Year: 2025
Type: article
Abstract: Aortic stenosis (AS) is the most prevalent valvular heart disease, potentially leading to severe complications such as heart failure, stroke, and sudden death if left untreated. Many patients remain undiagnosed due to the absence of prominent symptoms. Given the serious consequences of AS and the prevalence of undiagnosed asymptomatic patients, there is an urgent need for large-scale screening to enhance early detection and improve patient prognosis. In this work, we propose PASS, a PPG-based Aortic Stenosis Screening System designed for cost-effective, non-invasive, and scalable AS detection. Unlike previous studies that relied on small-scale cohorts or indirect cardiac signals, PASS leverages fingertip PPG to capture the hemodynamic impact of AS in a more accessible manner. To implement PASS, we develop a customized neural network feature extractor capable of performing segmentation, feature extraction, and fusion to capture subtle AS features within the PPG signals. Furthermore, we enhance the classification robustness of PASS by introducing a metric learning-based classifier designed to address the challenge of significant intra-class variation. We evaluate PASS on a real-world clinical dataset comprising 325 subjects, including 158 AS patients confirmed by echocardiography. Our system achieves 91.92% sensitivity and 92.75% specificity, demonstrating strong potential for population-level AS screening using consumer-grade devices.
DOI: 10.1145/3749548
Authors: Le Kang, Ruotong Yang, Yihui Liu, Anfu Zhou, Huadong Ma, Hao Cui
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Awards: G1474490597, G6287799790
OpenAlex ID: W4413968546