Automated detection of shockable ECG signals: A review
| Autor: | Amira Abdelatey, Ahmed A. Abd El-Latif, Nizal Sarrafzadegan, Joanna Pławiak, Paweł Pławiak, Mariam Zomorodi-Moghadam, Saeid Nahavandi, Ru San Tan, Mohamed Hammad, Ryszard Tadeusiewicz, Abbas Khosravi, Vladimir Makarenkov, Moloud Abdar, U. Rajendra Acharya, Rajesh N.V.P.S. Kandala |
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| Rok vydání: | 2021 |
| Předmět: |
Tachycardia
Information Systems and Management Speech recognition Diagnostic accuracy 02 engineering and technology Theoretical Computer Science Sudden cardiac death Artificial Intelligence 0202 electrical engineering electronic engineering information engineering medicine cardiovascular diseases Normal Sinus Rhythm business.industry Deep learning 05 social sciences 050301 education Electrical shock medicine.disease Computer Science Applications Control and Systems Engineering Ventricular fibrillation 020201 artificial intelligence & image processing Artificial intelligence medicine.symptom Ecg signal business 0503 education Software |
| Zdroj: | Information Sciences. 571:580-604 |
| ISSN: | 0020-0255 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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