Objective: The objective of this work is to develop efficient classification systems using intelligent computing techniques for classification of normal and abnormal EEG signals.
Methods: In this work, EEG recordings were carried out on volunteers (N=170). The features for classification of clinical EEG signals were extracted using wavelet transform and the feature selection was carried out using Principal Component Analysis. Intelligent techniques like Back Propagation Network (BPN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Particle Swarm Optimization Neural network (PSONN) and Radial Basis function Neural network (RBFNN) were trained for diagnosing seizures. Further, the performance of the developed classifiers was compared.
Results: Results demonstrate that RBFNN classifies normal and abnormal EEG signals better than the other methods. It appears that the RBFNN is able to detect Generalized Tonic-Clonic Seizure (GTCS) more efficiently than the Complex Partial Seizures (CPS). Positive predictive value was better in PSONN and ANFIS than BPN method.
Conclusions: It appears that the combination of Wavelet transform method and PCA derived features along with RBFNN classifier is efficient for automated EEG signal classification