Privacy-Preserving Single-Lead Smartwatch ECG Monitoring for Multi-Class Arrhythmia Detection

This work presents a fully local, privacy-preserving inference framework, ECGNet, for multi-class arrhythmia detection from simulated 1-lead smartwatch ECG, extending beyond binary Normal/AFib detection to four clinically relevant rhythm classes. A confidence-calibration mechanism withholds low-confidence predictions rather than forcing unreliable diagnoses, and a fair 12-lead vs. single-lead benchmark under an identical model quantifies the diagnostic cost of wearable-grade acquisition, achieving 90.64% accuracy with a 9.36% inconclusive rate at a calibrated 0.80 confidence threshold. A hardware-agnostic sampling-rate simulator further evaluates robustness across device-specific acquisition rates without requiring physical wearable hardware.