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New hybrid deep learning approach using bigru-bilstm and multilayered dilated cnn to detect arrhythmia

Deep learning methods have shown early progress in analyzing complicated ECG signals, especially in heartbeat classification and arrhythmia detection. However, there is still a long way to go in terms of health-related data analysis. This research provides a duel structured and bidirectional Recurre...

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Main Authors:	Islam, Md Shofiqul, Islam, Md Nahidul, Noramiza, Hashim, Rashid, Mamunur, Bari, Bifta Sama, Al Farid, Fahmid
Format:	Article
Language:	English
Published:	Institute of Electrical and Electronics Engineers Inc. 2022
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/34932/1/New%20hybrid%20deep%20learning%20approach%20using%20bigru-bilstm%20and%20multilayered%20dilated%20cnn%20to%20detect%20arrhythmia.pdf http://umpir.ump.edu.my/id/eprint/34932/ https://doi.org/10.1109/ACCESS.2022.3178710 https://doi.org/10.1109/ACCESS.2022.3178710
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spelling	my.ump.umpir.349322022-11-07T08:10:53Z http://umpir.ump.edu.my/id/eprint/34932/ New hybrid deep learning approach using bigru-bilstm and multilayered dilated cnn to detect arrhythmia Islam, Md Shofiqul Islam, Md Nahidul Noramiza, Hashim Rashid, Mamunur Bari, Bifta Sama Al Farid, Fahmid QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Deep learning methods have shown early progress in analyzing complicated ECG signals, especially in heartbeat classification and arrhythmia detection. However, there is still a long way to go in terms of health-related data analysis. This research provides a duel structured and bidirectional Recurrent Neural Network(RNN) method for arrhythmia classification that addresses the issues with multilayered dilated convolution neural network (CNN) models. Initially, the data is preprocessed by Chebyshev Type II filtering that is faster and do not use statistical characteristics. Noise from the preprocesed filter is aslo removed by using Daubechies wavelet that can able to solve fractal problems and signal discontinuities. An then Z-normalization is done using Pan-Tompkins normalization technique for handling of different normally distributed samples. Finally, a generative adversarial network (GAN)-based synthetic signal is generated for recreation of signal to handle imbalanced signal class. The proposed Bidirectional RNN with Dilated CNN (BRDC) appears to take advantage of the potentiality of multilayered dilated CNN and bidirectional RNN unit (bidirectional gated recurrent Units, BiGRU - bidirectional long short-term memory, BiLSTM) architecture to generate fusion features. Finally, the signals are classified by fully connected layer and Rectified Linear Unit (ReLU) activation function. The PhysioNet 2017 challenge dataset is used to train and validate the proposed model. By combining fusion features with dilated CNN, the learned model significantly improves the classification performance and interpretability. The experimental findings show that, for MIT-BIH provided ECG (electrocardiogram) data to identify arrhythmia, the proposed BRDC model outperforms existing models with 99.90 % accuracy, 98.41 % F1, 97.96 % precision, and 99.90 % recall during training. One of the significant findings of this study is that the proposed approach can significantly reduce time length when employing RNN networks with multilayered dilated CNN. Overall, our hybrid model using BiGRU-BiLSTM and multi-layered dilated CNN provides a cost-effective ECG signal reduction and high-performance automated recognition technique to identify arrhythmia. Our future improvement will focus on the classification of numerous arrhythmia signal-based data, automatic and cloud based ECG classification. Institute of Electrical and Electronics Engineers Inc. 2022 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/34932/1/New%20hybrid%20deep%20learning%20approach%20using%20bigru-bilstm%20and%20multilayered%20dilated%20cnn%20to%20detect%20arrhythmia.pdf Islam, Md Shofiqul and Islam, Md Nahidul and Noramiza, Hashim and Rashid, Mamunur and Bari, Bifta Sama and Al Farid, Fahmid (2022) New hybrid deep learning approach using bigru-bilstm and multilayered dilated cnn to detect arrhythmia. IEEE Access, 10. pp. 58081-58096. ISSN 2169-3536 https://doi.org/10.1109/ACCESS.2022.3178710 https://doi.org/10.1109/ACCESS.2022.3178710
