Wavelet-Based EEG Processing for Epilepsy Detection Using Fuzzy Entropy and Associative Petri Net
Epilepsy is a common neurological disease that can cause seizures and loss of consciousness and can have a severe negative impact on long-term cognitive function. Reducing the severity of impact requires early diagnosis and treatment. Epilepsy is traditionally diagnosed using electroencephalography...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8764553/ |
| _version_ | 1818380750755463168 |
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| author | Hsiu-Sen Chiang Mu-Yen Chen Yu-Jhih Huang |
| author_facet | Hsiu-Sen Chiang Mu-Yen Chen Yu-Jhih Huang |
| author_sort | Hsiu-Sen Chiang |
| collection | DOAJ |
| description | Epilepsy is a common neurological disease that can cause seizures and loss of consciousness and can have a severe negative impact on long-term cognitive function. Reducing the severity of impact requires early diagnosis and treatment. Epilepsy is traditionally diagnosed using electroencephalography (EEG) performed by trained physicians or technicians but this process is time-consuming and prone to interference, which can negatively impact accuracy. This paper develops a model for epilepsy diagnosis using discrete wavelet transform to analyze sub-bands within the EEG parameter and select EEG characteristics for epilepsy detection. The minimize entropy principle approach is used to build fuzzy membership functions of the characteristics of each brain wave and are then used as the basis for the construction of an associative Petri net model. Using our APN model, the associative Petri net approach provides diagnosis accuracy rates of 93.8%, outperforming similar approaches using decision tree, support vector machine, neural network, Bayes net, naïve Bayes, and tree augmented naïve Bayes. Thus, the proposed approach shows promise for fast, accurate, and objective diagnosis of epilepsy in clinical settings. |
| first_indexed | 2024-12-14T02:23:39Z |
| format | Article |
| id | doaj.art-60ac5687228c4a138c4e3f831e260017 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T02:23:39Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-60ac5687228c4a138c4e3f831e2600172022-12-21T23:20:27ZengIEEEIEEE Access2169-35362019-01-01710325510326210.1109/ACCESS.2019.29292668764553Wavelet-Based EEG Processing for Epilepsy Detection Using Fuzzy Entropy and Associative Petri NetHsiu-Sen Chiang0https://orcid.org/0000-0003-0228-6826Mu-Yen Chen1https://orcid.org/0000-0002-3945-4363Yu-Jhih Huang2Department of Information Management, National Taichung University of Science and Technology, Taichung, TaiwanDepartment of Information Management, National Taichung University of Science and Technology, Taichung, TaiwanDepartment of Information Management, National Taichung University of Science and Technology, Taichung, TaiwanEpilepsy is a common neurological disease that can cause seizures and loss of consciousness and can have a severe negative impact on long-term cognitive function. Reducing the severity of impact requires early diagnosis and treatment. Epilepsy is traditionally diagnosed using electroencephalography (EEG) performed by trained physicians or technicians but this process is time-consuming and prone to interference, which can negatively impact accuracy. This paper develops a model for epilepsy diagnosis using discrete wavelet transform to analyze sub-bands within the EEG parameter and select EEG characteristics for epilepsy detection. The minimize entropy principle approach is used to build fuzzy membership functions of the characteristics of each brain wave and are then used as the basis for the construction of an associative Petri net model. Using our APN model, the associative Petri net approach provides diagnosis accuracy rates of 93.8%, outperforming similar approaches using decision tree, support vector machine, neural network, Bayes net, naïve Bayes, and tree augmented naïve Bayes. Thus, the proposed approach shows promise for fast, accurate, and objective diagnosis of epilepsy in clinical settings.https://ieeexplore.ieee.org/document/8764553/Epilepsyelectroencephalogramwavelet transformassociative petri net |
| spellingShingle | Hsiu-Sen Chiang Mu-Yen Chen Yu-Jhih Huang Wavelet-Based EEG Processing for Epilepsy Detection Using Fuzzy Entropy and Associative Petri Net IEEE Access Epilepsy electroencephalogram wavelet transform associative petri net |
| title | Wavelet-Based EEG Processing for Epilepsy Detection Using Fuzzy Entropy and Associative Petri Net |
| title_full | Wavelet-Based EEG Processing for Epilepsy Detection Using Fuzzy Entropy and Associative Petri Net |
| title_fullStr | Wavelet-Based EEG Processing for Epilepsy Detection Using Fuzzy Entropy and Associative Petri Net |
| title_full_unstemmed | Wavelet-Based EEG Processing for Epilepsy Detection Using Fuzzy Entropy and Associative Petri Net |
| title_short | Wavelet-Based EEG Processing for Epilepsy Detection Using Fuzzy Entropy and Associative Petri Net |
| title_sort | wavelet based eeg processing for epilepsy detection using fuzzy entropy and associative petri net |
| topic | Epilepsy electroencephalogram wavelet transform associative petri net |
| url | https://ieeexplore.ieee.org/document/8764553/ |
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