| description abstract | Abstract. Hypertrophic cardiomyopathy (HCM) is a genetic heart disease affecting approximately 1 in 500 people and is the leading cause of sudden cardiac death (SCD) in young athletes. Current diagnostic methods—cardiovascular magnetic resonance (CMR), echocardiography, and genetic testing—are limited by high costs, operator dependency, or insufficient accuracy, while standard electrocardiogram (ECG) analysis cannot reliably distinguish HCM from acquired left ventricular hypertrophy (LVH). This paper presents a wearable ECG device paired with a classification algorithm that differentiates HCM from acquired LVH using ECG signals alone. The portable device integrates a 3-lead electrode system, an AD8232 signal conditioning module, an Arduino Nano 33 BLE microcontroller, and a lithium polymer battery. The algorithm extracts two quantitative indices—HCM index 1 and HCM index 2—from each heartbeat and classifies patients via dual statistical thresholds. Validation on 483 LVH patients (PhysioNet) and 29 HCM patients (digitized clinical records) yields 75.86% sensitivity, 99.17% specificity, and an F1-score of 80.00%. Leave-one-out cross-validation (LOOCV) confirms generalizability, with cross-validated sensitivity of 72.41%, specificity of 98.96%, and F1-score of 76.36% (95% confidence intervals (CIs) reported). A digitization confound analysis demonstrates that the classification is driven by physiological cardiac features rather than data source artifacts. A simulated device acquisition chain analysis confirms that the wearable hardware's signal characteristics are compatible with the classification algorithm. The system offers a promising tool for affordable HCM screening in resource-limited settings. | |
