Classification of electroencephalogram signals using time-frequency decomposition and linear discriminant analysis

The studies on the classification methods are intensively developed in the recent years. Epilepsy and Alzheimer’s Disease belong to the most common neurological diseases. Automated detection system consists of two key steps: extraction of features from EEG signals and classification for detection of pathology activity. All the segments of the analyzed EEG signals were normalized before the feature extraction. The EEG sequences were analyzed using Short-Time Fourier Transform and the classification was performed using Linear Discriminant Analysis. EEG dataset has been divided into two types: training and testing data. Then, the classifier for new data with unknown classes has been used. The accuracy of the technique was tested on three sets of electroencephalographic (EEG) signals: epilepsy, healthy and Alzheimer’s Disease. The classification error below 10% has been considered a success. In all classification experiments, the highest accuracy can be obtained for new data of unknown classes. The proposed methodology can be helpful for medical practice, especially in differentiation epilepsy seizure and disturbances in the EEG signal in Alzheimer’s Disease.