Real-Time Myocardial Infarction Detection Approaches with a Microcontroller-Based Edge-AI Device
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...
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MDPI AG
2024-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/3/828 |
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| author | Maria Gragnaniello Alessandro Borghese Vincenzo Romano Marrazzo Luca Maresca Giovanni Breglio Andrea Irace Michele Riccio |
| author_facet | Maria Gragnaniello Alessandro Borghese Vincenzo Romano Marrazzo Luca Maresca Giovanni Breglio Andrea Irace Michele Riccio |
| author_sort | Maria Gragnaniello |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T03:48:58Z |
| format | Article |
| id | doaj.art-99477a07ea9e46af9f50db7c560cc800 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T03:48:58Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-99477a07ea9e46af9f50db7c560cc8002024-02-09T15:21:58ZengMDPI AGSensors1424-82202024-01-0124382810.3390/s24030828Real-Time Myocardial Infarction Detection Approaches with a Microcontroller-Based Edge-AI DeviceMaria Gragnaniello0Alessandro Borghese1Vincenzo Romano Marrazzo2Luca Maresca3Giovanni Breglio4Andrea Irace5Michele Riccio6Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, ItalyDepartment of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, ItalyDepartment of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, ItalyDepartment of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, ItalyDepartment of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, ItalyDepartment of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, ItalyDepartment of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, ItalyMyocardial 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.https://www.mdpi.com/1424-8220/24/3/828deep learningedge computingmachine learningmyocardial infarction detection |
| spellingShingle | Maria Gragnaniello Alessandro Borghese Vincenzo Romano Marrazzo Luca Maresca Giovanni Breglio Andrea Irace Michele Riccio Real-Time Myocardial Infarction Detection Approaches with a Microcontroller-Based Edge-AI Device Sensors deep learning edge computing machine learning myocardial infarction detection |
| title | Real-Time Myocardial Infarction Detection Approaches with a Microcontroller-Based Edge-AI Device |
| title_full | Real-Time Myocardial Infarction Detection Approaches with a Microcontroller-Based Edge-AI Device |
| title_fullStr | Real-Time Myocardial Infarction Detection Approaches with a Microcontroller-Based Edge-AI Device |
| title_full_unstemmed | Real-Time Myocardial Infarction Detection Approaches with a Microcontroller-Based Edge-AI Device |
| title_short | Real-Time Myocardial Infarction Detection Approaches with a Microcontroller-Based Edge-AI Device |
| title_sort | real time myocardial infarction detection approaches with a microcontroller based edge ai device |
| topic | deep learning edge computing machine learning myocardial infarction detection |
| url | https://www.mdpi.com/1424-8220/24/3/828 |
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