EEG signal classification using PSO trained RBF neural network for epilepsy identification

Abstract

The electroencephalogram (EEG) is a low amplitude signal generated in the brain, as a result of information flow during the communication of several neurons. Hence, careful analysis of these signals could be useful in understanding many human brain disorder diseases. One such disease topic is epileptic seizure identification, which can be identified via a classification process of the EEG signal after preprocessing with the discrete wavelet transform (DWT). To classify the EEG signal, we used a radial basis function neural network (RBFNN). As shown herein, the network can be trained to optimize the mean square error (MSE) by using a modified particle swarm optimization (PSO) algorithm. The key idea behind the modification of PSO is to introduce a method to overcome the problem of slow searching in and around the global optimum solution. The effectiveness of this procedure was verified by an experimental analysis on a benchmark dataset which is publicly available. The result of our experimental analysis revealed that the improvement in the algorithm is significant with respect to RBF trained by gradient descent and canonical PSO. Here, two classes of EEG signals were considered: the first being an epileptic and the other being non-epileptic. The proposed method produced a maximum accuracy of 99% as compared to the other techniques.

An Effective Fuzzy Neural Network with Reinforcement Learning Approach for Medical Data Classification

“…For a given policy, the Bellman expectation equation is used to estimate the state-value function in some simple cases, which is mathematically explained in Eq. (5).…”

“…The manual practice is rapidly overcome by using the digitization of clinical procedure in the medical domain. Nowadays, medical specialists consider the promising application like Computer Aided Diagnosis (CAD) due to early processing and their effective handling of identification of diseases, however it is facing several challenges [4,5]. But, the important issue is to handle the appropriate medical records, which will help the doctors and technologist to predict the diseases rapidly and further proceed for a speedy recovery.…”

PSR-based research of feature extraction from one-second EEG signals: a neural network study

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“…The accuracy of NN depends on its topology and the quality of input data. Currently, there are many results from multichannel EEG mapping, where spatial and temporal data of EEG signals are studied, mainly for applications in neurology [19][20][21]. One of the good examples may be the work of Jiao et al In which the pattern recognition methods were used to classify EEG signals coming from working memory during cognitive tasks [22].…”

A Method for Fast Establishing Tropospheric Refractivity Profile Model Based on Radial Basis Function Neural Network

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