用自监督网络从噪声PPG中分离心跳信号，提升心率检测精度。
Self-Supervised Autoencoder Network for Robust Heart Rate Extraction from Noisy Photoplethysmogram: Applying Blind Source Separation to Biosignal Analysis
- 设计多编码器自编码器，通过自监督学习分离心跳源信号。
- 在9名受试者日常活动数据上，心率检测准确率显著优于原始信号。
- 无需预处理，适合真实场景下的生物信号分离任务。
生物信号可视为反映特定生理事件的混合信号，盲源分离（BSS）旨在从混合信号中提取潜在源信号。本文提出一种自监督多编码器自编码器（MEAE），用于从光电容积脉搏波（PPG）中分离与心跳相关的源信号，从而提升噪声环境下心率（HR）检测性能。该MEAE在大规模公开多导睡眠图数据库的PPG信号上训练，无需任何预处理或数据筛选。训练后的模型应用于九名受试者在日常活动中采集的噪声PPG数据集。分离出的心跳相关源信号显著改善了心率检测效果，优于原始PPG信号。该方法无需预处理且具备自监督特性，结合其优异表现，凸显了MEAE在生物信号分析中进行盲源分离的巨大潜力。
原文摘要 · Abstract (English)
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 MEAE for BSS in biosignal analysis.
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