Automated Generation of Tiny Model for Real-Time ECG Classification on Tiny Edge Devices

Abstract

Continuous monitoring of cardiac health through single-lead wearable Electrocardiogram (ECG), is important for paroxysmal Atrial Fibrillation (AF) detection. Wearable ECG straps, watches, and implantable loop recorders (ILR) are based on this paradigm. These devices are used by medical professionals to view data from multiple patients, perform continuous monitoring and analysis to provide immediate care to patients. These monitoring devices display simple health screening alerts to the subjects and generate distress signals for people working outdoors or in isolated environments with intermittent Internet connectivity. Hence, low-memory, low-power, low-latency on-device inference becomes very important. This work aims at realizing such solutions by providing a framework to generate tiny (less than 256 KB) Deep Neural Networks customized for typical microcontrollers (MCU) used in those devices.