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

• Published in: Biomedical signal processing and control Vol. 68; p. 102589
• Main Authors: Béres, Szabolcs, Hejjel, László
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2021
• Subjects: Heart rate variability; Photoplethysmography; Sampling rate; Wearable devices; Photoplethysmography; Wearable devices; Heart rate variability; Sampling rate
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2021.102589

•Photoplethysmography is a popular way to monitor pulse rate by wearable devices.•Lower sampling rate reduces power consumption, memory- and transmission needs.•Heart (Pulse) rate variability analysis requires appropriate temporal resolution.•Interpolation methods can improve the accuracy of HRV parameters from lower temporal resolution records. 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. 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. 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. 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.