Automated detection of shockable ECG signals: A review
Article
Hammad, Mohamed, Rajesh, Kandala N.V.P.S, Abdelatey, Amira, Abdar, Moloud, Zomorodi-Moghadam, Mariam, Tan, Ru-San, Acharya, U. Rajendra, Plawiak, Joanna, Tadeusiewicz, Ryszard, Makarenkov, Vladimir, Sarrafzadegan, Nizal, Khosravi, Abbas, Nahavandi, Saeid, Abd El-Latif, Ahmed A. and Plawiak, Pawel. 2021. "Automated detection of shockable ECG signals: A review." Information Sciences. 571, pp. 580-604. https://doi.org/10.1016/j.ins.2021.05.035
| Article Title | Automated detection of shockable ECG signals: A review |
|---|---|
| ERA Journal ID | 17908 |
| Article Category | Article |
| Authors | Hammad, Mohamed, Rajesh, Kandala N.V.P.S, Abdelatey, Amira, Abdar, Moloud, Zomorodi-Moghadam, Mariam, Tan, Ru-San, Acharya, U. Rajendra, Plawiak, Joanna, Tadeusiewicz, Ryszard, Makarenkov, Vladimir, Sarrafzadegan, Nizal, Khosravi, Abbas, Nahavandi, Saeid, Abd El-Latif, Ahmed A. and Plawiak, Pawel |
| Journal Title | Information Sciences |
| Journal Citation | 571, pp. 580-604 |
| Number of Pages | 25 |
| Year | 2021 |
| Publisher | Elsevier |
| Place of Publication | United Kingdom |
| ISSN | 0020-0255 |
| 1872-6291 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.ins.2021.05.035 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0020025521004953 |
| 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. |
| Keywords | Arrhythmia; Electrocardiogram (ECG); Computer-aided arrhythmia classification (CAAC); Signal processing; Machine learning; Deep learning; Ensemble learning; Feature extraction; Feature selection; Optimization; Evolutionary computation |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Menoufia University, Egypt |
| GayatriVidyaParishad College of Engineering, India | |
| Deakin University | |
| Ferdowsi University of Mashhad, Iran | |
| Cracow University of Technology, Poland | |
| National Heart Centre, Singapore | |
| Duke-NUS Medical School, Singapore | |
| Ngee Ann Polytechnic, Singapore | |
| Singapore University of Social Sciences (SUSS), Singapore | |
| Asia University, Taiwan | |
| AGH University of Science and Technology, Poland | |
| University of Quebec, Canada | |
| Isfahan University of Medical Sciences, Iran | |
| University of British Columbia, Canada | |
| Harbin Institute of Technology, China | |
| Nile University, Egypt | |
| Polish Academy of Sciences, Poland |
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https://research.usq.edu.au/item/z1v98/automated-detection-of-shockable-ecg-signals-a-review
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