Epilepsy is a neurological disorder characterized by the existence of recurring seizures. Like many other neurological disorders, epilepsy can be appraisal by the electroencephalogram (EEG). The EEG signal is highly non-linear and non-stationary and consist of lot of data including significant data and artifact and hence, it is practically arduous to characterize and interpret it. However, it is a well-established clinical technique with low associated costs for detection of various neurological disorders. In this work, we propose a methodology for the automatic detection of normal, epilepsy and brain death from recorded EEG signals collected from clinic. Discrete wavelet transform is applied for feature extraction. Back Propagation neural network optimized by particle swarm optimization is used for classification of neurological disorders. Simple BPNN has several drawbacks which mainly include large time duration during EEG signal classification. This drawback is removed by PSO. In this paper, the proposed method used to detect the number of neurons in hidden layer of BPNN using optimization technique of PSO. Once the numbers of neurons in hidden layers are detected, optimum value for initial weights and biases for BPNN estimated which is further used for classification and sortilege of various neurological disorders. So that time duration decreases and accuracy increases. EEG signals are recorded for 30 minutes of three different patients that are epileptic, normal and brain death used to rehearse and test the proposed algorithm. A signal used to test is integrated signal by taking mean of 16 channels. By applying techniques to signals of epilepsy or normal or brain death patient which are known to us we find the more accurate results with less number of iteration and time .

No abstract

No abstract

No abstract

No abstract