NDLI: Epileptic Seizure Detection Using Probability Distribution Based On Equal Frequency Discretization

Content Provider	Springer Nature Link
Author	Orhan, Umut Hekim, Mahmut Ozer, Mahmut
Copyright Year	2011
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.
Starting Page	2219
Ending Page	2224
Page Count	6
File Format	PDF
ISSN	01485598
Journal	Journal of Medical Systems
Volume Number	36
Issue Number	4
e-ISSN	1573689X
Language	English
Publisher	Springer US
Publisher Date	2011-03-29
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	EEG signals Epileptic seizure detection Equal frequency discretization (EFD) Probability distribution Curve fitting Mean square error (MSE) Multilayer perceptron neural network (MLPNN) Statistics for Life Sciences, Medicine, Health Sciences Health Informatics
Content Type	Text
Resource Type	Article
Subject	Health Information Management Information Systems Health Informatics

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