Arrhythmia classification using multirate processing metaheuristic optimization and variational mode decomposition
The concept of mobile healthcare systems is promising. It is based on the cloud connected wireless biomedical wearables. In this scenario, the compression, processing, transmission and power effectiveness with precision are the key terms. A novel technique is presented for arrhythmia identification...
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| Published in | Journal of King Saud University. Computer and information sciences Vol. 35; no. 1; pp. 26 - 37 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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Elsevier B.V
01.01.2023
Elsevier Springer |
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| Abstract | The concept of mobile healthcare systems is promising. It is based on the cloud connected wireless biomedical wearables. In this scenario, the compression, processing, transmission and power effectiveness with precision are the key terms. A novel technique is presented for arrhythmia identification by processing the electrocardiogram signals. The solution is based on an effective hybridization of the multirate processing, QRS selection, variational mode decomposition, features mining from Modes, Metaheuristic optimization based features selection, and machine learning algorithms. The MIT-BIH dataset is used for experimentation. Performance of the Butterfly Optimization Algorithm, Manta Ray Foraging Optimization, and Emperor Penguin Optimization algorithms is investigated for features selection. A multi-subjects and multi-class dataset is used for testing the performance of classification by following the 10-fold cross validation strategy. The multirate processing with QRS selection and Metaheuristic optimization dependent features selection bring compression and aptitude for the processing and data transmission efficiencies. The system efficiently incorporates the multirate processing while securing an effective signal reconstruction. The respective compression gains and classification accuracies for the Butterfly Optimization Algorithm, Manta Ray Foraging Optimization, and Emperor Penguin Optimization algorithms are 27-fold, 29.45-fold & 46.29-fold and 99.14%, 99.08% & 98.65%. |
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| AbstractList | The concept of mobile healthcare systems is promising. It is based on the cloud connected wireless biomedical wearables. In this scenario, the compression, processing, transmission and power effectiveness with precision are the key terms. A novel technique is presented for arrhythmia identification by processing the electrocardiogram signals. The solution is based on an effective hybridization of the multirate processing, QRS selection, variational mode decomposition, features mining from Modes, Metaheuristic optimization based features selection, and machine learning algorithms. The MIT-BIH dataset is used for experimentation. Performance of the Butterfly Optimization Algorithm, Manta Ray Foraging Optimization, and Emperor Penguin Optimization algorithms is investigated for features selection. A multi-subjects and multi-class dataset is used for testing the performance of classification by following the 10-fold cross validation strategy. The multirate processing with QRS selection and Metaheuristic optimization dependent features selection bring compression and aptitude for the processing and data transmission efficiencies. The system efficiently incorporates the multirate processing while securing an effective signal reconstruction. The respective compression gains and classification accuracies for the Butterfly Optimization Algorithm, Manta Ray Foraging Optimization, and Emperor Penguin Optimization algorithms are 27-fold, 29.45-fold & 46.29-fold and 99.14%, 99.08% & 98.65%. |
| Author | Srinivasan, Kathiravan Mian Qaisar, Saeed I. Khan, Sibghatulla Krichen, Moez |
| Author_xml | – sequence: 1 givenname: Saeed surname: Mian Qaisar fullname: Mian Qaisar, Saeed email: sqaisar@effatuniversity.edu.sa organization: Department of Electrical and Computer Engineering, Effat University, 22332 Jeddah, Saudi Arabia – sequence: 2 givenname: Sibghatulla surname: I. Khan fullname: I. Khan, Sibghatulla organization: Department of Electronics and Communication Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, 501301, India – sequence: 3 givenname: Kathiravan surname: Srinivasan fullname: Srinivasan, Kathiravan organization: School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India – sequence: 4 givenname: Moez surname: Krichen fullname: Krichen, Moez email: moez.krichen@redcad.org organization: ReDCAD Laboratory, University of Sfax, Sfax, 3029, Tunisia |
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| Keywords | Metaheuristic optimization Compression Multirate processing Arrhythmia Electrocardiogram Classification Machine learning Mobile healthcare Variational mode decomposition Modes features selection Computational complexity Oscillatory Modes Feature selection |
| Language | English |
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| SubjectTerms | Arrhythmia Classification Compression Computational complexity Computer Science Electrocardiogram Engineering Sciences Machine learning Metaheuristic optimization Mobile healthcare Modes features selection Multirate processing Variational mode decomposition |
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| Title | Arrhythmia classification using multirate processing metaheuristic optimization and variational mode decomposition |
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