Epileptic Seizure Detection Using Probability Distribution Based On Equal Frequency Discretization

Department of Electronics and Computer, Gaziosmanpasa University, Tokat, Turkey.
Journal of Medical Systems (Impact Factor: 2.21). 03/2011; 36(4):2219-24. DOI: 10.1007/s10916-011-9689-y
Source: PubMed


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.

45 Reads
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We introduced a multilayer perceptron neural network (MLPNN) based classification model as a diagnostic decision support mechanism in the epilepsy treatment. EEG signals were decomposed into frequency sub-bands using discrete wavelet transform (DWT). The wavelet coefficients were clustered using the K-means algorithm for each frequency sub-band. The probability distributions were computed according to distribution of wavelet coefficients to the clusters, and then used as inputs to the MLPNN model. We conducted five different experiments to evaluate the performance of the proposed model in the classifications of different mixtures of healthy segments, epileptic seizure free segments and epileptic seizure segments. We showed that the proposed model resulted in satisfactory classification accuracy rates.
    Expert Systems with Applications 09/2011; 38(10):13475-13481. DOI:10.1016/j.eswa.2011.04.149 · 2.24 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: This study concentrates on detection of the epileptic activities in the electroencephalogram (EEG) signals. For this aim, features are extracted from the EEG signals by using first wavelet transform and then the approach of densities based on equal frequency discretization, and these features are classified by using support vector machines. The obtained results are compared with the results of three different studies. The results show that the feature extraction method used improves the classification success rate and SVM obtains the highest classification success rate possible in faster running time.
    Innovations in Intelligent Systems and Applications (INISTA), 2012 International Symposium on; 07/2012
  • [Show abstract] [Hide abstract]
    ABSTRACT: In Intelligent Tutoring Systems, affect-based computing is an important research area. Common approaches to deal with the affective state identification are based on input data from external sensors such as eye-tracker and EEG, as well as methods based on mining of ITS log data. Sensor based methods are viable in laboratory settings but they are tough to implement in real-world scenario which might cater to a large number of students. In our research, we create a mathematical model of frustration based on its theoretical definition. We identify the variables in the model by applying the theoretical definition of frustration to the ITS log data. This approach is different from existing data mining techniques, which use correlation analysis with labeled data. We apply our model to Mindspark, a commercial maths Intelligent Tutoring System, used by several thousand students. We validate our model with human observations of frustration.
    Advanced Learning Technologies (ICALT), 2012 IEEE 12th International Conference on; 07/2012
Show more