Automatic Epilepsy Detection Based on Wavelets Constructed From Data
Epileptic seizures are caused by excessive, synchronized activity of large groups of neurons. In human electroencephalograph (EEG), they are reflected by multiple epileptic characteristic waves. Based on the idea of template matching, this paper presents a patient-specific approach for the automatic...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8457077/ |
| _version_ | 1831799130959118336 |
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| author | Zhenghui Gu Gang Yan Jun Zhang Yuanqing Li Zhu Liang Yu |
| author_facet | Zhenghui Gu Gang Yan Jun Zhang Yuanqing Li Zhu Liang Yu |
| author_sort | Zhenghui Gu |
| collection | DOAJ |
| description | Epileptic seizures are caused by excessive, synchronized activity of large groups of neurons. In human electroencephalograph (EEG), they are reflected by multiple epileptic characteristic waves. Based on the idea of template matching, this paper presents a patient-specific approach for the automatic detection of epileptic seizures. In our method, a set of wavelets are constructed based on the epileptic characteristic waves extracted from training EEG signals, and then continuous wavelet transform (CWT) is performed on the recorded EEG. The coefficients of CWT reflect the similarity of the recorded EEG and the epileptic characteristic waveforms and thus can be used to detect if the epileptic characteristic waveforms exist in the EEG. After applying data fusion to the CWT coefficient matrices corresponding to the multiple constructed wavelets, the boundaries of seizures can be determined. In the experiment, our constructed wavelets performed better in the detection of epileptic characteristic waves compared to the Daubechies wavelet. We analyzed the EEG of 10 patients and our method detected 32 out of 34 seizures and declared five false detections. Therefore, our method is promising for the automatic detection of epileptic seizures and the real-time monitoring of patients' EEG signal. |
| first_indexed | 2024-12-22T17:39:49Z |
| format | Article |
| id | doaj.art-fdc38e6682cc4a8a9e118a696ad3f5cd |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T17:39:49Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-fdc38e6682cc4a8a9e118a696ad3f5cd2022-12-21T18:18:26ZengIEEEIEEE Access2169-35362018-01-016531335314010.1109/ACCESS.2018.28676428457077Automatic Epilepsy Detection Based on Wavelets Constructed From DataZhenghui Gu0Gang Yan1Jun Zhang2https://orcid.org/0000-0002-9647-0941Yuanqing Li3Zhu Liang Yu4College of Automation Science and Engineering, South China University of Technology, Guangzhou, ChinaCollege of Automation Science and Engineering, South China University of Technology, Guangzhou, ChinaSchool of Information Engineering, Guangdong University of Technology, Guangzhou, ChinaCollege of Automation Science and Engineering, South China University of Technology, Guangzhou, ChinaCollege of Automation Science and Engineering, South China University of Technology, Guangzhou, ChinaEpileptic seizures are caused by excessive, synchronized activity of large groups of neurons. In human electroencephalograph (EEG), they are reflected by multiple epileptic characteristic waves. Based on the idea of template matching, this paper presents a patient-specific approach for the automatic detection of epileptic seizures. In our method, a set of wavelets are constructed based on the epileptic characteristic waves extracted from training EEG signals, and then continuous wavelet transform (CWT) is performed on the recorded EEG. The coefficients of CWT reflect the similarity of the recorded EEG and the epileptic characteristic waveforms and thus can be used to detect if the epileptic characteristic waveforms exist in the EEG. After applying data fusion to the CWT coefficient matrices corresponding to the multiple constructed wavelets, the boundaries of seizures can be determined. In the experiment, our constructed wavelets performed better in the detection of epileptic characteristic waves compared to the Daubechies wavelet. We analyzed the EEG of 10 patients and our method detected 32 out of 34 seizures and declared five false detections. Therefore, our method is promising for the automatic detection of epileptic seizures and the real-time monitoring of patients' EEG signal.https://ieeexplore.ieee.org/document/8457077/EEGconstructed waveletcontinuous wavelet transformepileptic seizure detection |
| spellingShingle | Zhenghui Gu Gang Yan Jun Zhang Yuanqing Li Zhu Liang Yu Automatic Epilepsy Detection Based on Wavelets Constructed From Data IEEE Access EEG constructed wavelet continuous wavelet transform epileptic seizure detection |
| title | Automatic Epilepsy Detection Based on Wavelets Constructed From Data |
| title_full | Automatic Epilepsy Detection Based on Wavelets Constructed From Data |
| title_fullStr | Automatic Epilepsy Detection Based on Wavelets Constructed From Data |
| title_full_unstemmed | Automatic Epilepsy Detection Based on Wavelets Constructed From Data |
| title_short | Automatic Epilepsy Detection Based on Wavelets Constructed From Data |
| title_sort | automatic epilepsy detection based on wavelets constructed from data |
| topic | EEG constructed wavelet continuous wavelet transform epileptic seizure detection |
| url | https://ieeexplore.ieee.org/document/8457077/ |
| work_keys_str_mv | AT zhenghuigu automaticepilepsydetectionbasedonwaveletsconstructedfromdata AT gangyan automaticepilepsydetectionbasedonwaveletsconstructedfromdata AT junzhang automaticepilepsydetectionbasedonwaveletsconstructedfromdata AT yuanqingli automaticepilepsydetectionbasedonwaveletsconstructedfromdata AT zhuliangyu automaticepilepsydetectionbasedonwaveletsconstructedfromdata |