Szczegóły publikacji
Opis bibliograficzny
A review of shockable arrhythmia detection of ECG signals using machine and deep learning techniques / Lakkakula Kavya, Yepuganti Karuna, Saladi Saritha, Allam Jaya Prakash, Kiran Kumar Patro, Suraj Prakash Sahoo, Ryszard TADEUSIEWICZ, Paweł Pławiak // International Journal of Applied Mathematics and Computer Science ; ISSN 1641-876X. — 2024 — vol. 34 no. 3, s. 485–511. — Bibliogr. s. 503–509, Abstr.
Autorzy (8)
- Kavya Lakkakula
- Karuna Yepuganti
- Saritha Saladi
- Prakash Allam Jaya
- Patro Kiran Kumar
- Sahoo Suraj Prakash
- AGHTadeusiewicz Ryszard
- Pławiak Paweł
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 155480 |
|---|---|
| Data dodania do BaDAP | 2024-09-23 |
| Tekst źródłowy | URL |
| DOI | 10.61822/amcs-2024-0034 |
| Rok publikacji | 2024 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | International Journal of Applied Mathematics and Computer Science |
Abstract
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.
