Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection

• Published in: Expert systems with applications Vol. 38; no. 11; pp. 14314 - 14320
• Main Authors: Wang, Deng, Miao, Duoqian, Xie, Chen
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2011
• Subjects: Accuracy; Classification; Diseases; Electroencephalogram (EEG); Entropy; Epileptic detection; Feature extraction; Hierarchical knowledge base; Knowledge bases (artificial intelligence); Wavelet; Wavelet packet entropy; Feature extraction; Epileptic detection; Electroencephalogram (EEG); Hierarchical knowledge base; Wavelet packet entropy
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2011.05.096

► Best basis-based wavelet packet entropy feature extraction. ► Hierarchical knowledge base construction. ► EEG classification system. In this study, a hierarchical electroencephalogram (EEG) classification system for epileptic seizure detection is proposed. The system includes the following three stages: (i) original EEG signals representation by wavelet packet coefficients and feature extraction using the best basis-based wavelet packet entropy method, (ii) cross-validation (CV) method together with k-Nearest Neighbor ( k-NN) classifier used in the training stage to hierarchical knowledge base (HKB) construction, and (iii) in the testing stage, computing classification accuracy and rejection rate using the top-ranked discriminative rules from the HKB. The data set is taken from a publicly available EEG database which aims to differentiate healthy subjects and subjects suffering from epilepsy diseases. Experimental results show the efficiency of our proposed system. The best classification accuracy is about 100% via 2-, 5-, and 10-fold cross-validation, which indicates the proposed method has potential in designing a new intelligent EEG-based assistance diagnosis system for early detection of the electroencephalographic changes.