Heart Arrhythmia Classification Using Deep Learning: A Comparative Study

Heart arrhythmia is an irregular heartbeat that causes heart problems. It can be classified by their seriousness into serious and non-serious arrhythmia. Mainly to diagnose heart arrhythmias, we use Electrocardiogram (ECG). In this paper, the authors compared three different models of classifiers: C...

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Bibliographic Details
Main Authors: Radi, Omar, Alslatie, Mohammad, Mustafa, Wan Azani, Alquran, Hiam, Badarneh, Alaa, Mohammed, F. F., Alkhayyat, Ahmed
Format: Conference Proceeding
Language:English
Subjects:
Arrhythmia
arrhythmia classification
CNN
Deep learning
DNN
Heart
heart arrhythmia
Heart beat
LSTM
Medical services
Neural Network
Neural networks
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Summary:Heart arrhythmia is an irregular heartbeat that causes heart problems. It can be classified by their seriousness into serious and non-serious arrhythmia. Mainly to diagnose heart arrhythmias, we use Electrocardiogram (ECG). In this paper, the authors compared three different models of classifiers: Convolutional Neural Network, Dense Neural Network and Long Short-Term Memory to classify cardiac arrhythmia into two types normal and abnormal, using the MIT-BIH database. The results show that CNN and DNN have the best result of the models with 99% accuracy while LSTM shows 60 accuracy percent.
ISSN:2831-753X
DOI:10.1109/IICETA57613.2023.10351336