Epileptic Seizure Detection Using Probability Distribution Based On Equal Frequency Discretization

被引:23
作者
Orhan, Umut [1 ]
Hekim, Mahmut [1 ]
Ozer, Mahmut [2 ]
机构
[1] Gaziosmanpasa Univ, Dept Elect & Comp, TR-60250 Tokat, Turkey
[2] Zonguldak Karaelmas Univ, Fac Engn, Dept Elect & Elect Engn, TR-67100 Zonguldak, Turkey
关键词
EEG signals; Epileptic seizure detection; Equal frequency discretization (EFD); Probability distribution; Curve fitting; Mean square error (MSE); Multilayer perceptron neural network (MLPNN); ARTIFICIAL NEURAL-NETWORK; FUZZY INFERENCE SYSTEM; EEG-SIGNALS; WAVELET TRANSFORM; AUTOMATIC RECOGNITION; CONTINUOUS-VARIABLES; ALERTNESS LEVEL; CLASSIFICATION; MODEL;
D O I
10.1007/s10916-011-9689-y
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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.
引用
收藏
页码:2219 / 2224
页数:6
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