Feature Selection Using Selective Opposition Based Artificial Rabbits Optimization for Arrhythmia Classification on Internet of Medical Things Environment
An Electrocardiogram (ECG) is a non-invasive test that is broadly utilized for monitoring and diagnosing the cardiac arrhythmia. An irregularity of the heartbeat is generally defined as arrhythmia, which potentially causes the fatal difficulties that creates an instantaneous life risk. Therefore, th...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10242063/ |
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| author | G. S. Nijaguna N. Dayananda Lal Parameshachari Bidare Divakarachari Rocio Perez de Prado Marcin Wozniak Raj Kumar Patra |
| author_facet | G. S. Nijaguna N. Dayananda Lal Parameshachari Bidare Divakarachari Rocio Perez de Prado Marcin Wozniak Raj Kumar Patra |
| author_sort | G. S. Nijaguna |
| collection | DOAJ |
| description | An Electrocardiogram (ECG) is a non-invasive test that is broadly utilized for monitoring and diagnosing the cardiac arrhythmia. An irregularity of the heartbeat is generally defined as arrhythmia, which potentially causes the fatal difficulties that creates an instantaneous life risk. Therefore, the arrhythmia classification is a challenging task because of the overfitting issue caused by high dimensional feature space of ECG signal. In this research, the incorporation of the Internet of Medical Things (IoMT) is developed with artificial intelligence to provide the health monitoring for people who are having arrhythmia. In this work, the time, time-frequency, entropy, nonlinearity features of ECG and deep features of ECG from Convolutional Neural Network (CNN) are extracted to obtain different categories of ECG signal features. The Selective Opposition (SO) strategy based Artificial Rabbits Optimization (SOARO) is proposed for selecting the optimal feature subset from the overall features to avoid the overfitting issue. The chosen features are used to improve the classification done by Auto Encoder (AE). Further, the Shapley additive explanations (SHAP) based model is used to interpret the classified output from AE. The MIT-BIH arrhythmia database is used for evaluating the proposed SOARO-AE. The performance of the proposed SOARO-AE is evaluated by using the accuracy, sensitivity, specificity, recall and F1-Measure. The existing researches such as C-LSTM, DL-LAC-CNN, CNN-DNN, MC-ECG, FC and MEAHA-CNN are used to evaluate the SOARO-AE method. The accuracy of SOARO-AE is 98.89% which is high when compared to the C-LSTM, DL-LAC-CNN, CNN-DNN, FC and MEAHA-CNN. |
| first_indexed | 2024-03-11T23:36:48Z |
| format | Article |
| id | doaj.art-51f927131a024cde8c4a1b2efe51af43 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T23:36:48Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-51f927131a024cde8c4a1b2efe51af432023-09-19T23:01:07ZengIEEEIEEE Access2169-35362023-01-011110005210006910.1109/ACCESS.2023.331253710242063Feature Selection Using Selective Opposition Based Artificial Rabbits Optimization for Arrhythmia Classification on Internet of Medical Things EnvironmentG. S. Nijaguna0N. Dayananda Lal1Parameshachari Bidare Divakarachari2https://orcid.org/0000-0002-3997-5070Rocio Perez de Prado3https://orcid.org/0000-0001-6097-4016Marcin Wozniak4https://orcid.org/0000-0002-9073-5347Raj Kumar Patra5Department of Artificial Intelligence and Machine Learning, S.E.A. College of Engineering and Technology, Bengaluru, IndiaDepartment of CSE, GITAM School of Technology, GITAM (Deemed to be University), Bengaluru, IndiaDepartment of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Bengaluru, IndiaTelecommunication Engineering Department, University of Jaén, Linares, Jaén, SpainFaculty of Applied Mathematics, Silesian University of Technology, Gliwice, PolandDepartment of Computer Science and Engineering, CMR Technical Campus, Hyderabad, IndiaAn Electrocardiogram (ECG) is a non-invasive test that is broadly utilized for monitoring and diagnosing the cardiac arrhythmia. An irregularity of the heartbeat is generally defined as arrhythmia, which potentially causes the fatal difficulties that creates an instantaneous life risk. Therefore, the arrhythmia classification is a challenging task because of the overfitting issue caused by high dimensional feature space of ECG signal. In this research, the incorporation of the Internet of Medical Things (IoMT) is developed with artificial intelligence to provide the health monitoring for people who are having arrhythmia. In this work, the time, time-frequency, entropy, nonlinearity features of ECG and deep features of ECG from Convolutional Neural Network (CNN) are extracted to obtain different categories of ECG signal features. The Selective Opposition (SO) strategy based Artificial Rabbits Optimization (SOARO) is proposed for selecting the optimal feature subset from the overall features to avoid the overfitting issue. The chosen features are used to improve the classification done by Auto Encoder (AE). Further, the Shapley additive explanations (SHAP) based model is used to interpret the classified output from AE. The MIT-BIH arrhythmia database is used for evaluating the proposed SOARO-AE. The performance of the proposed SOARO-AE is evaluated by using the accuracy, sensitivity, specificity, recall and F1-Measure. The existing researches such as C-LSTM, DL-LAC-CNN, CNN-DNN, MC-ECG, FC and MEAHA-CNN are used to evaluate the SOARO-AE method. The accuracy of SOARO-AE is 98.89% which is high when compared to the C-LSTM, DL-LAC-CNN, CNN-DNN, FC and MEAHA-CNN.https://ieeexplore.ieee.org/document/10242063/Arrhythmiaartificial rabbits optimizationauto encoderelectrocardiogramhealth monitoringinternet of medical things |
| spellingShingle | G. S. Nijaguna N. Dayananda Lal Parameshachari Bidare Divakarachari Rocio Perez de Prado Marcin Wozniak Raj Kumar Patra Feature Selection Using Selective Opposition Based Artificial Rabbits Optimization for Arrhythmia Classification on Internet of Medical Things Environment IEEE Access Arrhythmia artificial rabbits optimization auto encoder electrocardiogram health monitoring internet of medical things |
| title | Feature Selection Using Selective Opposition Based Artificial Rabbits Optimization for Arrhythmia Classification on Internet of Medical Things Environment |
| title_full | Feature Selection Using Selective Opposition Based Artificial Rabbits Optimization for Arrhythmia Classification on Internet of Medical Things Environment |
| title_fullStr | Feature Selection Using Selective Opposition Based Artificial Rabbits Optimization for Arrhythmia Classification on Internet of Medical Things Environment |
| title_full_unstemmed | Feature Selection Using Selective Opposition Based Artificial Rabbits Optimization for Arrhythmia Classification on Internet of Medical Things Environment |
| title_short | Feature Selection Using Selective Opposition Based Artificial Rabbits Optimization for Arrhythmia Classification on Internet of Medical Things Environment |
| title_sort | feature selection using selective opposition based artificial rabbits optimization for arrhythmia classification on internet of medical things environment |
| topic | Arrhythmia artificial rabbits optimization auto encoder electrocardiogram health monitoring internet of medical things |
| url | https://ieeexplore.ieee.org/document/10242063/ |
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