Seizure Detection Based on Improved Genetic Algorithm Optimized Multilayer Network
With the increasment of epilepsy patients, traditional epileptic seizure recognition is generally completed by encephalography (EEG) technicians, which is time-consuming and labor-intensive, so the automatic detection of seizure is imminent. This paper proposes a method which constructs a multi-laye...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9848795/ |
| _version_ | 1798040462632681472 |
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| author | Yuhuan Xiong Fang Dong Duanpo Wu Lurong Jiang Junbiao Liu Bingqian Li |
| author_facet | Yuhuan Xiong Fang Dong Duanpo Wu Lurong Jiang Junbiao Liu Bingqian Li |
| author_sort | Yuhuan Xiong |
| collection | DOAJ |
| description | With the increasment of epilepsy patients, traditional epileptic seizure recognition is generally completed by encephalography (EEG) technicians, which is time-consuming and labor-intensive, so the automatic detection of seizure is imminent. This paper proposes a method which constructs a multi-layer network and extracts the same features in each network optimized by improved genetic algorithm (IGA). Among them, the multi-layer network refers to the three-layer network constructed by pearson correlation coefficient, mutual information and permutation disalignment index respectively. There is a lack of research on the fusion and comparison of different networks in previous studies. Therefore, this paper analyzes the effectiveness of different networks by studying the fusion relationship of different networks, and further uses IGA for iterative optimization with constraints to weight the network and features, and finally uses the random forest classifier to automatically detect epileptic seizures. On CHB-MIT database, accuracy (ACC), specificity (SPE), sensitivity (SEN) and F1 score (F1) of the method proposed in this paper reach 97.26%, 97.55%, 96.89% and 97.11%, respectively. On Siena scalp database, ACC, SPE, SEN and F1 reach 98.88%, 99.13%, 98.36% and 98.75%, respectively. The results show that the joint detection effect of the multi-layer network is better than the combined effect of other networks, and IGA can improve the effect of seizure detection. |
| first_indexed | 2024-04-11T22:07:56Z |
| format | Article |
| id | doaj.art-5b5041e2111847a0bfd3c19b6f141fe1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T22:07:56Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5b5041e2111847a0bfd3c19b6f141fe12022-12-22T04:00:38ZengIEEEIEEE Access2169-35362022-01-0110813438135410.1109/ACCESS.2022.31960049848795Seizure Detection Based on Improved Genetic Algorithm Optimized Multilayer NetworkYuhuan Xiong0https://orcid.org/0000-0001-5219-0879Fang Dong1Duanpo Wu2https://orcid.org/0000-0001-6954-6587Lurong Jiang3https://orcid.org/0000-0003-4870-9361Junbiao Liu4https://orcid.org/0000-0001-7326-4574Bingqian Li5School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, ChinaCollege of Information and Electric Engineering, Zhejiang University City College, Hangzhou, ChinaSchool of Communication Engineering, Hangzhou Dianzi University, Hangzhou, ChinaSchool of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, ChinaArtistic Design and Creation School, Zhejiang University City College, Hangzhou, ChinaSchool of Communication Engineering, Hangzhou Dianzi University, Hangzhou, ChinaWith the increasment of epilepsy patients, traditional epileptic seizure recognition is generally completed by encephalography (EEG) technicians, which is time-consuming and labor-intensive, so the automatic detection of seizure is imminent. This paper proposes a method which constructs a multi-layer network and extracts the same features in each network optimized by improved genetic algorithm (IGA). Among them, the multi-layer network refers to the three-layer network constructed by pearson correlation coefficient, mutual information and permutation disalignment index respectively. There is a lack of research on the fusion and comparison of different networks in previous studies. Therefore, this paper analyzes the effectiveness of different networks by studying the fusion relationship of different networks, and further uses IGA for iterative optimization with constraints to weight the network and features, and finally uses the random forest classifier to automatically detect epileptic seizures. On CHB-MIT database, accuracy (ACC), specificity (SPE), sensitivity (SEN) and F1 score (F1) of the method proposed in this paper reach 97.26%, 97.55%, 96.89% and 97.11%, respectively. On Siena scalp database, ACC, SPE, SEN and F1 reach 98.88%, 99.13%, 98.36% and 98.75%, respectively. The results show that the joint detection effect of the multi-layer network is better than the combined effect of other networks, and IGA can improve the effect of seizure detection.https://ieeexplore.ieee.org/document/9848795/Permutation disalignment indexmulti-layer networkgenetic algorithmseizure detection |
| spellingShingle | Yuhuan Xiong Fang Dong Duanpo Wu Lurong Jiang Junbiao Liu Bingqian Li Seizure Detection Based on Improved Genetic Algorithm Optimized Multilayer Network IEEE Access Permutation disalignment index multi-layer network genetic algorithm seizure detection |
| title | Seizure Detection Based on Improved Genetic Algorithm Optimized Multilayer Network |
| title_full | Seizure Detection Based on Improved Genetic Algorithm Optimized Multilayer Network |
| title_fullStr | Seizure Detection Based on Improved Genetic Algorithm Optimized Multilayer Network |
| title_full_unstemmed | Seizure Detection Based on Improved Genetic Algorithm Optimized Multilayer Network |
| title_short | Seizure Detection Based on Improved Genetic Algorithm Optimized Multilayer Network |
| title_sort | seizure detection based on improved genetic algorithm optimized multilayer network |
| topic | Permutation disalignment index multi-layer network genetic algorithm seizure detection |
| url | https://ieeexplore.ieee.org/document/9848795/ |
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