Digital Library of Zielona Góra contains 9 272 digital objects
Object
Title: A review of shockable arrhythmia detection of ECG signals using machine and deep learning techniques
Creator:
Kavya, Lakkakula ; Karuna, Yepuganti ; Saritha, Saladi ; Prakash, Allam Jaya ; Patro, Kiran Kumar ; Sahoo, Suraj Prakash ; Tadeusiewicz, Ryszard (1947- ) ; Pławiak, Paweł
Date:
Resource Type:
Contributor:
Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.
Group publication title:
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.
Publisher:
Zielona Góra: Uniwersytet Zielonogórski
Resource Identifier:
DOI:
Pages:
Source:
AMCS, volume 34, number 3 (2024) ; click here to follow the link
Language:
License CC BY 4.0:
Rights:
Biblioteka Uniwersytetu Zielonogórskiego
Object collections:
- Repository > Faculties > Faculty of Computer, Electrical and Control Engineering
- Repository > Types of work > Articles
- Repository > Scientific journals and UZ publishing series > International Journal of Applied Mathematics and Computer Science (AMCS) > International Journal of Applied Mathematics and Computer Science (AMCS) (2024)
Last modified:
Aug 5, 2025
In our library since:
Aug 5, 2025
Number of object content hits:
14
All available object's versions:
https://zbc.uz.zgora.pl/repozytorium/publication/101877
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 34, number 1 (2024)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 34, number 2 (2024)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 34, number 3 (2024)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 34, number 4 (2024)
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| Edition name | Date |
|---|---|
| A review of shockable arrhythmia detection of ECG signals using machine and deep learning techniques | Aug 5, 2025 |
Objects Similar
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Yilmaz, Mustafa Berkay Öztürk, Kagan Korbicz, Józef (1951- ) - red. Uciński, Dariusz - red.
