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A Novel Deep-Learning-Based Framework for the Classification of Cardiac Arrhythmia
Cardiovascular diseases (CVDs) are the primary cause of death. Every year, many people die due to heart attacks. The electrocardiogram (ECG) signal plays a vital role in diagnosing CVDs. ECG signals provide us with information about the heartbeat. ECGs can detect cardiac arrhythmia. In this article,...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8949672/ https://www.ncbi.nlm.nih.gov/pubmed/35324625 http://dx.doi.org/10.3390/jimaging8030070 |
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author | Jamil, Sonain Rahman, MuhibUr |
author_facet | Jamil, Sonain Rahman, MuhibUr |
author_sort | Jamil, Sonain |
collection | PubMed |
description | Cardiovascular diseases (CVDs) are the primary cause of death. Every year, many people die due to heart attacks. The electrocardiogram (ECG) signal plays a vital role in diagnosing CVDs. ECG signals provide us with information about the heartbeat. ECGs can detect cardiac arrhythmia. In this article, a novel deep-learning-based approach is proposed to classify ECG signals as normal and into sixteen arrhythmia classes. The ECG signal is preprocessed and converted into a 2D signal using continuous wavelet transform (CWT). The time–frequency domain representation of the CWT is given to the deep convolutional neural network (D-CNN) with an attention block to extract the spatial features vector (SFV). The attention block is proposed to capture global features. For dimensionality reduction in SFV, a novel clump of features (CoF) framework is proposed. The k-fold cross-validation is applied to obtain the reduced feature vector (RFV), and the RFV is given to the classifier to classify the arrhythmia class. The proposed framework achieves 99.84% accuracy with 100% sensitivity and 99.6% specificity. The proposed algorithm outperforms the state-of-the-art accuracy, F1-score, and sensitivity techniques. |
format | Online Article Text |
id | pubmed-8949672 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89496722022-03-26 A Novel Deep-Learning-Based Framework for the Classification of Cardiac Arrhythmia Jamil, Sonain Rahman, MuhibUr J Imaging Article Cardiovascular diseases (CVDs) are the primary cause of death. Every year, many people die due to heart attacks. The electrocardiogram (ECG) signal plays a vital role in diagnosing CVDs. ECG signals provide us with information about the heartbeat. ECGs can detect cardiac arrhythmia. In this article, a novel deep-learning-based approach is proposed to classify ECG signals as normal and into sixteen arrhythmia classes. The ECG signal is preprocessed and converted into a 2D signal using continuous wavelet transform (CWT). The time–frequency domain representation of the CWT is given to the deep convolutional neural network (D-CNN) with an attention block to extract the spatial features vector (SFV). The attention block is proposed to capture global features. For dimensionality reduction in SFV, a novel clump of features (CoF) framework is proposed. The k-fold cross-validation is applied to obtain the reduced feature vector (RFV), and the RFV is given to the classifier to classify the arrhythmia class. The proposed framework achieves 99.84% accuracy with 100% sensitivity and 99.6% specificity. The proposed algorithm outperforms the state-of-the-art accuracy, F1-score, and sensitivity techniques. MDPI 2022-03-10 /pmc/articles/PMC8949672/ /pubmed/35324625 http://dx.doi.org/10.3390/jimaging8030070 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Jamil, Sonain Rahman, MuhibUr A Novel Deep-Learning-Based Framework for the Classification of Cardiac Arrhythmia |
title | A Novel Deep-Learning-Based Framework for the Classification of Cardiac Arrhythmia |
title_full | A Novel Deep-Learning-Based Framework for the Classification of Cardiac Arrhythmia |
title_fullStr | A Novel Deep-Learning-Based Framework for the Classification of Cardiac Arrhythmia |
title_full_unstemmed | A Novel Deep-Learning-Based Framework for the Classification of Cardiac Arrhythmia |
title_short | A Novel Deep-Learning-Based Framework for the Classification of Cardiac Arrhythmia |
title_sort | novel deep-learning-based framework for the classification of cardiac arrhythmia |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8949672/ https://www.ncbi.nlm.nih.gov/pubmed/35324625 http://dx.doi.org/10.3390/jimaging8030070 |
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