Self-Supervised Autoencoder Network for Robust Heart Rate Extraction from Noisy Photoplethysmogram: Applying Blind Source Separation to Biosignal Analysis

• URL: http://arxiv.org/abs/2504.09132v1
• Date: Sat, 12 Apr 2025 08:47:53 GMT
• Title: Self-Supervised Autoencoder Network for Robust Heart Rate Extraction from Noisy Photoplethysmogram: Applying Blind Source Separation to Biosignal Analysis
• Authors: Matthew B. Webster, Dongheon Lee, Joonnyong Lee,
• Abstract summary: Blind source separation (BSS) aims to extract underlying source signals from mixtures.<n>This paper proposes a self-supervised multi-encoder autoencoder (MEAE) to separate source signals from photoplethysmogram ( PPG)<n>The MEAE is trained on PPG signals from a large open polysomnography database without any pre-processing or data selection.
• Score: 2.069879636268966
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. 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 BSS in biosignal analysis.

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