An Advanced Arrhythmia Recognition Methodology Based on R-waves Time-Series Derivatives and Benchmarking Machine-Learning Algorithms

Trardi, Youssef; Ananou, Bouchra; Ouladsine, Mustapha

doi:10.23919/ecc51009.2020.9143678

Cited by 3 publications

(1 citation statement)

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“…Other works were mainly based on the extraction of different features from R-wave derivatives for automatic medical decision-making, especially for AF detection as in literature. 4 - 6 In addition, the use of univariate and multivariate methods plays a major role in the analysis of the ECG time series.…”

Section: Introductionmentioning

confidence: 99%

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

Boulif

Ananou

Ouladsine

et al. 2023

Bioinform Biol Insights

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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.

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Section: Introductionmentioning

confidence: 99%