Classification and Detection of ECG Arrhythmia and Myocardial Infarction Using Deep Learning: A Review

Author:

Rawi Atiaf Ayal,Albashir Murtada Kalafalla,Ahmed Awadallah Mohammed

Abstract

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.

Publisher

NeuroQuantology Journal

Subject

Information Systems and Management,Library and Information Sciences,Human-Computer Interaction,Software

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An adaptive Marine Predator Optimization Algorithm (MPOA) integrated Gated Recurrent Neural Network (GRNN) classifier model for arrhythmia detection;Biomedical Signal Processing and Control;2024-08

2. Myocardial infarction detection method based on the continuous T-wave area feature and multi-lead-fusion deep features;Physiological Measurement;2024-05-01

3. Heart Disease Detection Using Deep Learning: A Survey;Lecture Notes in Networks and Systems;2024

4. Detection and classification of cardiac arrhythmia using artificial intelligence;International Journal of System Assurance Engineering and Management;2023-07-28

5. Classification of Arrhythmia ECG Signals using Convolutional Neural Network;2023 30th International Conference on Systems, Signals and Image Processing (IWSSIP);2023-06-27

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