A Novel Approach to Detect Cardiac Arrhythmia Based on Continuous Wavelet Transform and Convolutional Neural Network
Electrocardiogram (ECG) signal is informative as well as non-invasive clinical tool to diagnose cardiac diseases of human heart. However, the diagnosis requires professionals’ clarification and is also time-consuming. To make the diagnosis proficient, a novel convolutional neural network (CNN) ha...
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Research and Development Wing, MIST
2023
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| Online-Zugang: | http://dspace.mist.ac.bd:8080/xmlui/handle/123456789/744 |
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| author | Mohonta, Shadhon Chandra Firoj Ali, Md. |
| author_browse | Firoj Ali, Md. Mohonta, Shadhon Chandra |
| author_facet | Mohonta, Shadhon Chandra Firoj Ali, Md. |
| author_sort | Mohonta, Shadhon Chandra |
| collection | DSpace |
| description | Electrocardiogram (ECG) signal is informative as well as non-invasive clinical
tool to diagnose cardiac diseases of human heart. However, the diagnosis
requires professionals’ clarification and is also time-consuming. To make the
diagnosis proficient, a novel convolutional neural network (CNN) has been
proposed for automatic arrhythmia detection. In this work, the ECG data
collected from the MIT-BIH database have been preprocessed, and segmented in
short ECG segments of 60 s. Then, all these segments have been transformed
into scalogram images obtained from time-frequency analysis using continuous
wavelet transform (CWT). Finally, these scalogram images have been exploited
as an input for our designed CNN classifier to classify cardiac arrhythmia. In this
approach, the overall accuracy, sensitivity, and specificity are 99.39%, 98.79%,
and 100% respectively. Proposed CNN model has significant advantages, and it
can be used to differentiate the healthy and arrhythmic patients effectively. |
| format | Article |
| id | oai:localhost:123456789-744 |
| institution | My University |
| language | English |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Research and Development Wing, MIST |
| publisherStr | Research and Development Wing, MIST |
| record_format | dspace |
| spelling | oai:localhost:123456789-7442023-01-22T06:00:06Z A Novel Approach to Detect Cardiac Arrhythmia Based on Continuous Wavelet Transform and Convolutional Neural Network Mohonta, Shadhon Chandra Firoj Ali, Md. Electrocardiogram, Continuous Wavelet Transform, Arrhythmia, Convolutional Neural Network Electrocardiogram (ECG) signal is informative as well as non-invasive clinical tool to diagnose cardiac diseases of human heart. However, the diagnosis requires professionals’ clarification and is also time-consuming. To make the diagnosis proficient, a novel convolutional neural network (CNN) has been proposed for automatic arrhythmia detection. In this work, the ECG data collected from the MIT-BIH database have been preprocessed, and segmented in short ECG segments of 60 s. Then, all these segments have been transformed into scalogram images obtained from time-frequency analysis using continuous wavelet transform (CWT). Finally, these scalogram images have been exploited as an input for our designed CNN classifier to classify cardiac arrhythmia. In this approach, the overall accuracy, sensitivity, and specificity are 99.39%, 98.79%, and 100% respectively. Proposed CNN model has significant advantages, and it can be used to differentiate the healthy and arrhythmic patients effectively. 2023-01-22T06:00:05Z 2023-01-22T06:00:05Z 2022-12 Article 2224-2007 http://dspace.mist.ac.bd:8080/xmlui/handle/123456789/744 en application/pdf Research and Development Wing, MIST |
| spellingShingle | Electrocardiogram, Continuous Wavelet Transform, Arrhythmia, Convolutional Neural Network Mohonta, Shadhon Chandra Firoj Ali, Md. A Novel Approach to Detect Cardiac Arrhythmia Based on Continuous Wavelet Transform and Convolutional Neural Network |
| title | A Novel Approach to Detect Cardiac Arrhythmia Based on Continuous Wavelet Transform and Convolutional Neural Network |
| title_full | A Novel Approach to Detect Cardiac Arrhythmia Based on Continuous Wavelet Transform and Convolutional Neural Network |
| title_fullStr | A Novel Approach to Detect Cardiac Arrhythmia Based on Continuous Wavelet Transform and Convolutional Neural Network |
| title_full_unstemmed | A Novel Approach to Detect Cardiac Arrhythmia Based on Continuous Wavelet Transform and Convolutional Neural Network |
| title_short | A Novel Approach to Detect Cardiac Arrhythmia Based on Continuous Wavelet Transform and Convolutional Neural Network |
| title_sort | novel approach to detect cardiac arrhythmia based on continuous wavelet transform and convolutional neural network |
| topic | Electrocardiogram, Continuous Wavelet Transform, Arrhythmia, Convolutional Neural Network |
| url | http://dspace.mist.ac.bd:8080/xmlui/handle/123456789/744 |
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