Detecting myocardial infraction in ECG waveforms using YOLOv8

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dc.contributor.authorAlbasrawi, Roaa
dc.contributor.authorIlyas, Muhammad
dc.date.accessioned2025-01-13T13:57:05Z
dc.date.available2025-01-13T13:57:05Z
dc.date.issued2024en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractThe 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.en_US
dc.identifier.citationAlbasrawi, 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.KEE62309.2024.10761689en_US
dc.identifier.isbn9798331531331
dc.identifier.scopus2-s2.0-85213351581
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5147
dc.indekslendigikaynakScopus
dc.institutionauthorAlbasrawi, Roaa
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2024 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
dc.relation.isversionof10.1109/gDigiHealth.KEE62309.2024.10761689en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectComputer Visionen_US
dc.subjectDeep Learningen_US
dc.subjectHealthcareen_US
dc.subjectYOLOv8en_US
dc.subjectMyocar- dial infractionsen_US
dc.titleDetecting myocardial infraction in ECG waveforms using YOLOv8
dc.typeConference Object

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