Abstract:Aiming at the problem of low accuracy and classification of epileptic EEG in medical diagnosis

a signal classification and detection technique based on particle swarm optimization (PSO) was proposed to optimize the support vector machine (SVM) based on the theory of particle swarm optimization and support vector machine (SVM).Firstly

the EEG signals were decomposed and reconstructed by wavelet analysis.Secondly

the coefficients of fluctuation and approximate entropy of the reconstructed signals containing the functional parameters of epilepsy were extracted. Finally

The support vector machine (SVM) optimized by particle swarm optimization (PSO) is used to classify the EEG signals. The experimental results show that the this method can correctly identify three types of EEG signals in healthy

interictal epilepsy and epileptic seizures

the final recognition rate can reach 99.83%.