Assessing Coronary Artery Disease Risk Using Seismocardiography in Patients with Chest Pain

This study introduces the EMR Score, a novel approach that integrates seismocardiography (SCG) features with clinical risk factors to estimate the pre-test probability of coronary artery disease (CAD) while focusing solely on chest pain presence. Data were collected from a multicenter randomized trial enrolling 1,640 participants, including 740 patients diagnosed with obstructive CAD and 900 healthy controls. CAD diagnosis was confirmed using coronary computed tomography angiography (CCTA) or invasive coronary angiography (ICA). SCG and electrocardiography (ECG) signals were recorded using the HeartForce CardioClin device. The EMR Score was developed using a one-dimensional convolutional neural network (1D CNN) trained on SCG-derived features and clinical variables, including age, sex, smoking status, hypertension, hyperlipidemia, diabetes, family history, and chest pain presence. The final model produced probabilities for CAD and non-CAD outcomes, optimized using categorical cross-entropy and the Adam optimizer, with performance evaluated via cross-validation. Unlike traditional models that rely on chest pain subtyping, the EMR Score follows the American Heart Association's (AHA) recommendation to prioritize chest pain as a key screening factor, reducing interobserver variability and improving applicability across diverse populations. The EMR Score outperformed the AHA model, achieving a higher AUC (0.85 vs. 0.74) and improved specificity (42% vs. 35%) while maintaining high sensitivity (96% vs. 90%). It also reclassified many intermediate-risk patients (69% in the AHA model vs. 19%), shifting them to low- (25%) or high-risk (55%) categories, where CAD prevalence was 8% and 70%, respectively. By eliminating subjective symptom classification and leveraging SCG-derived features, the EMR Score provides a scalable, cost-effective screening tool that enhances CAD risk stratification and optimizes clinical decision-making.