Real-Time Myocardial Infarction Detection Approaches with a Microcontroller-Based Edge-AI Device.
In: Sensors (14248220), Jg. 24 (2024-02-01), Heft 3, S. 828-841
Online
academicJournal
Zugriff:
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. [ABSTRACT FROM AUTHOR]
Copyright of Sensors (14248220) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Titel: |
Real-Time Myocardial Infarction Detection Approaches with a Microcontroller-Based Edge-AI Device.
|
---|---|
Autor/in / Beteiligte Person: | Gragnaniello, Maria ; Borghese, Alessandro ; Marrazzo, Vincenzo Romano ; Maresca, Luca ; Breglio, Giovanni ; Irace, Andrea ; Riccio, Michele |
Link: | |
Zeitschrift: | Sensors (14248220), Jg. 24 (2024-02-01), Heft 3, S. 828-841 |
Veröffentlichung: | 2024 |
Medientyp: | academicJournal |
ISSN: | 1424-8220 (print) |
DOI: | 10.3390/s24030828 |
Schlagwort: |
|
Sonstiges: |
|