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Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique

Cardiac arrhythmias pose a significant danger to human life; therefore, it is of utmost importance to be able to efficiently diagnose these arrhythmias promptly. There exist many techniques for the detection of arrhythmias; however, the most widely adopted method is the use of an Electrocardiogram (...

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Autores principales: Irfan, Saad, Anjum, Nadeem, Althobaiti, Turke, Alotaibi, Abdullah Alhumaidi, Siddiqui, Abdul Basit, Ramzan, Naeem
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9370835/
https://www.ncbi.nlm.nih.gov/pubmed/35957162
http://dx.doi.org/10.3390/s22155606
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author Irfan, Saad
Anjum, Nadeem
Althobaiti, Turke
Alotaibi, Abdullah Alhumaidi
Siddiqui, Abdul Basit
Ramzan, Naeem
author_facet Irfan, Saad
Anjum, Nadeem
Althobaiti, Turke
Alotaibi, Abdullah Alhumaidi
Siddiqui, Abdul Basit
Ramzan, Naeem
author_sort Irfan, Saad
collection PubMed
description Cardiac arrhythmias pose a significant danger to human life; therefore, it is of utmost importance to be able to efficiently diagnose these arrhythmias promptly. There exist many techniques for the detection of arrhythmias; however, the most widely adopted method is the use of an Electrocardiogram (ECG). The manual analysis of ECGs by medical experts is often inefficient. Therefore, the detection and recognition of ECG characteristics via machine-learning techniques have become prevalent. There are two major drawbacks of existing machine-learning approaches: (a) they require extensive training time; and (b) they require manual feature selection. To address these issues, this paper presents a novel deep-learning framework that integrates various networks by stacking similar layers in each network to produce a single robust model. The proposed framework has been tested on two publicly available datasets for the recognition of five micro-classes of arrhythmias. The overall classification sensitivity, specificity, positive predictive value, and accuracy of the proposed approach are 98.37%, 99.59%, 98.41%, and 99.35%, respectively. The results are compared with state-of-the-art approaches. The proposed approach outperformed the existing approaches in terms of sensitivity, specificity, positive predictive value, accuracy and computational cost.
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spelling pubmed-93708352022-08-12 Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique Irfan, Saad Anjum, Nadeem Althobaiti, Turke Alotaibi, Abdullah Alhumaidi Siddiqui, Abdul Basit Ramzan, Naeem Sensors (Basel) Article Cardiac arrhythmias pose a significant danger to human life; therefore, it is of utmost importance to be able to efficiently diagnose these arrhythmias promptly. There exist many techniques for the detection of arrhythmias; however, the most widely adopted method is the use of an Electrocardiogram (ECG). The manual analysis of ECGs by medical experts is often inefficient. Therefore, the detection and recognition of ECG characteristics via machine-learning techniques have become prevalent. There are two major drawbacks of existing machine-learning approaches: (a) they require extensive training time; and (b) they require manual feature selection. To address these issues, this paper presents a novel deep-learning framework that integrates various networks by stacking similar layers in each network to produce a single robust model. The proposed framework has been tested on two publicly available datasets for the recognition of five micro-classes of arrhythmias. The overall classification sensitivity, specificity, positive predictive value, and accuracy of the proposed approach are 98.37%, 99.59%, 98.41%, and 99.35%, respectively. The results are compared with state-of-the-art approaches. The proposed approach outperformed the existing approaches in terms of sensitivity, specificity, positive predictive value, accuracy and computational cost. MDPI 2022-07-27 /pmc/articles/PMC9370835/ /pubmed/35957162 http://dx.doi.org/10.3390/s22155606 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
Irfan, Saad
Anjum, Nadeem
Althobaiti, Turke
Alotaibi, Abdullah Alhumaidi
Siddiqui, Abdul Basit
Ramzan, Naeem
Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique
title Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique
title_full Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique
title_fullStr Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique
title_full_unstemmed Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique
title_short Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique
title_sort heartbeat classification and arrhythmia detection using a multi-model deep-learning technique
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9370835/
https://www.ncbi.nlm.nih.gov/pubmed/35957162
http://dx.doi.org/10.3390/s22155606
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