cbPPGGAN: A Generic Enhancement Framework for Unpaired Pulse Waveforms in Camera-Based Photoplethysmography

作者：Ze Yang, Haofei Wang, Bo Liu, Feng Lu · 发表于：IEEE Journal of Biomedical and Health Informatics · 年份：2023 · DOI：10.1109/jbhi.2023.3314282 · 被引用次数：14 · 研究领域：Non-Invasive Vital Sign Monitoring、Heart Rate Variability and Autonomic Control、Hemodynamic Monitoring and Therapy

Camera-based photoplethysmography (cbP PG) is a non-contact technique that measures cardiac-related blood volume alterations in skin surface vessels through the analysis of facial videos. While traditional approaches can estimate heart rate (HR) under different illuminations, their accuracy can be affected by motion artifacts, leading to poor waveform fidelity and hindering further analysis of heart rate variability (HRV); deep learning-based approaches reconstruct high-quality pulse waveform, yet their performance significantly degrades under illumination variations. In this work, we aim to leverage the strength of these two methods and propose a framework that possesses favorable generalization capabilities while maintaining waveform fidelity. For this purpose, we propose the cbPPGGAN, an enhancement framework for cbPPG that enables the flexible incorporation of both unpaired and paired data sources in the training process. Based on the waveforms extracted by traditional approaches, the cbPPGGAN reconstructs high-quality waveforms that enable accurate HR estimation and HRV analysis. In addition, to address the lack of paired training data problems in real-world applications, we propose a cycle consistency loss that guarantees the time-frequency consistency before/after mapping. The method enhances the waveform quality of traditional POS approaches in different illumination tests (BH-rPPG) and cross-datasets (UBFC-rPPG) with mean absolute error (MAE) values of 1.34 bpm and 1...