Revised Tunable Q-Factor Wavelet Transform for EEG-Based Epileptic Seizure Detection
Electroencephalogram (EEG) signals are an essential tool for the detection of epilepsy. Because of the complex time series and frequency features of EEG signals, traditional feature extraction methods have difficulty meeting the requirements of recognition performance. The tunable Q-factor wavelet t...
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| Format: | Article |
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IEEE
2023-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10070792/ |
| _version_ | 1797805026830188544 |
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| author | Zhen Liu Bingyu Zhu Manfeng Hu Zhaohong Deng Jingxiang Zhang |
| author_facet | Zhen Liu Bingyu Zhu Manfeng Hu Zhaohong Deng Jingxiang Zhang |
| author_sort | Zhen Liu |
| collection | DOAJ |
| description | Electroencephalogram (EEG) signals are an essential tool for the detection of epilepsy. Because of the complex time series and frequency features of EEG signals, traditional feature extraction methods have difficulty meeting the requirements of recognition performance. The tunable Q-factor wavelet transform (TQWT), which is a constant-Q transform that is easily invertible and modestly oversampled, has been successfully used for feature extraction of EEG signals. Because the constant-Q is set in advance and cannot be optimized, further applications of the TQWT are restricted. To solve this problem, the revised tunable Q-factor wavelet transform (RTQWT) is proposed in this paper. RTQWT is based on the weighted normalized entropy and overcomes the problems of a nontunable Q-factor and the lack of an optimized tunable criterion. In contrast to the continuous wavelet transform and the raw tunable Q-factor wavelet transform, the wavelet transform corresponding to the revised Q-factor, i.e., RTQWT, is sufficiently better adapted to the nonstationary nature of EEG signals. Therefore, the precise and specific characteristic subspaces obtained can improve the classification accuracy of EEG signals. The classification of the extracted features was performed using the decision tree, linear discriminant, naive Bayes, SVM and KNN classifiers. The performance of the new approach was tested by evaluating the accuracies of five time-frequency distributions: FT, EMD, DWT, CWT and TQWT. The experiments showed that the RTQWT proposed in this paper can be used to extract detailed features more effectively and improve the classification accuracy of EEG signals. |
| first_indexed | 2024-03-13T05:45:52Z |
| format | Article |
| id | doaj.art-7e170971c01a4c318cbecd8ba7f39144 |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-13T05:45:52Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-7e170971c01a4c318cbecd8ba7f391442023-06-13T20:10:23ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102023-01-01311707172010.1109/TNSRE.2023.325730610070792Revised Tunable Q-Factor Wavelet Transform for EEG-Based Epileptic Seizure DetectionZhen Liu0https://orcid.org/0000-0001-5399-5978Bingyu Zhu1Manfeng Hu2https://orcid.org/0000-0001-6169-3013Zhaohong Deng3https://orcid.org/0000-0002-0790-6492Jingxiang Zhang4https://orcid.org/0000-0002-5845-4887School of Science, Jiangnan University, Wuxi, ChinaSchool of Science, Jiangnan University, Wuxi, ChinaSchool of Science, Jiangnan University, Wuxi, ChinaSchool of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, ChinaSchool of Science, Jiangnan University, Wuxi, ChinaElectroencephalogram (EEG) signals are an essential tool for the detection of epilepsy. Because of the complex time series and frequency features of EEG signals, traditional feature extraction methods have difficulty meeting the requirements of recognition performance. The tunable Q-factor wavelet transform (TQWT), which is a constant-Q transform that is easily invertible and modestly oversampled, has been successfully used for feature extraction of EEG signals. Because the constant-Q is set in advance and cannot be optimized, further applications of the TQWT are restricted. To solve this problem, the revised tunable Q-factor wavelet transform (RTQWT) is proposed in this paper. RTQWT is based on the weighted normalized entropy and overcomes the problems of a nontunable Q-factor and the lack of an optimized tunable criterion. In contrast to the continuous wavelet transform and the raw tunable Q-factor wavelet transform, the wavelet transform corresponding to the revised Q-factor, i.e., RTQWT, is sufficiently better adapted to the nonstationary nature of EEG signals. Therefore, the precise and specific characteristic subspaces obtained can improve the classification accuracy of EEG signals. The classification of the extracted features was performed using the decision tree, linear discriminant, naive Bayes, SVM and KNN classifiers. The performance of the new approach was tested by evaluating the accuracies of five time-frequency distributions: FT, EMD, DWT, CWT and TQWT. The experiments showed that the RTQWT proposed in this paper can be used to extract detailed features more effectively and improve the classification accuracy of EEG signals.https://ieeexplore.ieee.org/document/10070792/ElectroencephalogramepilepsyQ-factorwavelet transformcharacteristic subspace |
| spellingShingle | Zhen Liu Bingyu Zhu Manfeng Hu Zhaohong Deng Jingxiang Zhang Revised Tunable Q-Factor Wavelet Transform for EEG-Based Epileptic Seizure Detection IEEE Transactions on Neural Systems and Rehabilitation Engineering Electroencephalogram epilepsy Q-factor wavelet transform characteristic subspace |
| title | Revised Tunable Q-Factor Wavelet Transform for EEG-Based Epileptic Seizure Detection |
| title_full | Revised Tunable Q-Factor Wavelet Transform for EEG-Based Epileptic Seizure Detection |
| title_fullStr | Revised Tunable Q-Factor Wavelet Transform for EEG-Based Epileptic Seizure Detection |
| title_full_unstemmed | Revised Tunable Q-Factor Wavelet Transform for EEG-Based Epileptic Seizure Detection |
| title_short | Revised Tunable Q-Factor Wavelet Transform for EEG-Based Epileptic Seizure Detection |
| title_sort | revised tunable q factor wavelet transform for eeg based epileptic seizure detection |
| topic | Electroencephalogram epilepsy Q-factor wavelet transform characteristic subspace |
| url | https://ieeexplore.ieee.org/document/10070792/ |
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