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01-08-2012 | ORIGINAL PAPER

Epileptic Seizure Detection Using Probability Distribution Based On Equal Frequency Discretization

Authors: Umut Orhan, Mahmut Hekim, Mahmut Ozer

Published in: Journal of Medical Systems | Issue 4/2012

Abstract

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.

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Title: Epileptic Seizure Detection Using Probability Distribution Based On Equal Frequency Discretization
Authors: Umut Orhan
Mahmut Hekim
Mahmut Ozer
Publication date: 01-08-2012
Publisher: Springer US
Published in: Journal of Medical Systems / Issue 4/2012
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI: https://doi.org/10.1007/s10916-011-9689-y

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Epileptic Seizure Detection Using Probability Distribution Based On Equal Frequency Discretization

Abstract

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