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Real-Time Myocardial Infarction Detection Approaches with a Microcontroller-Based Edge-AI Device
Maria Gragnaniello1, Alessandro Borghese1, Vincenzo Romano Marrazzo1
1Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, Italy.
Insights
This study presents an edge computing system for real-time Myocardial Infarction (MI) detection using a single microcontroller. Machine learning and deep learning models achieve high accuracy on low-power hardware, enabling continuous, cloud-free monitoring.
Area of Science:
- Biomedical Engineering
- Cardiology
- Computer Science
Background:
- Myocardial Infarction (MI), or heart attack, requires continuous monitoring due to its recurring and often asymptomatic nature.
- Wearable devices are crucial for real-time health surveillance, but often rely on cloud connectivity for data processing.
- Edge computing offers a decentralized approach for on-device data analysis, reducing latency and enhancing privacy.
Purpose of the Study:
- To develop and evaluate a single-microcontroller system for automatic Myocardial Infarction (MI) detection using edge computing.
- To compare the performance of Machine Learning (ML) and Deep Learning (DL) algorithms optimized for low-resource hardware.
- To assess the feasibility of real-time MI detection on a 32-bit microcontroller with an ARM Cortex-M4 core.
Main Methods:
- Implementation of two MI detection algorithms: one based on ML with feature extraction and a simpler Neural Network (NN), and another based on DL using Spectrogram Analysis and a Convolutional Neural Network (CNN).
- Optimization of both ML and DL algorithms for deployment on low-resource, single-microcontroller hardware (ARM Cortex-M4).
- Feasibility assessment including accuracy, inference time, and memory usage analysis for both approaches.
Main Results:
- The ML approach achieved 89.40% accuracy, while the DL approach reached 94.76% accuracy, both utilizing the same low-power hardware.
- The DL method, while having a longer inference time and higher memory usage, demonstrated superior detection accuracy.
- The developed system performs all processing at the edge, enabling real-time MI detection without cloud or remote server reliance.
Conclusions:
- A single-microcontroller edge computing system can effectively perform real-time Myocardial Infarction detection.
- Deep learning models, despite higher resource demands, offer superior accuracy for MI detection on resource-constrained devices.
- The energy-efficient prototype enables continuous, private, and immediate MI monitoring via wearable technology.
Abstract:
Myocardial Infarction (MI), commonly known as heart attack, is a cardiac condition characterized by damage to a portion of the heart, specifically the myocardium, due to the disruption of blood flow. Given its recurring and often asymptomatic nature, there is the need for continuous monitoring using wearable devices. This paper proposes a single-microcontroller-based system designed for the automatic detection of MI based on the Edge Computing paradigm. Two solutions for MI detection are evaluated, based on Machine Learning (ML) and Deep Learning (DL) techniques. The developed algorithms are based on two different approaches currently available in the literature, and they are optimized for deployment on low-resource hardware. A feasibility assessment of their implementation on a single 32-bit microcontroller with an ARM Cortex-M4 core was examined, and a comparison in terms of accuracy, inference time, and memory usage was detailed. For ML techniques, significant data processing for feature extraction, coupled with a simpler Neural Network (NN) is involved. On the other hand, the second method, based on DL, employs a Spectrogram Analysis for feature extraction and a Convolutional Neural Network (CNN) with a longer inference time and higher memory utilization. Both methods employ the same low power hardware reaching an accuracy of 89.40% and 94.76%, respectively. The final prototype is an energy-efficient system capable of real-time detection of MI without the need to connect to remote servers or the cloud. All processing is performed at the edge, enabling NN inference on the same microcontroller.
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