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Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data

Abstract : Atrial fibrillation (AF) is still a major cause of disease morbidity and mortality, making its early diagnosis desirable and urging researchers to develop efficient methods devoted to automatic AF detection. Till now, the analysis of Holter-ECG recordings remains the gold-standard technique to screen AF. This is usually achieved by studying either RR interval time series analysis, P-wave detection or combinations of both morphological characteristics. After extraction and selection of meaningful features, each of the AF detection methods might be conducted through univariate and multivariate data analysis. Many of these automatic techniques have been proposed over the last years. This work presents an overview of research studies of AF detection based on RR interval time series. The aim of this paper is to provide the scientific community and newcomers to the field of AF screening with a resource that presents introductory concepts, clinical features, and a literature review that describes the techniques that are mostly followed when RR interval time series are used for accurate detection of AF.
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https://hal-amu.archives-ouvertes.fr/hal-03800773
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Submitted on : Thursday, October 6, 2022 - 3:46:09 PM
Last modification on : Friday, October 7, 2022 - 4:43:19 AM

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Zouhair Haddi, Bouchra Ananou, Miquel Alfaras, Mustapha Ouladsine, Jean-Claude Deharo, et al.. Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data. Algorithms, 2022, 15 (7), pp.231. ⟨10.3390/a15070231⟩. ⟨hal-03800773⟩

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