Epileptic EEG signal classification based on wavelet packet transform and multivariate multiscale entropy / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 1073-1090, 2013.
Article
in Chinese
| WPRIM
| ID: wpr-352111
ABSTRACT
In this paper, a new method combining wavelet packet transform and multivariate multiscale entropy for the classification of epilepsy EEG signals is introduced. Firstly, the original EEG signals are decomposed at multi-scales with the wavelet packet transform, and the wavelet packet coefficients of the required frequency bands are extracted. Secondly, the wavelet packet coefficients are processed with multivariate multiscale entropy algorithm. Finally, the EEG data are classified by support vector machines (SVM). The experimental results on the international public Bonn epilepsy EEG dataset show that the proposed method can efficiently extract epileptic features and the accuracy of classification result is satisfactory.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Signal Processing, Computer-Assisted
/
Classification
/
Entropy
/
Diagnosis
/
Electroencephalography
/
Epilepsy
/
Wavelet Analysis
/
Methods
Type of study:
Diagnostic study
Limits:
Humans
Language:
Chinese
Journal:
Journal of Biomedical Engineering
Year:
2013
Type:
Article
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