Stacked Variational Autoencoder in the Classification of Cardiac Arrhythmia using ECG Signals with 2D-ECG Images

Cardiac Arrhythmia is an endangered signal to human life. Most arrhythmias are shortfalls of symptoms. Electrocardiogram (ECG) is a non-invasive, low-priced, and powerful tool to record the electrical signals of the heart and detect severe cardiovascular diseases. ECG interpretation is generally don...

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Published in:2022 International Conference on Intelligent Innovations in Engineering and Technology (ICIIET) pp. 222 - 226
Main Authors: Nithya, S, Rani, M. Mary Shanthi
Format: Conference Proceeding
Language:English
Published: IEEE 22.09.2022
Subjects:
2DECG
Arrhythmia
Deep learning
ECG
Electrocardiography
Heart beat
Image resolution
Sensitivity
Stacked Variational Autoencoder
Technological innovation
Training
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Abstract Cardiac Arrhythmia is an endangered signal to human life. Most arrhythmias are shortfalls of symptoms. Electrocardiogram (ECG) is a non-invasive, low-priced, and powerful tool to record the electrical signals of the heart and detect severe cardiovascular diseases. ECG interpretation is generally done by the cardiologist which is time-consuming and sometimes may lead to the wrong diagnosis. Moreover, numerous arrhythmia heartbeats remain unexplored. The arrhythmia record has an expeditious and divergent ECG. Prompt diagnosis of arrhythmia can reduce the mortality rate. In this paper, we have investigated the application of a Stacked Variational Autoencoder (SVAE) for the automatic diagnosis of arrhythmia from ECG signals. Furthermore, the augmented dat aset is used for training the model, to resolve the imbalance in the classes. The proposed model reached an overall accuracy of 98.96% and sensitivity of 97.32%. SVAE classified twelve classes of cardiac arrhythmia including normal sinus rhythm.
AbstractList Cardiac Arrhythmia is an endangered signal to human life. Most arrhythmias are shortfalls of symptoms. Electrocardiogram (ECG) is a non-invasive, low-priced, and powerful tool to record the electrical signals of the heart and detect severe cardiovascular diseases. ECG interpretation is generally done by the cardiologist which is time-consuming and sometimes may lead to the wrong diagnosis. Moreover, numerous arrhythmia heartbeats remain unexplored. The arrhythmia record has an expeditious and divergent ECG. Prompt diagnosis of arrhythmia can reduce the mortality rate. In this paper, we have investigated the application of a Stacked Variational Autoencoder (SVAE) for the automatic diagnosis of arrhythmia from ECG signals. Furthermore, the augmented dat aset is used for training the model, to resolve the imbalance in the classes. The proposed model reached an overall accuracy of 98.96% and sensitivity of 97.32%. SVAE classified twelve classes of cardiac arrhythmia including normal sinus rhythm.
Author Nithya, S
Rani, M. Mary Shanthi
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  givenname: M. Mary Shanthi
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  fullname: Rani, M. Mary Shanthi
  email: drmaryshanthi@gmail.com
  organization: The Gandhigram Rural Institute,Department of Computer Science and Applications,Dindugal,India
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Snippet Cardiac Arrhythmia is an endangered signal to human life. Most arrhythmias are shortfalls of symptoms. Electrocardiogram (ECG) is a non-invasive, low-priced,...
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StartPage 222
SubjectTerms 2DECG
Arrhythmia
Deep learning
ECG
Electrocardiography
Heart beat
Image resolution
Sensitivity
Stacked Variational Autoencoder
Technological innovation
Training
Title Stacked Variational Autoencoder in the Classification of Cardiac Arrhythmia using ECG Signals with 2D-ECG Images
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