Key result
The WaveHRV algorithm achieved the lowest mean absolute error of 10.5 ms for RMSSD and 6.15 ms for SDNN on the UBFCrPPG dataset compared to existing algorithms.
Why the study?
Does the WaveHRV algorithm improve the accuracy of heart rate variability measurement from facial videos compared to existing algorithms?
Does the WaveHRV algorithm improve the accuracy of heart rate variability measurement from facial videos compared to existing algorithms?
The WaveHRV algorithm provides a promising contactless method for accurate heart rate variability measurement from facial videos.
May enable remote HRV monitoring; leaves open clinical validation before practice adoption.
Remote Photoplethysmography (rPPG) is a contactless method that enables the detection of various physiological signals from facial videos. rPPG utilizes a digital camera to detect subtle changes in skin color to measure vital signs such as heart rate variability (HRV), an important biomarker related to the autonomous nervous system. This paper presents a novel contactless HRV extraction algorithm, WaveHRV, based on the Wavelet Scattering Transform technique, followed by adaptive bandpass filtering and inter-beat-interval (IBI) analysis. Furthermore, a novel method is introduced to preprocess noisy contact-based PPG signals. WaveHRV is bench-marked against existing algorithms and public datasets. Our results show that WaveHRV is promising and achieves the lowest mean absolute error (MAE) of 10.5 ms and 6.15 ms for RMSSD and SDNN on the UBFCrPPG dataset.
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Odinaev et al. (2023) studied this question. WaveHRV algorithm vs. existing algorithms was evaluated on mean absolute error (MAE) for RMSSD and SDNN. The WaveHRV algorithm achieved the lowest mean absolute error of 10.5 ms for RMSSD and 6.15 ms for SDNN on the UBFCrPPG dataset compared to existing algorithms.
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