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

• URL: http://arxiv.org/abs/2509.12510v1
• Date: Mon, 15 Sep 2025 23:22:02 GMT
• Title: Self-Supervised and Topological Signal-Quality Assessment for Any PPG Device
• Authors: Wei Shao, Ruoyu Zhang, Zequan Liang, Ehsan Kourkchi, Setareh Rafatirad, Houman Homayoun,
• Abstract summary: Existing signal-quality assessment methods rely on brittles or on data-hungry supervised models.<n>We introduce the first fully unsupervised SQA pipeline for wrist photoplethysmography.<n>We propose a hybrid self-learning--topological-data-analysis framework that offers a drop-in, scalable, cross-device quality gate for PPG signals.
• Score: 10.55542625721902
• License: http://creativecommons.org/licenses/by/4.0/
• 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 6173, 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.

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