Detection of Epileptic Seizure from EEG Signals by Using Recurrence Quantification Analysis

Kutlu F., KÖSE C.

22nd IEEE Signal Processing and Communications Applications Conference (SIU), Trabzon, Turkey, 23 - 25 April 2014, pp.1387-1390 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/siu.2014.6830497
  • City: Trabzon
  • Country: Turkey
  • Page Numbers: pp.1387-1390
  • Karadeniz Technical University Affiliated: Yes


The pre-diagnosis of diseases with computerized systems is widely used in recent years for reducing diagnosis time and ratio of misdiagnosis. In this study, a pre-diagnosis system has been proposed which separates of healthy and epileptic seizures periods. For the experiments, EEG signals acquired from healthy and epileptic individuals were used. In feature extraction stage, recurrence quantification analysis (RQA); in classification stage, support vector machines (SVM), multilayer perceptron neural networks (MLPNN) and Naive Bayes classifiers have been utilized. Accordingly, in case of using MLPNN, 96.67% classification performance was obtained.