Autor:

Kavya, Lakkakula ; Karuna, Yepuganti ; Saritha, Saladi ; Prakash, Allam Jaya ; Patro, Kiran Kumar ; Sahoo, Suraj Prakash ; Tadeusiewicz, Ryszard (1947- ) ; Pławiak, Paweł

Współtwórca:

Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.

Tytuł:

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

Tytuł publikacji grupowej:

AMCS, volume 34 (2024)

Temat i słowa kluczowe:

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

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.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Data wydania:

2024

Typ zasobu:

artykuł

DOI:

10.61822/amcs-2024-0034

Strony:

485-511

Źródło:

AMCS, volume 34, number 3 (2024) ; kliknij tutaj, żeby przejść

Jezyk:

eng

Licencja CC BY 4.0:

kliknij tutaj, żeby przejść

Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego