Szczegóły publikacji
Opis bibliograficzny
Automated detection of shockable ECG signals: a review / Mohamed Hammad, Rajesh N. V. P. S. Kandala, Amira Abdelatey, Moloud Abdar, Mariam Zomorodi-Moghadam, Ru San Tan, U. Rajendra Acharya, Joanna Pławiak, Ryszard TADEUSIEWICZ, Vladimir Makarenkov, Nizal Sarrafzadegan, Abbas Khosravi, Saeid Nahavandi, Ahmed A. Abd EL-Latif, Paweł Pławiak // Information Sciences ; ISSN 0020-0255. — 2021 — vol. 571, s. 580–604. — Bibliogr. s. 601–604, Abstr. — Publikacja dostępna online od: 2021-05-21
Autorzy (15)
- Hammad Mohamed
- Kandala Rajesh N.V.P.S.
- Abdelatey Amira
- Abdar Moloud
- Zomorodi‐Moghadam Mariam
- Tan Ru-San
- Acharya U. Rajendra
- Pławiak Joanna
- AGHTadeusiewicz Ryszard
- Makarenkov Vladimir
- Sarrafzadegan Nizal
- Khosravi Abbas
- Nahavandi Saeid
- Abd El-Latif Ahmed A.
- Pławiak Paweł
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 135680 |
|---|---|
| Data dodania do BaDAP | 2021-08-30 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.ins.2021.05.035 |
| Rok publikacji | 2021 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Information Sciences |
Abstract
Sudden cardiac death from lethal arrhythmia is a preventable cause of death. Ventricular fibrillation and tachycardia are shockable electrocardiographic (ECG)rhythms that can respond to emergency electrical shock therapy and revert to normal sinus rhythm if diagnosed early upon cardiac arrest with the restoration of adequate cardiac pump function. However, manual inspection of ECG signals is a difficult task in the acute setting. Thus, computer-aided arrhythmia classification (CAAC) systems have been developed to detect shockable ECG rhythm. Traditional machine learning and deep learning methods are now progressively employed to enhance the diagnostic accuracy of CAAC systems. This paper reviews the state-of-the-art machine and deep learning based CAAC expert systems for shockable ECG signal recognition, discussing their strengths, advantages, and drawbacks. Moreover, unique bispectrum and recurrence plots are proposed to represent shockable and non-shockable ECG signals. Deep learning methods are usually more robust and accurate than standard machine learning methods but require big data of good quality for training. We recommend collecting large accessible ECG datasets with a meaningful proportion of abnormal cases for research and development of superior CAAC systems. (c) 2021 Elsevier Inc. All rights reserved.
