Multifeatured method for detection and classification of epileptic seizure based on time frequency analysis of EEG signals

Abstract

Epileptic seizure describes a recurrent abnormal but synchronized surge of electrical activity in the brain, which is the second common neurological disease. Signal processing methods try to model visual information into few parameters, which can be easily detected thus decision making becomes more accurate compared to the method based on visual observation of EEG. For seizure detection, the features can be categorized as univariate/bivariate and linear/nonlinear types. But, the use of composite feature set has been limitedly reported. Since, EEG is a non-stationary signal and distribution of its energy demonstrates the seizure activities, time-frequency distribution can perform better than the conventional frequency analysis methods. E ectiveness of time-frequency based feature extraction depends on the choice of a kernel and its processing time. Moreover, development of a multifeatured set capable of detecting and classifying epileptic seizure originated from di erent parts and state of the brain is still a challenging problem. Prior to feature extraction, pre-processing involving Hilbert transform is performed. For the preprocessed EEG signal, time-frequency distributions (TFDs) are obtained by using twelve Cohen Class kernels capable of reducing in uence of cross-terms. The TFDs are examined to nd out nonuniform modules corresponding to dominant bands, namely ; ; ; and components and to form a feature set containing modular cumulative energy at percentile frequencies and modular entropy. This feature set when fed to each of the decision tree, KNN and ANN classi er, can produce greater detection accuracy independent of the kernels and o ers lesser processing time. But, it is unable to classify epileptic seizure originated from di erent parts and state of the brain. Therefore, a high number of uniform modules are formed to compute a mutifeatured set containing modular energy and entropy which is found e ective in detecting as well as classifying epileptic seizure originated from ve di erent parts and state of the brain. Simulations are carried out using standard EEG dataset to evaluate the performance of the proposed method in terms of selectivity, sensitivity and accuracy. It is shown that the proposed method outperforms a state-of-the-art method with superior e cacy.