Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data
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 scree...
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| Format: | Article |
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MDPI AG
2022-07-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/15/7/231 |
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| author | Zouhair Haddi Bouchra Ananou Miquel Alfaras Mustapha Ouladsine Jean-Claude Deharo Narcís Avellana Stéphane Delliaux |
| author_facet | Zouhair Haddi Bouchra Ananou Miquel Alfaras Mustapha Ouladsine Jean-Claude Deharo Narcís Avellana Stéphane Delliaux |
| author_sort | Zouhair Haddi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T10:24:04Z |
| format | Article |
| id | doaj.art-5aab335069004538b0a382505c03f981 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-09T10:24:04Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-5aab335069004538b0a382505c03f9812023-12-01T21:46:42ZengMDPI AGAlgorithms1999-48932022-07-0115723110.3390/a15070231Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate DataZouhair Haddi0Bouchra Ananou1Miquel Alfaras2Mustapha Ouladsine3Jean-Claude Deharo4Narcís Avellana5Stéphane Delliaux6NVISION Systems and Technologies SL, 08028 Barcelona, SpainLIS, CNRS, Aix Marseille University, University of Toulon, 13007 Marseille, FranceNVISION Systems and Technologies SL, 08028 Barcelona, SpainLIS, CNRS, Aix Marseille University, University of Toulon, 13007 Marseille, FranceAssistance Publique—Hôpitaux de Marseille, Centre Hospitalier Universitaire La Timone, Service de Cardiologie, 13005 Marseille, FranceNVISION Systems and Technologies SL, 08028 Barcelona, SpainFaculty of Medicine, Aix Marseille University, APHM, INSERM, INRAE, C2VN, Hôpital Nord, Explorations Fonctionnelles Respiratoires et à l’Exercice, 13007 Marseille, FranceAtrial 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.https://www.mdpi.com/1999-4893/15/7/231atrial fibrillationarrhythmiaRR time seriesdiagnosis-based-dataunivariate analysismultivariate analysis |
| spellingShingle | Zouhair Haddi Bouchra Ananou Miquel Alfaras Mustapha Ouladsine Jean-Claude Deharo Narcís Avellana Stéphane Delliaux Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data Algorithms atrial fibrillation arrhythmia RR time series diagnosis-based-data univariate analysis multivariate analysis |
| title | Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data |
| title_full | Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data |
| title_fullStr | Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data |
| title_full_unstemmed | Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data |
| title_short | Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data |
| title_sort | automatic atrial fibrillation arrhythmia detection using univariate and multivariate data |
| topic | atrial fibrillation arrhythmia RR time series diagnosis-based-data univariate analysis multivariate analysis |
| url | https://www.mdpi.com/1999-4893/15/7/231 |
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