Effect of tuning TQWT parameters on epileptic seizure detection from EEG signals
| Autor: | Eman A. Abdel-Ghaffar |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
medicine.diagnostic_test
Computer science business.industry Feature extraction Wavelet transform 020206 networking & telecommunications Pattern recognition 02 engineering and technology Electroencephalography Signal 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Epileptic seizure Artificial intelligence medicine.symptom business |
| Zdroj: | 2017 12th International Conference on Computer Engineering and Systems (ICCES). |
| Popis: | In this paper, we study the effect of tuning the tunable-Q wavelet transform (TQWT) parameters on analyzing the Electroencephalogram (EEG) signals used for detecting epileptic seizure. Publicly available Bonn University database is used in this study, fifteen different combinations were examined. TQWT is used to decompose each EEG signal into a valuable set of band limited signals (sub-bands), the value of the Q parameter is tuned from one to four and the number of sub-bands (J) from six to twenty two. Two statistical features were extracted from the sub-bands having the highest percentage of total signal energy. K-nearest neighbor (K-NN) was used for classifying the EEG signals into either seizure or seizure-free. Our results clarify that, increasing the value of Q enhance the classification accuracy and best results were achieved at Q equals two. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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