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From ECG signals to images: a transformation based approach for deep learning
Provocative heart disease is related to ventricular arrhythmias (VA). Ventricular tachyarrhythmia is an irregular and fast heart rhythm that emerges from inappropriate electrical impulses in the ventricles of the heart. Different types of arrhythmias are associated with different patterns, which can...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7959637/ https://www.ncbi.nlm.nih.gov/pubmed/33817032 http://dx.doi.org/10.7717/peerj-cs.386 |
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author | Naz, Mahwish Shah, Jamal Hussain Khan, Muhammad Attique Sharif, Muhammad Raza, Mudassar Damaševičius, Robertas |
author_facet | Naz, Mahwish Shah, Jamal Hussain Khan, Muhammad Attique Sharif, Muhammad Raza, Mudassar Damaševičius, Robertas |
author_sort | Naz, Mahwish |
collection | PubMed |
description | Provocative heart disease is related to ventricular arrhythmias (VA). Ventricular tachyarrhythmia is an irregular and fast heart rhythm that emerges from inappropriate electrical impulses in the ventricles of the heart. Different types of arrhythmias are associated with different patterns, which can be identified. An electrocardiogram (ECG) is the major analytical tool used to interpret and record ECG signals. ECG signals are nonlinear and difficult to interpret and analyze. We propose a new deep learning approach for the detection of VA. Initially, the ECG signals are transformed into images that have not been done before. Later, these images are normalized and utilized to train the AlexNet, VGG-16 and Inception-v3 deep learning models. Transfer learning is performed to train a model and extract the deep features from different output layers. After that, the features are fused by a concatenation approach, and the best features are selected using a heuristic entropy calculation approach. Finally, supervised learning classifiers are utilized for final feature classification. The results are evaluated on the MIT-BIH dataset and achieved an accuracy of 97.6% (using Cubic Support Vector Machine as a final stage classifier). |
format | Online Article Text |
id | pubmed-7959637 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-79596372021-04-02 From ECG signals to images: a transformation based approach for deep learning Naz, Mahwish Shah, Jamal Hussain Khan, Muhammad Attique Sharif, Muhammad Raza, Mudassar Damaševičius, Robertas PeerJ Comput Sci Artificial Intelligence Provocative heart disease is related to ventricular arrhythmias (VA). Ventricular tachyarrhythmia is an irregular and fast heart rhythm that emerges from inappropriate electrical impulses in the ventricles of the heart. Different types of arrhythmias are associated with different patterns, which can be identified. An electrocardiogram (ECG) is the major analytical tool used to interpret and record ECG signals. ECG signals are nonlinear and difficult to interpret and analyze. We propose a new deep learning approach for the detection of VA. Initially, the ECG signals are transformed into images that have not been done before. Later, these images are normalized and utilized to train the AlexNet, VGG-16 and Inception-v3 deep learning models. Transfer learning is performed to train a model and extract the deep features from different output layers. After that, the features are fused by a concatenation approach, and the best features are selected using a heuristic entropy calculation approach. Finally, supervised learning classifiers are utilized for final feature classification. The results are evaluated on the MIT-BIH dataset and achieved an accuracy of 97.6% (using Cubic Support Vector Machine as a final stage classifier). PeerJ Inc. 2021-02-10 /pmc/articles/PMC7959637/ /pubmed/33817032 http://dx.doi.org/10.7717/peerj-cs.386 Text en © 2021 Naz et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Artificial Intelligence Naz, Mahwish Shah, Jamal Hussain Khan, Muhammad Attique Sharif, Muhammad Raza, Mudassar Damaševičius, Robertas From ECG signals to images: a transformation based approach for deep learning |
title | From ECG signals to images: a transformation based approach for deep learning |
title_full | From ECG signals to images: a transformation based approach for deep learning |
title_fullStr | From ECG signals to images: a transformation based approach for deep learning |
title_full_unstemmed | From ECG signals to images: a transformation based approach for deep learning |
title_short | From ECG signals to images: a transformation based approach for deep learning |
title_sort | from ecg signals to images: a transformation based approach for deep learning |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7959637/ https://www.ncbi.nlm.nih.gov/pubmed/33817032 http://dx.doi.org/10.7717/peerj-cs.386 |
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