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A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal
Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7867037/ https://www.ncbi.nlm.nih.gov/pubmed/33535397 http://dx.doi.org/10.3390/s21030951 |
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author | Ullah, Amin Rehman, Sadaqat ur Tu, Shanshan Mehmood, Raja Majid Fawad, Ehatisham-ul-haq, Muhammad |
author_facet | Ullah, Amin Rehman, Sadaqat ur Tu, Shanshan Mehmood, Raja Majid Fawad, Ehatisham-ul-haq, Muhammad |
author_sort | Ullah, Amin |
collection | PubMed |
description | Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model’s classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models’ effectiveness. |
format | Online Article Text |
id | pubmed-7867037 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-78670372021-02-07 A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal Ullah, Amin Rehman, Sadaqat ur Tu, Shanshan Mehmood, Raja Majid Fawad, Ehatisham-ul-haq, Muhammad Sensors (Basel) Article Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model’s classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models’ effectiveness. MDPI 2021-02-01 /pmc/articles/PMC7867037/ /pubmed/33535397 http://dx.doi.org/10.3390/s21030951 Text en © 2021 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ullah, Amin Rehman, Sadaqat ur Tu, Shanshan Mehmood, Raja Majid Fawad, Ehatisham-ul-haq, Muhammad A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal |
title | A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal |
title_full | A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal |
title_fullStr | A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal |
title_full_unstemmed | A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal |
title_short | A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal |
title_sort | hybrid deep cnn model for abnormal arrhythmia detection based on cardiac ecg signal |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7867037/ https://www.ncbi.nlm.nih.gov/pubmed/33535397 http://dx.doi.org/10.3390/s21030951 |
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