The seizure is a transient abnormal behavior of neurons within one or several neural networks, which limits the patients physical and mental activities. Since conventional time or frequency domain analysis is found inadequate to describe the characteristics of a non-stationary signal, such as electroencephalography (EEG), in this Matlab design, EEG data is transformed using twelve Cohen class kernel functions in order to facilitate the time-frequency analysis. The transformed data thus obtained is exploited to formulate a feature vector consists of modular energy and modular entropy that can better model the time-frequency behavior of the EEG data. The feature vector is fed to an Artificial Neural Network (ANN) classifier in order to classify epileptic seizure data originating from different parts and state of the brain. The proposed EEG based epileptic seizure classification method consists of some major steps, namely, pre-processing, time-frequency analysis, feature extraction (2D-DFT and FFT) and classification. In the classification, we consider five classes of epileptic seizure data, namely Z, O, N, F and S. Several simulations are carried out using a benchmark EEG data-set. The comparison is done between two methods 2D-DFT and FFT that obtained by using a state-of-the-art method of epileptic seizure classification using the same EEG data-set and classifier.

Reference Paper: Multiclass Epileptic Seizure Classification Using Time-Frequency Analysis of EEG Signals

Author’s Name: Partha Pratim Acharjee, and Celia Shahnaz

Source: IEEE


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SIMULATION VIDEO DEMO-2D DFT                                                                                                                                     

SIMULATION VIDEO DEMO-FFT