A hybridised neural network and optimisation algorithms for prediction and classification of neurological disorders Online publication date: Tue, 06-Nov-2018
by Pravin R. Kshirsagar; Sudhir G. Akojwar; Nidhi D. Bajaj
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 28, No. 4, 2018
Abstract: This work introduces a hybrid model of Artificial Neural Network and Particle Swarm Optimisation (PSO) algorithm for the classification and prediction of various neurological disorders. The proposed algorithm is capable of performing classification and prediction of neurological diseases based on the EEG signal input. Here Probabilistic Neural Network (PNN) is used as it is very efficient for classification purposes. Classification results are verified by using tenfold cross-validation technique. Prediction is performed by using modified PSO. The EEG database is obtained from CIIMS Hospital, Nagpur. The results are highly reliable with graphs for predicted signal and prediction error. Percentage of accuracy, sensitivity and mean squared error are calculated as well. With the help of this system classification of EEG signals can now be easily done with accuracy greater than 99% and in a short span of time.
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