Detection of Epileptic Seizures using Wavelet Coefficients, Artificial Neural Networks (ANNs) and Particle Swarm Optimization (PSO)

Electroencephalogram signals (EEGs) show the electrical activity of brain neurons. EEG is a non-invasive method that can be used to detect abnormal brain activities. Seizure is one of these abnormal activities and is the most common manifestation of epilepsy. Spikes are the most important characteristic of the seizure prone EEG signals. By detecting spikes, it is possible to detect epileptic seizures from EEG signals. EEG signals are non-stationary signals, so the wavelet transform that has appropriate time and frequency resolution can be a good option for extracting features of EEG signals. In this paper, after the extraction process using wavelet transform, artificial neural networks (ANNs) are used to classify healthy and epileptic signals. Particle swarm optimization (PSO) is also used as a novel approach to select weights and biases of network to improve network performance. The results of the implementation of the proposed algorithm have a 96.2% accuracy, which shows acceptable performance compared to existing methods.