A Literature Review: ECG-Based Models for Arrhythmia Diagnosis Using Artificial Intelligence Techniques

Autor: Abir Boulif, Bouchra Ananou, Mustapha Ouladsine, Stéphane Delliaux
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Bioinformatics and Biology Insights, Vol 17 (2023)
Druh dokumentu: article
ISSN: 1177-9322
11779322
DOI: 10.1177/11779322221149600
Popis: In the health care and medical domain, it has been proven challenging to diagnose correctly many diseases with complicated and interferential symptoms, including arrhythmia. However, with the evolution of artificial intelligence (AI) techniques, the diagnosis and prognosis of arrhythmia became easier for the physicians and practitioners using only an electrocardiogram (ECG) examination. This review presents a synthesis of the studies conducted in the last 12 years to predict arrhythmia’s occurrence by classifying automatically different heartbeat rhythms. From a variety of research academic databases, 40 studies were selected to analyze, among which 29 of them applied deep learning methods (72.5%), 9 of them addressed the problem with machine learning methods (22.5%), and 2 of them combined both deep learning and machine learning to predict arrhythmia (5%). Indeed, the use of AI for arrhythmia diagnosis is emerging in literature, although there are some challenging issues, such as the explicability of the Deep Learning methods and the computational resources needed to achieve high performance. However, with the continuous development of cloud platforms and quantum calculation for AI, we can achieve a breakthrough in arrhythmia diagnosis.
Databáze: Directory of Open Access Journals
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