Discrimination of multi-class EEG signal in phase space of variability for epileptic seizure detection using error correcting output code (ECOC)

Abstract

A new approach for detection of epileptic seizure based on multiclass decision tree classier combining with error correcting output codes (ECOC) using ensemble learning is presented in this paper. The dynamical properties in electroencephalogram (EEG) recorded data of electrical character of the brain during seizure is different from a normal behavior of the brain. Based on the electrical characteristics of the brain, the classification of epileptic seizure in EEG signal is performed in two steps: extraction of feature by first-order difference (FOD) whose variability has been used in 95% confidence ellipse area computation in 2D phase space reconstruction (PSR) and Interquartile range (IQR) of Euclidean distance of FOD plot in 3D PSR; and for classification, bagging algorithm of ensemble learning with decision tree as a base classifier and ECOC is used. This shows a great discriminating ability among datasets belonging to five groups (denoted A–E) at different state of the brain. The proposed method shows 100% classification accuracy.