A review of shockable arrhythmia detection of ECG signals using machine and deep learning techniques

• Autorzy: Kavya, Lakkakula , Karuna, Yepuganti , Saritha, Saladi , Prakash, Allam Jaya , Patro, Kiran Kumar , Sahoo, Suraj Prakash , Tadeusiewicz, Ryszard , Pławiak, Paweł
• Języki publikacji: EN

Abstrakty

EN

An electrocardiogram (ECG) is an essential medical tool for analyzing the functioning of the heart. An arrhythmia is a deviation in the shape of the ECG signal from the normal sinus rhythm. Long-term arrhythmias are the primary sources of cardiac disorders. Shockable arrhythmias, a type of life-threatening arrhythmia in cardiac patients, are characterized by disorganized or chaotic electrical activity in the heart’s lower chambers (ventricles), disrupting blood flow throughout the body. This condition may lead to sudden cardiac arrest in most patients. Therefore, detecting and classifying shockable arrhythmias is crucial for prompt defibrillation. In this work, various machine and deep learning algorithms from the literature are analyzed and summarized, which is helpful in automatic classification of shockable arrhythmias. Additionally, the advantages of these methods are compared with existing traditional unsupervised methods. The importance of digital signal processing techniques based on feature extraction, feature selection, and optimization is also discussed at various stages. Finally, available databases, the performance of automated algorithms, limitations, and the scope for future research are analyzed. This review encourages researchers’ interest in this challenging topic and provides a broad overview of its latest developments.

Słowa kluczowe

PL

uczenie głębokie; defibrylacja; elektrokardiogram; ekstrakcja cech; arytmia serca; migotanie komór serca; częstoskurcz komorowy

EN

deep learning; defibrillation; electrocardiogram; feature extraction; shockable arrhythmia; ventricular fibrillation; ventricular tachycardia

• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2024
• Tom: Vol. 34, no. 3
• Strony: 485--511
• Opis fizyczny: Bibliogr. 171 poz., rys., tab., wykr.

Twórcy

autor

Kavya, Lakkakula

• School of Electronics Engineering (SENSE), Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India

autor

Karuna, Yepuganti

• School of Electronics Engineering, VIT-AP University, Inavolu, Amaravati, Andhra Pradesh, 522241, India

autor

Saritha, Saladi

• School of Electronics Engineering, VIT-AP University, Inavolu, Amaravati, Andhra Pradesh, 522241, India

autor

Prakash, Allam Jaya

• School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India

autor

Patro, Kiran Kumar

• Department of Electronics and Communication Engineering, Aditya Institute of Technology and Management, Tekkali, Andhra Pradesh, 532201, India

autor

Sahoo, Suraj Prakash

• School of Electronics Engineering (SENSE), Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India

autor

Tadeusiewicz, Ryszard

• Department of Biocybernetics and Biomedical Engineering, AGH University of Krakow, Mickiewicza 30, 30-059 Kraków, Poland

autor

Pławiak, Paweł

• Department of Computer Science, Cracow University of Technology, Warszawska 24, 31-155 Kraków, Poland,
• Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100 Gliwice, Poland

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Identyfikatory

DOI

10.61822/amcs-2024-0034