Deep learning-based system to predict cardiac arrhythmia using hybrid features of transform techniques
An early and accurate detection of arrhythmias is essential reduce the mortality rate due to cardiac diseases. Manual screening of the electrocardiogram (ECG) signals are time consuming, strenuous, and liable to human errors. This article proposes a deep learning approach for automated detection of...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Intelligent Systems with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305322000643 |
| _version_ | 1828172758579675136 |
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| author | Santanu Sahoo Pratyusa Dash B.S.P. Mishra Sukanta Kumar Sabut |
| author_facet | Santanu Sahoo Pratyusa Dash B.S.P. Mishra Sukanta Kumar Sabut |
| author_sort | Santanu Sahoo |
| collection | DOAJ |
| description | An early and accurate detection of arrhythmias is essential reduce the mortality rate due to cardiac diseases. Manual screening of the electrocardiogram (ECG) signals are time consuming, strenuous, and liable to human errors. This article proposes a deep learning approach for automated detection of cardiac arrhythmia using RCG signals fro MIT-BIH database. Various decomposition techniques namely: discrete wavelet transform (DWT), empirical mode decomposition (EMD) and variational mode decomposition (VMD) are used to de-noise the ECG signal. The time-frequency based multi-domain features are extracted from the various coefficients of the sub-bands from de-noised signals. These obtained features are ranked based on Chi-squared test and particle swarm optimization (PSO) based methods to select the best informative features for better classification accuracy. The hybrid features was classified with deep neural network (DNN) with ten-fold cross validation strategy in classifying five types of ECG beats. The best results was obtained with an accuracy of 99.75% with less computational complexity of 0.14 s using Chi squared selection approach. Thus the proposed model can be used in the hospitals set-up to automatically screen the abnormal ECG beats. |
| first_indexed | 2024-04-12T03:46:03Z |
| format | Article |
| id | doaj.art-19fa1560c1d54ed5bd0c47e2c1cb1ce1 |
| institution | Directory Open Access Journal |
| issn | 2667-3053 |
| language | English |
| last_indexed | 2024-04-12T03:46:03Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj.art-19fa1560c1d54ed5bd0c47e2c1cb1ce12022-12-22T03:49:07ZengElsevierIntelligent Systems with Applications2667-30532022-11-0116200127Deep learning-based system to predict cardiac arrhythmia using hybrid features of transform techniquesSantanu Sahoo0Pratyusa Dash1B.S.P. Mishra2Sukanta Kumar Sabut3Department of ECE, SOA Deemed to be University, Bhubaneswar, Odisha, IndiaDepartment of Computer Science & Engineering, Heritage Institute of Technology, Kolkata, West Bengal, IndiaSchool of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, IndiaSchool of Electronics Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India; Corresponding author.An early and accurate detection of arrhythmias is essential reduce the mortality rate due to cardiac diseases. Manual screening of the electrocardiogram (ECG) signals are time consuming, strenuous, and liable to human errors. This article proposes a deep learning approach for automated detection of cardiac arrhythmia using RCG signals fro MIT-BIH database. Various decomposition techniques namely: discrete wavelet transform (DWT), empirical mode decomposition (EMD) and variational mode decomposition (VMD) are used to de-noise the ECG signal. The time-frequency based multi-domain features are extracted from the various coefficients of the sub-bands from de-noised signals. These obtained features are ranked based on Chi-squared test and particle swarm optimization (PSO) based methods to select the best informative features for better classification accuracy. The hybrid features was classified with deep neural network (DNN) with ten-fold cross validation strategy in classifying five types of ECG beats. The best results was obtained with an accuracy of 99.75% with less computational complexity of 0.14 s using Chi squared selection approach. Thus the proposed model can be used in the hospitals set-up to automatically screen the abnormal ECG beats.http://www.sciencedirect.com/science/article/pii/S2667305322000643ECG signalArrhythmiasSignal decompositionDNN model and RF classifier |
| spellingShingle | Santanu Sahoo Pratyusa Dash B.S.P. Mishra Sukanta Kumar Sabut Deep learning-based system to predict cardiac arrhythmia using hybrid features of transform techniques Intelligent Systems with Applications ECG signal Arrhythmias Signal decomposition DNN model and RF classifier |
| title | Deep learning-based system to predict cardiac arrhythmia using hybrid features of transform techniques |
| title_full | Deep learning-based system to predict cardiac arrhythmia using hybrid features of transform techniques |
| title_fullStr | Deep learning-based system to predict cardiac arrhythmia using hybrid features of transform techniques |
| title_full_unstemmed | Deep learning-based system to predict cardiac arrhythmia using hybrid features of transform techniques |
| title_short | Deep learning-based system to predict cardiac arrhythmia using hybrid features of transform techniques |
| title_sort | deep learning based system to predict cardiac arrhythmia using hybrid features of transform techniques |
| topic | ECG signal Arrhythmias Signal decomposition DNN model and RF classifier |
| url | http://www.sciencedirect.com/science/article/pii/S2667305322000643 |
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