institution	Universiti Malaysia Pahang
building	UMP Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang
content_source	UMP Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English
topic	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General)
spellingShingle	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Islam, Md Shofiqul Islam, Md Nahidul Noramiza, Hashim Rashid, Mamunur Bari, Bifta Sama Al Farid, Fahmid New hybrid deep learning approach using bigru-bilstm and multilayered dilated cnn to detect arrhythmia
description	Deep learning methods have shown early progress in analyzing complicated ECG signals, especially in heartbeat classification and arrhythmia detection. However, there is still a long way to go in terms of health-related data analysis. This research provides a duel structured and bidirectional Recurrent Neural Network(RNN) method for arrhythmia classification that addresses the issues with multilayered dilated convolution neural network (CNN) models. Initially, the data is preprocessed by Chebyshev Type II filtering that is faster and do not use statistical characteristics. Noise from the preprocesed filter is aslo removed by using Daubechies wavelet that can able to solve fractal problems and signal discontinuities. An then Z-normalization is done using Pan-Tompkins normalization technique for handling of different normally distributed samples. Finally, a generative adversarial network (GAN)-based synthetic signal is generated for recreation of signal to handle imbalanced signal class. The proposed Bidirectional RNN with Dilated CNN (BRDC) appears to take advantage of the potentiality of multilayered dilated CNN and bidirectional RNN unit (bidirectional gated recurrent Units, BiGRU - bidirectional long short-term memory, BiLSTM) architecture to generate fusion features. Finally, the signals are classified by fully connected layer and Rectified Linear Unit (ReLU) activation function. The PhysioNet 2017 challenge dataset is used to train and validate the proposed model. By combining fusion features with dilated CNN, the learned model significantly improves the classification performance and interpretability. The experimental findings show that, for MIT-BIH provided ECG (electrocardiogram) data to identify arrhythmia, the proposed BRDC model outperforms existing models with 99.90 % accuracy, 98.41 % F1, 97.96 % precision, and 99.90 % recall during training. One of the significant findings of this study is that the proposed approach can significantly reduce time length when employing RNN networks with multilayered dilated CNN. Overall, our hybrid model using BiGRU-BiLSTM and multi-layered dilated CNN provides a cost-effective ECG signal reduction and high-performance automated recognition technique to identify arrhythmia. Our future improvement will focus on the classification of numerous arrhythmia signal-based data, automatic and cloud based ECG classification.
format	Article
author	Islam, Md Shofiqul Islam, Md Nahidul Noramiza, Hashim Rashid, Mamunur Bari, Bifta Sama Al Farid, Fahmid
author_facet	Islam, Md Shofiqul Islam, Md Nahidul Noramiza, Hashim Rashid, Mamunur Bari, Bifta Sama Al Farid, Fahmid
author_sort	Islam, Md Shofiqul
title	New hybrid deep learning approach using bigru-bilstm and multilayered dilated cnn to detect arrhythmia
title_short	New hybrid deep learning approach using bigru-bilstm and multilayered dilated cnn to detect arrhythmia
title_full	New hybrid deep learning approach using bigru-bilstm and multilayered dilated cnn to detect arrhythmia
title_fullStr	New hybrid deep learning approach using bigru-bilstm and multilayered dilated cnn to detect arrhythmia
title_full_unstemmed	New hybrid deep learning approach using bigru-bilstm and multilayered dilated cnn to detect arrhythmia
title_sort	new hybrid deep learning approach using bigru-bilstm and multilayered dilated cnn to detect arrhythmia
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2022
url	http://umpir.ump.edu.my/id/eprint/34932/1/New%20hybrid%20deep%20learning%20approach%20using%20bigru-bilstm%20and%20multilayered%20dilated%20cnn%20to%20detect%20arrhythmia.pdf http://umpir.ump.edu.my/id/eprint/34932/ https://doi.org/10.1109/ACCESS.2022.3178710 https://doi.org/10.1109/ACCESS.2022.3178710
_version_	1751536381141712896
score	13.211869

New hybrid deep learning approach using bigru-bilstm and multilayered dilated cnn to detect arrhythmia

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