Detection of Arrhythmia Heartbeats from ECG Signal Using Wavelet Transform-Based CNN Model
Abstract In India, over 25,000 people have died from cardiovascular annually over the past 4 years , and over 28,000 in the previous 3 years. Most of the deaths nowadays are mainly due to cardiovascular diseases (CVD). Arrhythmia is the leading cause of cardiovascular mortality. Arrhythmia is a cond...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2023-05-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-023-00256-z |
| _version_ | 1797827375475458048 |
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| author | Saroj Kumar pandey Anupam Shukla Surbhi Bhatia Thippa Reddy Gadekallu Ankit Kumar Arwa Mashat Mohd Asif Shah Rekh Ram Janghel |
| author_facet | Saroj Kumar pandey Anupam Shukla Surbhi Bhatia Thippa Reddy Gadekallu Ankit Kumar Arwa Mashat Mohd Asif Shah Rekh Ram Janghel |
| author_sort | Saroj Kumar pandey |
| collection | DOAJ |
| description | Abstract In India, over 25,000 people have died from cardiovascular annually over the past 4 years , and over 28,000 in the previous 3 years. Most of the deaths nowadays are mainly due to cardiovascular diseases (CVD). Arrhythmia is the leading cause of cardiovascular mortality. Arrhythmia is a condition in which the heartbeat is abnormally fast or slow. The current detection method for diseases is analyzing by the electrocardiogram (ECG), a medical monitoring technique that records heart activity. Since actuations in ECG signals are so slight that they cannot be seen by the human eye, the identification of cardiac arrhythmias is one of the most difficult undertakings. Unfortunately, it takes a lot of medical time and money to find professionals to examine a large amount of ECG data . As a result, machine learning-based methods have become increasingly prevalent for recognizing ECG features. In this work, we classify five different heartbeats using the MIT-BIH arrhythmia database . Wavelet self-adaptive thresholding methods are used to first denoise the ECG signal. Then, an efficient 12-layer deep 1D Convolutional Neural Network (CNN) is introduced for better features extraction, and finally, SoftMax and machine learning classifiers are applied to classify the heartbeats. The proposed method achieved an average accuracy of 99.40%, precision of 98.78%, recall of 98.78%, and F1 score of 98.74%, which clearly show that it outperforms with the exiting model . Architecture of proposed work is simple but effective in remote cardiac diagnosis paradigm that can be implemented on e-health devices. |
| first_indexed | 2024-04-09T12:47:16Z |
| format | Article |
| id | doaj.art-63108d2c6cd64af6a013b7b2c114d6b3 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-04-09T12:47:16Z |
| publishDate | 2023-05-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-63108d2c6cd64af6a013b7b2c114d6b32023-05-14T11:27:11ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832023-05-0116111410.1007/s44196-023-00256-zDetection of Arrhythmia Heartbeats from ECG Signal Using Wavelet Transform-Based CNN ModelSaroj Kumar pandey0Anupam Shukla1Surbhi Bhatia2Thippa Reddy Gadekallu3Ankit Kumar4Arwa Mashat5Mohd Asif Shah6Rekh Ram Janghel7Department of Computer Engineering & Applications, GLA UniversityDepartment of Computer Science, ABV-IIITMDepartment of Information Systems, College of Computer Sciences and Information Technology, King Faisal UniversitySchool of Information Technology and Engineering, Vellore Institute of TechnologyDepartment of Computer Engineering & Applications, GLA UniversityCollege of Computer Science and Information Technology, King Abdulaziz UniversityDepartment of Economics College of Business and Economics, Kabridahar UniversityDepartment of Information Technology, National Institute of TechnologyAbstract In India, over 25,000 people have died from cardiovascular annually over the past 4 years , and over 28,000 in the previous 3 years. Most of the deaths nowadays are mainly due to cardiovascular diseases (CVD). Arrhythmia is the leading cause of cardiovascular mortality. Arrhythmia is a condition in which the heartbeat is abnormally fast or slow. The current detection method for diseases is analyzing by the electrocardiogram (ECG), a medical monitoring technique that records heart activity. Since actuations in ECG signals are so slight that they cannot be seen by the human eye, the identification of cardiac arrhythmias is one of the most difficult undertakings. Unfortunately, it takes a lot of medical time and money to find professionals to examine a large amount of ECG data . As a result, machine learning-based methods have become increasingly prevalent for recognizing ECG features. In this work, we classify five different heartbeats using the MIT-BIH arrhythmia database . Wavelet self-adaptive thresholding methods are used to first denoise the ECG signal. Then, an efficient 12-layer deep 1D Convolutional Neural Network (CNN) is introduced for better features extraction, and finally, SoftMax and machine learning classifiers are applied to classify the heartbeats. The proposed method achieved an average accuracy of 99.40%, precision of 98.78%, recall of 98.78%, and F1 score of 98.74%, which clearly show that it outperforms with the exiting model . Architecture of proposed work is simple but effective in remote cardiac diagnosis paradigm that can be implemented on e-health devices.https://doi.org/10.1007/s44196-023-00256-zCNNClassificationArrhythmiaImbalanced data setECG signals |
| spellingShingle | Saroj Kumar pandey Anupam Shukla Surbhi Bhatia Thippa Reddy Gadekallu Ankit Kumar Arwa Mashat Mohd Asif Shah Rekh Ram Janghel Detection of Arrhythmia Heartbeats from ECG Signal Using Wavelet Transform-Based CNN Model International Journal of Computational Intelligence Systems CNN Classification Arrhythmia Imbalanced data set ECG signals |
| title | Detection of Arrhythmia Heartbeats from ECG Signal Using Wavelet Transform-Based CNN Model |
| title_full | Detection of Arrhythmia Heartbeats from ECG Signal Using Wavelet Transform-Based CNN Model |
| title_fullStr | Detection of Arrhythmia Heartbeats from ECG Signal Using Wavelet Transform-Based CNN Model |
| title_full_unstemmed | Detection of Arrhythmia Heartbeats from ECG Signal Using Wavelet Transform-Based CNN Model |
| title_short | Detection of Arrhythmia Heartbeats from ECG Signal Using Wavelet Transform-Based CNN Model |
| title_sort | detection of arrhythmia heartbeats from ecg signal using wavelet transform based cnn model |
| topic | CNN Classification Arrhythmia Imbalanced data set ECG signals |
| url | https://doi.org/10.1007/s44196-023-00256-z |
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