Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform

The aim of this study is to detect epileptic seizure in EEG signals using a hybrid system based on decision tree classifier and fast Fourier transform (FFT). The present study proposes a hybrid system with two stages: feature extraction using FFT and decision making using decision tree classifier. T...

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Bibliographic Details
Published in:Applied mathematics and computation Vol. 187; no. 2; pp. 1017 - 1026
Main Authors: Polat, Kemal, Güneş, Salih
Format: Journal Article
Language:English
Published: New York, NY Elsevier Inc 15.04.2007
Elsevier
Subjects:
Calculus of variations and optimal control
Combinatorics
Combinatorics. Ordered structures
Decision tree classifier
Designs and configurations
Electroencephalogram (EEG)
Epileptic seizure
Exact sciences and technology
FFT
k-Fold cross-validation
Mathematical analysis
Mathematics
Numerical analysis
Numerical analysis. Scientific computation
Sciences and techniques of general use
Decision tree classifier
k-Fold cross-validation
Electroencephalogram (EEG)
FFT
Epileptic seizure
Disease
Fast Fourier transformation
Cross validation
Classifier
Decision tree
Depth
Hybrid system
Design
Electrodes
Accuracy
Classification
Evolution
Diagnosis
Intelligent system
Decision making
Cross classification
Sensitivity
Numerical analysis
Applied mathematics
Experimental design
Feature extraction
Detection
Hippocampus
ISSN:0096-3003, 1873-5649
Online Access:Get full text
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Summary:The aim of this study is to detect epileptic seizure in EEG signals using a hybrid system based on decision tree classifier and fast Fourier transform (FFT). The present study proposes a hybrid system with two stages: feature extraction using FFT and decision making using decision tree classifier. The detection of epileptiform discharges in the electroencephalogram (EEG) is an important part in the diagnosis of epilepsy. All data set were obtained from EEG signals of healthy subjects and subjects suffering from epilepsy diseases. For healthy subjects is background EEG (scalp) with open eyes and for epileptic patients correspond to a seizure recorded in hippocampus (epileptic focus) with depth electrodes. The evolution of proposed system was conducted using k-fold cross-validation, classification accuracy, and sensitivity and specificity values. We have obtained 98.68% and 98.72% classification accuracies using 5- and 10-fold cross-validation. The stated results show that the proposed method could point out the ability of design of a new intelligent assistance diagnosis system.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2006.09.022