Epileptic Seizure Detection Using Probability Distribution Based On Equal Frequency Discretization

Orhan U., Hekim M., Ozer M.

JOURNAL OF MEDICAL SYSTEMS, cilt.36, sa.4, ss.2219-2224, 2012 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 36 Sayı: 4
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1007/s10916-011-9689-y
  • Dergi Adı: JOURNAL OF MEDICAL SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2219-2224
  • Anahtar Kelimeler: 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
  • Çukurova Üniversitesi Adresli: Hayır

Özet

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.