PPG-based Heart Rate Estimation with Time-Frequency Spectra: A Deep Learning Approach

PPG-based continuous heart rate estimation is challenging due to the effects of physical activity. Recently, methods based on time-frequency spectra emerged to compensate motion artefacts. However, existing approaches are highly parametrised and optimised for specific scenarios. In this paper, we first argue that cross-validation schemes should be adapted to this topic, and show that the generalisation capabilities of current approaches are limited. We then introduce deep learning, specifically CNN-models, to this domain. We investigate different CNN-architectures (e.g. the number of convolutional layers, applying batch normalisation, or ensemble prediction), and report insights based on our systematic evaluation on two publicly available datasets. Finally, we show that our CNN-based approach performs comparably to classical methods.