q factor of low frequencies is lower & the q factor higher in high frequencies than controls, thus the amplitude is higher.
https://www.frontiersin.org/articles/10.3389/fnins.2020.00196/full
we propose a novel method for automatic epileptic EEG identification based upon dual Q-factor signal decomposition derived from tunable Q-factor wavelet transform (TQWT), fast and adaptive multivariate empirical mode decomposition (FA-MVEMD) and neural networks. First, EEG signals are decomposed into high- and low-resonance components using both high-Q-factor and low-Q-factor TQWT jointly. Second, FA-MVEMD decomposes the high- and low-Q-factor components into scale-aligned intrinsic mode components (IMFs). The first two IMFs of the high- and low-Q-factor components are extracted, which contain most of the EEG signals’ energy and are considered to be the predominant IMFs. The properties associated with the nonlinear EEG system dynamics are preserved in the predominant IMFs which demonstrate significant difference in EEG system dynamics between normal (healthy), interictal and ictal EEG signals. Third, neural networks are then used to model, identify and classify EEG system dynamics with the predominant IMFs as the input. Finally, experiments on Bonn EEG database are included to assess the effectiveness of the proposed method. The overall average accuracy for binary classification, ternary classification and five-class classification is reported to be \(98.54\%\), \(99.18\%\) and \(97.84\%\), respectively, by using tenfold cross-validation. In comparison with other state-of-the-art methods, our algorithm illustrates superior performance, which can serve as a potential candidate for the automatic EEG seizure detection in the clinical application
Revised Tunable Q-Factor Wavelet Transform for EEG-Based Epileptic Seizure Detection
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10070792
RTQWT with DT.
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
Jimi Mased 