Detecting myocardial infraction in ECG waveforms using YOLOv8
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Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The detection of myocardial infarction (MI) using advanced imaging and analysis techniques is a pivotal advance- ment in medical diagnostics. This study employs YOLOv8, a sophisticated deep learning algorithm, for the identification of MI from the electrocardiogram (ECG) images, focusing specifically on the critical ST elevation segment associated with MI. Unlike traditional methods that rely on manual interpretation, our approach automates the detection process, significantly reducing the time and potential for human error. By fine-tuning YOLOv8 on a curated dataset and employing a loss-modified model, we achieved an average accuracy of 96% and a mean Average Precision (mAP) of 0.99 at a 50% confidence threshold, with a Recall of 97.7%. To make this technology accessible to medical staff and patients without technical expertise, we developed a user-friendly GUI that simplifies the detection process by clicking a button. This research not only highlights the potential of applying deep learning in cardiology but also emphasizes the importance of user-centered design in medical technology, promising significant improvements in clinical diagnosis and patient care.
Açıklama
Anahtar Kelimeler
Computer Vision, Deep Learning, Healthcare, YOLOv8, Myocar- dial infractions
Kaynak
2024 Global Digital Health Knowledge Exchange and Empowerment Conference: Knowledge Exchange of the State-of-the-Art Research and Development in Digital Health Technologies, Enable and Empower Stakeholders Engaged in Enriching and Enhancing the Patient Healthcare Journey, gDigiHealth.KEE 2024
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Albasrawi, R., Ilyas, M. (2024). Detecting myocardial infraction in ECG waveforms using YOLOv8. 2024 Global Digital Health Knowledge Exchange and Empowerment Conference: Knowledge Exchange of the State-of-the-Art Research and Development in Digital Health Technologies, Enable and Empower Stakeholders Engaged in Enriching and Enhancing the Patient Healthcare Journey, gDigiHealth.KEE 2024. 10.1109/gDigiHealth.KEE62309.2024.10761689
