Edge-AI on Wearable Devices: Myocardial Infarction Detection with Spectrogram and 1D-CNN

The Electrocardiogram (ECG) provides a detailed representation of the heart's electrical activity, emerging as a crucial resource for continuous cardiac health monitoring. Recent advances in Artificial Intelligence (AI) techniques have revolutionized ECG signal processing, creating new possibilities for every-day health monitoring. The exploitation of these AI technologies has driven a growing interest in wearable devices where the challenge is to implement these functionalities on limited memory resource hardware. In this scenario, the Edge Computing paradigm, where computation occurs near the data source rather than in a remote data center, emerges as a promising solution. This article proposes an efficient approach for Myocardial Infarction (MI) detection based on Deep Learning (DL) methods using spectrogram and a 1D Convolutional Neural Network (1D-CNN). The aim is to strike a balance between computational efficiency and accuracy enabling practical application on wearable devices. In the presented work, a study on the impact of the spectrogram parameters and results on the 1D-CNN was conducted. The final training phase yielded a remarkable accuracy of 95.94%, showcasing the efficacy of the proposed approach. Notably, the trained model was successfully deployed on a 32-bit microcontroller featuring an ARM Cortex-M4 architecture, underscoring the feasibility of real-world implementation for embedded systems in healthcare applications.