An adaptive method for feature selection and extraction for classification of epileptic EEG signal in significant states

Electroencephalography (EEG) is a measurement tool to measure the electrical activity of brain observed due to chemical variation in brain. The EEG analysis has important role in feature extraction and classification methods for detecting and predicting various brain diseases. Epilepsy is a major disease characterized by seizures and observed due to sudden abnormal electrical discharges in the brain. Mostly the research is carried out to classify the normal EEG signal from epileptic EEG signal. The objective of the paper is to identify pre-seizure state and seizure state of EEG signal using time and frequency features. Classifying these states of EEG signal using fuzzy classifier helps in predicting seizures. The methodology uses hypothetical testing to refine feature selection and pattern adapted wavelet transform to improve classification. The appropriate feature selection reduces computational complexity of the classifier. The artifacts are removed from the EEG signal using Independent Component Analysis (ICA). The CHB-MIT EEG scalp dataset from Children’s Hospital, Boston is used for experimentation. The result shows classification accuracy of 96.48 %, True Positive Rate 96.52% and False Positive Rate 0.352 for seizure detection and 96.02% accuracy for Pre-seizure state detection with 13–110 s earlier than the onset seizures.