Self-supervised autoencoder network for robust heart rate extraction from noisy photoplethysmogram: Applying blind source separation to biosignal analysis - Webster, Matthew B.; Lee, Dongheon; Lee, Joonnyong (Computers in biology and medicine, 2025)
Biosignals can be viewed as mixtures measuring particular physiological events, and blind source separation (BSS) aims to extract underlying source signals from mixtures. This paper proposes a self-supervised multi-encoder autoencoder (MEAE) to separate heartbeat-related source signals from photoplethysmogram (PPG), enhancing heart rate (HR) detection in noisy PPG data. The MEAE is trained on PPG signals from a large open polysomnography database without any pre-processing or data selection. The trained network is then applied to a noisy PPG dataset collected during the daily activities of nine subjects and a surgical dataset comprising 4,681 patients. The extracted heartbeat-related source signal significantly improves HR detection as compared to the original PPG. The absence of pre-processing and the self-supervised nature of the proposed method, combined with its strong performance, highlight the potential of MEAE for BSS in biosignal analysis. •Biosignals are often difficult to analyze due to presence of unknown noises including motion artifacts.•Blind source separation via self-supervised autoencoder allows to separate out information from biosignals.•Blind source separation applied to noisy photoplethysmogram successfully isolated heartbeat related source.•Blind source separation without any directives (supervision in deep learning) can result in extraction of information present in the training data.•Analyses of large biosignal databases can benefit from self-supervised blind source separation due to the lack of pre-processing or data selection.