Real-time On-device Inference for ECG Classification of Ventricular Dysfunction

We present the development of a portable embedded system for real-time electrocardiogram (ECG) beat classification for ventricular dysfunction related arrhythmias and conditions. These conditions are clinically relevant for monitoring heart failure patients and possible Left Ventricular Assist Device (LVAD) need, as well as potentially enabling the monitoring of remodeling of the native heart after LVAD implantation. We create a dataset combining single-lead standard datasets from Physionet repository. The system processes single-lead ECG signals using a lightweight, quantized, hardware-accelerated model based on residual connections. We run the proof of concept in an ARM Cortex-M33-based SoC, measuring an inference time of 119 milliseconds with 0.95 mJ of energy consumption at 78 MHz, Flash usage of 709.6KB, and runtime RAM usage of 24.7 KB, with an average AUC of 0.9946 in the multiclass classification task. We validated the system end-to-end using a patient simulator and the MAX30003 sensor. The proposed system enables continuous monitoring without external computation, paving the way towards integration with LVAD systems to allow for patient monitoring.