Wavelet Based Features for Classification of Normal, Ictal and Interictal EEG Signals

作者：Fathima Thasneem^*; Bedeeuzzaman M; Joseph Paul K

来源：Journal of Medical Imaging and Health Informatics, 2013, 3(2): 301-305.

DOI：10.1166/jmihi.2013.1161

• Electroencephalogram
• Wavelet Transform
• Inter Quartile Range
• Seizure Detection

Electroencephalogram (EEG) is the major diagnostic tool used for analyzing the human epileptic seizure activity and there is a strong need of an efficient automatic seizure detection using it to ease the diagnosis. In this paper a method of classification of EEG signals using wavelet based features is presented. The wavelet decomposition was done up to fourth level, followed by the calculation of inter quartile range (IQR), an important statistical feature, over third and fourth level wavelet coefficients. The methodology was applied to five types of EEG signals: healthy subjects (eyes open and eyes closed), epileptic subjects during seizure free interval (interictal EEG from epileptogenic zone and opposite hemisphere of epileptogenic zone) and epileptic subjects during a seizure (ictal EEG). A linear classifier trained on these features could classify normal and ictal EEG signals with 100% sensitivity and specificity. The overall accuracy obtained for five classes was 95.6%.

• 出版日期2013-6