Signal-to-noise ratio is more important than sampling rate in beat-to-beat interval estimation from optical sensors. (April 2022)

• Record Type: Journal Article
• Authors: Zaunseder, Sebastian; Vehkaoja, Antti; Fleischhauer, Vincent; Hoog Antink, Christoph
• Is Part Of: Biomedical signal processing and control. Volume 74(2022)
• Publication Date: 2022-04
• Subjects: Photoplethysmography -- PPG -- Photoplethysmogram -- Photoplethysmographic imaging -- PPGI -- Sampling rate -- Sampling frequency -- Frame rate -- Signal-to-noise ratio -- Monte Carlo simulation -- Imaging -- Wearable -- Pulse decomposition; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103538
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Even low sampling rates can yield low interval estimation errors in PPG simulations. A high signal-to-noise ratio is crucial to obtain accurate interval estimates. Subtle variations in beat morphology increase error levels up to 800%. Ignoring the influence of noise might explain variations in the literature. Abstract: Photoplethysmographic Imaging (PPGI) allows the determination of pulse rate variability from sequential beat-to-beat intervals (BBI) and pulse wave velocity from spatially resolved recorded pulse waves. In either case, sufficient temporal accuracy is essential. The presented work investigates the temporal accuracy of BBI estimation from photoplethysmographic signals. Within comprehensive numerical simulation, we systematically assess the impact of sampling rate, signal-to-noise ratio (SNR), and beat-to-beat shape variations on the root mean square error (RMSE) between real and estimated BBI. Our results show that at sampling rates beyond 14 Hz only small errors exist when interpolation is used. For example, the average RMSE is 3 ms for a sampling rate of 14 Hz and an SNR of 18 dB. Further increasing the sampling rate only results in marginal improvements, e.g. more than tripling the sampling rate to 50 Hz reduces the error by approx. 14%. The most important finding relates to the SNR, which is shown to have a much stronger influence on the error than the sampling rate. For example, increasing the SNR from 18 dB to 24 dB at 14 Hz sampling rate reduced the error by almost 50% to 1.5 ms. Subtle beat-to-beat shape variations, moreover, increase the error decisively by up to 800%. Our results are highly relevant in three regards: first, they partially explain different results in the literature on minimum sampling rates. Second, they emphasize the importance to consider SNR and possibly shape variation in investigations on the minimal sampling rate. Third, they underline the importance of appropriate processing techniques to increase SNR. Importantly, though our motivation is PPGI, the presented work immediately applies to contact PPG and PPG in other settings such as wearables. To enable further investigations, we make the scripts used in modelling and simulation freely available.