J Med Syst - Epileptic seizure detection using probability distribution based on equal frequency discretization.

Tópicos

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Resumo

In this study, we offered a new feature extraction approach called probability distribution based on equal frequency discretization (EFD) to be used in the detection of epileptic seizure from electroencephalogram (EEG) signals. Here, after EEG signals were discretized by using EFD method, the probability densities of the signals were computed according to the number of data points in each interval. Two different probability density functions were defined by means of the polynomial curve fitting for the subjects without epileptic seizure and the subjects with epileptic seizure, and then when using the mean square error criterion for these two functions, the success of epileptic seizure detection was 96.72%. In addition, when the probability densities of EEG segments were used as the inputs of a multilayer perceptron neural network (MLPNN) model, the success of epileptic seizure detection was 99.23%. This results show that non-linear classifiers can easily detect the epileptic seizures from EEG signals by means of probability distribution based on EFD.

Resumo Limpo

studi offer new featur extract approach call probabl distribut base equal frequenc discret efd use detect epilept seizur electroencephalogram eeg signal eeg signal discret use efd method probabl densiti signal comput accord number data point interv two differ probabl densiti function defin mean polynomi curv fit subject without epilept seizur subject epilept seizur use mean squar error criterion two function success epilept seizur detect addit probabl densiti eeg segment use input multilay perceptron neural network mlpnn model success epilept seizur detect result show nonlinear classifi can easili detect epilept seizur eeg signal mean probabl distribut base efd

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