Key result
Deep learning techniques, specifically Convolutional Neural Networks, are increasingly applied to ECG data for the classification of heart rhythms and precise detection of myocardial infarction.
Why the study?
Deep learning has become a focus for healthcare applications where early detection via ECG plays a vital role in diagnosing pathological conditions.
Deep learning, particularly Convolutional Neural Networks, shows potential in analyzing ECGs for the early detection and classification of arrhythmias and myocardial infarction.
DL techniques advance ECG rhythm classification in reviews; leaves open prospective validation before clinical adoption.
Recently, DL (Deep Learning) becomes the focus study for researchers in wide and various applications such as; healthcare, where early detection can play an important and vital role in diagnosing abnormal (pathological) conditions through an electrocardiogram (ECG). In the current study, an extensive presentation was given on the modern techniques that have been applied in the ECG device, which have been introduced to classify heart rhythms and identify disturbances in it precisely in the infraction of the myocardial. To enter the method that defines the biological systems of vision, studies have been studied and reviewed that specifically describe the Convolutional Neural Network (CNN). Also, researches and studies related to the subject have been summarized from several aspects, the most important of which are according to the sequence: collecting data and application areas, in addition to planning the form and content of the model and the type of data that is entered, and then evaluating the performance.
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Rawi et al. (2022) conducted a review in ECG Arrhythmia and Myocardial Infarction. Deep Learning (Convolutional Neural Network) was evaluated. Deep learning techniques, specifically Convolutional Neural Networks, are increasingly applied to ECG data for the classification of heart rhythms and precise detection of myocardial infarction.
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