A new approach for automatic detection of focal EEG signals using wavelet packet decomposition and quad binary pattern method

Abstract

A comprehensive feature representation for electroencephalogram (EEG) signal to achieve effective epileptic focus localization using a one-dimensional quad binary pattern (QBP) is proposed in this work. The wavelet packet decomposition (WPD), entropies, and QBP methods are applied to EEG signals for identifying the non-focal and focal classes. The proposed approach consists of three strategies, based on the local pattern transformation of EEG signals using QBP. The time-domain EEG signals are transformed into the QBP domain, and histogram features are extracted in the first strategy. Secondly, the nonlinear features like sample entropy, log energy entropy, fuzzy entropy, permutation entropy, and approximate entropy are computed from the EEG signals and concatenated with the QBP features. In the third strategy, the EEG signals are decomposed into sub-bands by employing WPD, and histogram features based on QBP are extracted from each sub-band. The computed feature sets are used to classify the non-focal and focal EEG signals using an artificial neural network (ANN). For the selection of best settings in WPD, the EEG signals are decomposed in seven commonly used wavelet families with different decomposition levels, and the wavelet family providing the highest classification accuracy with the minimal computational cost is found out. The proposed approach is validated using the widely recognized Bern-Barcelona EEG dataset. The proposed method achieved a classification accuracy of 95.74 % on this dataset using the proposed WPD based QBP approach. In conclusion, the proposed approach is efficient in identifying the focal EEG signals and can be useful for accurate detection of focal regions in the epilepsy diagnosis.