Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75396
Title: Detection of epileptic seizures in EEG signals with rule-based interpretation by random forest approach
Authors: Wang, GJ 
Deng, ZH
Choi, KS 
Keywords: Seizure detection
EEG Random forest
SVM
Ensemble learning approach
Issue Date: 2015
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2015, v. 9227 LNCS, p. 738-744 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Epilepsy is a common neurological disorder and characterized by recurrent seizures. Although many classification methods have been applied to classify EEG signals for detection of epilepsy, little attention is paid on accurate epileptic seizure detection methods with comprehensible and transparent interpretation. This study develops a detection framework and focuses on doing a comparative study by applying the four rule-based classifiers, i.e., the decision tree algorithm C4.5, the random forest algorithm (RF), the support vector machine (SVM) based decision tree algorithm (SVM + C4.5) and the SVM based RF algorithm (SVM + RF), to two-group and three-group classification and the most challenging five-group classification on epileptic seizures in EEG signals. The experimental results justify that in addition to high interpretability, RF has the competitive advantage for two-group and three-group classification with the average accuracy of 0.9896 and 0.9600. More importantly, its performance is highlighted in five-group classification with the highest average accuracy of 0.8260 in contrast to other three rule-based classifiers.
Description: 11th International Conference on Intelligent Computing, ICIC 2015, Fuzhou, China, August 20-23, 2015
URI: http://hdl.handle.net/10397/75396
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-319-22053-6_78
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

3
Citations as of May 9, 2018

WEB OF SCIENCETM
Citations

2
Citations as of May 26, 2018

Page view(s)

3
Citations as of May 21, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.