Energy-Aware Design Methodology for Myocardial Infarction Detection on Low-Power Wearable Devices

Abstract

Myocardial Infarction (MI) is a heart disease that damages the heart muscle and requires immediate treatment. Its silent and recurrent nature necessitates real-time continuous monitoring of patients. Nowadays, wearable devices are smart enough to perform on-device processing of heartbeat segments and report any irregularities in them. However, the small form factor of wearable devices imposes resource constraints and requires energy-efficient solutions to satisfy them. In this paper, we propose a design methodology to automate the design space exploration of neural network architectures for MI detection. This methodology incorporates Neural Architecture Search (NAS) using Multi-Objective Bayesian Optimization (MOBO) to render Pareto optimal architectural models. These models minimize both detection error and energy consumption on the target device. The design space is inspired by Binary Convolutional Neural Networks (BCNNs) suited for mobile health applications with limited resources. The models' performance is validated using the PTB diagnostic ECG database from PhysioNet. Moreover, energy-related measurements are directly obtained from the target device in a typical hardware-in-the-loop fashion. Finally, we benchmark our models against other related works. One model exceeds state-of-the-art accuracy on wearable devices (reaching 91.22%), whereas others trade off some accuracy to reduce their energy consumption (by a factor reaching 8.26x).