Developing a Tunable Q-Factor Wavelet Transform Based Algorithm for Epileptic EEG Feature Extraction
| Autor: | Yan Li, Paul Wen, Hadi Ratham Al Ghayab, Siuly, Shahab Abdulla |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
medicine.diagnostic_test
Correlation coefficient Computer science business.industry Speech recognition Feature extraction Wavelet transform Pattern recognition 02 engineering and technology Electroencephalography k-nearest neighbors algorithm 03 medical and health sciences symbols.namesake 0302 clinical medicine Fourier transform 0202 electrical engineering electronic engineering information engineering symbols medicine 020201 artificial intelligence & image processing Artificial intelligence business F1 score Classifier (UML) 030217 neurology & neurosurgery |
| Zdroj: | Health Information Science ISBN: 9783319691817 HIS |
| DOI: | 10.1007/978-3-319-69182-4_6 |
| Popis: | Brain signals refer to electroencephalogram (EEG) data that contain the most important information in the human brain, which are non-stationary and nonlinear in nature. EEG signals are a mixture of sustained oscillation and non-oscillatory transients that are difficult to deal with by linear methods. This paper proposes a new technique based on a tunable Q-factor wavelet transform (TQWT) and statistical method (SM), denoted as TQWT-SM, to analyze epileptic EEG recordings. Firstly, EEG signals are decomposed into different sub—bands by the TWQT method, which is parameterized by its Q-factor and redundancy. This approach depends on the resonance of signals, instead of frequency or scales as the Fourier and wavelet transforms do. Secondly, each type of the sub-band vector is divided into n windows, and 10 statistical features from each window are extracted. Finally all the obtained statistical features are forwarded to a k nearest neighbor (k-NN) classifier to evaluate the performance of the proposed TQWT-SM method. The TQWT-SM features extraction method achieves good experimental results for the seven different epileptic EEG binary-categories by the k-NN classifier, in terms of accuracy (Acc), Matthew’s correlation coefficient (MCC), and F score (F1). The outcomes of the proposed technique can assist the experts to detect epileptic seizures. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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