Non-Invasive Detection of Coronary Artery Disease Using Wearable Vest with Integrated Phonocardiogram Sensors

Background: Cardiovascular disease (CVD) remains the leading cause of death and disability worldwide. Among its subtypes, coronary artery disease (CAD) is the most common and often develops silently, without noticeable symptoms. CAD-related murmurs typically fall below the human hearing threshold, limiting the effectiveness of traditional stethoscope-based auscultation. Currently, the gold standard for CAD diagnosis is coronary angiography, an invasive and expensive procedure usually reserved for symptomatic patients. This highlights the global need for a non-invasive, cost-effective pre-screening tool for asymptomatic CAD detection. Objectives: This study investigates the effectiveness of a wearable vest equipped with multiple digital stethoscopes to detect CAD. By applying signal processing and machine learning to multichannel phonocardiogram (PCG) data, we aim to evaluate the accuracy of CAD detection. We further assess the impact of incorporating patient metadata to enhance model performance. Methods: Data were collected from 40 CAD patients and 40 non-CAD individuals using a wearable vest with seven embedded PCG sensors. Subjects performed 10 s breath-hold recordings in a clinical setting. Linear-frequency cepstral coefficients were extracted from the PCG signals and classified using a support vector machine. Metadata, including body mass index, blood pressure, type 2 diabetes, and hypertension, were integrated to assess performance gains. Results: A combination of four channels achieved an accuracy of 80.44%, a 7% improvement over the best single-channel result. Incorporating metadata increased accuracy to 82.08%. Conclusions: The wearable vest demonstrated promising clinical potential, exceeding a 75% sensitivity-specificity average, and may support accessible, automated CAD screening in future validated settings.