Arrhythmia Classification Techniques Using Deep Neural Network
Electrocardiogram (ECG) is the most common and low-cost diagnostic tool used in healthcare institutes for screening heart electrical signals. The abnormal heart signals are commonly known as arrhythmia. Cardiac arrhythmia can be dangerous, or in most cases, it can cause death. The arrhythmia can be...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/9919588 |
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| author | Ali Haider Khan Muzammil Hussain Muhammad Kamran Malik |
| author_facet | Ali Haider Khan Muzammil Hussain Muhammad Kamran Malik |
| author_sort | Ali Haider Khan |
| collection | DOAJ |
| description | Electrocardiogram (ECG) is the most common and low-cost diagnostic tool used in healthcare institutes for screening heart electrical signals. The abnormal heart signals are commonly known as arrhythmia. Cardiac arrhythmia can be dangerous, or in most cases, it can cause death. The arrhythmia can be of different types, and it can be detected by an ECG test. The automated screening of arrhythmia classification using ECG beats is developed for ages. The automated systems that can be adapted as a tool for screening arrhythmia classification play a vital role not only for the patients but can also assist the doctors. The deep learning-based automated arrhythmia classification techniques are developed with high accuracy results but still not adopted by healthcare professionals as the generalized approach. The primary concerns that affect the success of the developed arrhythmia detection systems are (i) manual features selection, (ii) techniques used for features extraction, and (iii) algorithm used for classification and the most important is the use of imbalanced data for classification. This study focuses on the recent trends in arrhythmia classification techniques, and through extensive simulations, the performance of the various arrhythmia classification and detection models has been evaluated. Finally, the study presents insights into arrhythmia classification techniques to overcome the limitation in the existing methodologies. |
| format | Article |
| id | doaj-art-27d0f5cd008c467b8c80e92ea6d300bb |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-27d0f5cd008c467b8c80e92ea6d300bb2025-02-03T06:07:37ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/99195889919588Arrhythmia Classification Techniques Using Deep Neural NetworkAli Haider Khan0Muzammil Hussain1Muhammad Kamran Malik2Department of Computer Science, School of Systems & Technology, University of Management and Technology, Lahore 54000, PakistanDepartment of Computer Science, School of Systems & Technology, University of Management and Technology, Lahore 54000, PakistanPunjab University College of Information Technology, University of the Punjab, Lahore 54000, PakistanElectrocardiogram (ECG) is the most common and low-cost diagnostic tool used in healthcare institutes for screening heart electrical signals. The abnormal heart signals are commonly known as arrhythmia. Cardiac arrhythmia can be dangerous, or in most cases, it can cause death. The arrhythmia can be of different types, and it can be detected by an ECG test. The automated screening of arrhythmia classification using ECG beats is developed for ages. The automated systems that can be adapted as a tool for screening arrhythmia classification play a vital role not only for the patients but can also assist the doctors. The deep learning-based automated arrhythmia classification techniques are developed with high accuracy results but still not adopted by healthcare professionals as the generalized approach. The primary concerns that affect the success of the developed arrhythmia detection systems are (i) manual features selection, (ii) techniques used for features extraction, and (iii) algorithm used for classification and the most important is the use of imbalanced data for classification. This study focuses on the recent trends in arrhythmia classification techniques, and through extensive simulations, the performance of the various arrhythmia classification and detection models has been evaluated. Finally, the study presents insights into arrhythmia classification techniques to overcome the limitation in the existing methodologies.http://dx.doi.org/10.1155/2021/9919588 |
| spellingShingle | Ali Haider Khan Muzammil Hussain Muhammad Kamran Malik Arrhythmia Classification Techniques Using Deep Neural Network Complexity |
| title | Arrhythmia Classification Techniques Using Deep Neural Network |
| title_full | Arrhythmia Classification Techniques Using Deep Neural Network |
| title_fullStr | Arrhythmia Classification Techniques Using Deep Neural Network |
| title_full_unstemmed | Arrhythmia Classification Techniques Using Deep Neural Network |
| title_short | Arrhythmia Classification Techniques Using Deep Neural Network |
| title_sort | arrhythmia classification techniques using deep neural network |
| url | http://dx.doi.org/10.1155/2021/9919588 |
| work_keys_str_mv | AT alihaiderkhan arrhythmiaclassificationtechniquesusingdeepneuralnetwork AT muzammilhussain arrhythmiaclassificationtechniquesusingdeepneuralnetwork AT muhammadkamranmalik arrhythmiaclassificationtechniquesusingdeepneuralnetwork |