly from the photoplethysmogram (PPG). Minimizing the power consumption is crucial, one option is optimizing the signal sampling rate. Present study aimed to determine the minimal sampling frequency of the PPG signal, which is sufficient for accurate heart rate variability (HRV) analysis. Methods: 57 high-quality, 5-minute PPG signals from healthy volunteers sampled at 1 kHz (master) were decimated by a factor of 2, 5, 10, 20, 50, 100, 200, 500, then cubic spline and parabola interpolated back to 1 ms resolution. The mean pulse rate, its standard deviation (SDNN), root mean square of successive RR-differences (RMSSD), Porta and Guzik indices (PI, GI) were calculated. Their relative accuracy error (RAE) was determined, RAE<5% was acceptable. Also, the processing times were measured. Results: 200ms sampling interval without interpolation is sufficient to calculate mean pulse rate with RAE<0.05%. SDNN and RMSSD require at least 20 ms sampling interval without interpolation (RAE: 1.28 +/- 0.96% and 4.25 +/- 3.59%, respectively). By both interpolations, these sampling intervals can be increased to 100 ms and 50 ms, respectively. Also, the accuracy of GI and PI can be improved by interpolation, here parabola approximation was better (GI: 20 ms versus 100 ms, PI: 50 ms versus 100 ms). Parabola approximation needs significantly less computation than cubic spline interpolation. Conclusions: For monitoring the average heart rate, 5 Hz sampling frequency can be sufficient without interpolation in healthy subjects. Correct HRV analysis requires higher sampling rates depending on the parameter. Interpolation can improve HRV accuracy from lower temporal resolution PPGs.

Automated summary

The study found that a sampling frequency of 5 Hz may be sufficient for monitoring average heart rate in healthy subjects, while accurate HRV analysis requires higher sampling rates. Interpolation techniques can improve HRV accuracy from lower temporal resolution PPG signals.