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    Detecting myocardial infraction in ECG waveforms using YOLOv8
    (Institute of Electrical and Electronics Engineers Inc., 2024) Albasrawi, Roaa; Ilyas, Muhammad
    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.