Self-Supervised and Topological Signal-Quality Assessment for Any PPG Device

Wei Shao, Ruoyu Zhang, Zequan Liang, Ehsan Kourkchi, Setareh Rafatirad, Houman Homayoun

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Keywords: photoplethysmography, signal quality, self-supervised learning, persistent homology, wearable sensing

Abstract: Wearable photoplethysmography (PPG) is embedded in billions of devices, yet its optical waveform is easily corrupted by motion, perfusion loss, and ambient light—jeopardizing downstream cardiometric analytics. Existing signal-quality assessment (SQA) methods rely either on brittle heuristics or on data-hungry supervised models. We introduce the first fully unsupervised SQA pipeline for wrist PPG. Stage 1 trains a contrastive 1-D ResNet-18 on 276 h of raw, unlabeled data from heterogeneous sources (varying in device and sampling frequency), yielding optical-emitter– and motion-invariant embeddings (i.e., the learned representation is stable across differences in LED wavelength, drive intensity, and device optics, as well as wrist motion). Stage 2 converts each 512-D encoder embedding into a 4-D topological signature via persistent homology (PH) and clusters these signatures with HDBSCAN. To produce a binary signal-quality index (SQI), the acceptable PPG signals are represented by the densest cluster while the remaining clusters are assumed to mainly contain poor-quality PPG signals. Without re-tuning, the SQI attains Silhouette, Davies–Bouldin, and Calinski–Harabasz scores of 0.72, 0.34, and 6,173, respectively, on a stratified sample of 10,000 windows. In this study, we propose a hybrid self-supervised-learning–topological-data-analysis (SSL–TDA) framework that offers a drop-in, scalable, cross-device quality gate for PPG signals.

Track: 3. Signal processing, machine learning, deep learning, and decision-support algorithms for digital and computational health

NominateReviewer: Houman Homayoun, <[e-mail]>

Submission Number: 45