Classification of Epileptic and Non-Epileptic EEG Events

被引：0

作者：

Pippa, Evangelia ^{[1
]}

Zacharaki, Evangelia I. ^{[1
]}

Mporas, Iosif ^{[1
]}

Megalooikonomou, Vasileios ^{[1
]}

Tsirka, Vasiliki ^{[2
]}

Richardson, Mark ^{[2
]}

Koutroumanidis, Michael ^{[2
]}

机构：

[1] Univ Patras, Dept Comp Engn & Informat, Rion, Greece

[2] Guys & St Thomas & Evelina Hosp Children, Dept Clin Neurophysiol & Epilepsies, London, England

来源：

2014 EAI 4TH INTERNATIONAL CONFERENCE ON WIRELESS MOBILE COMMUNICATION AND HEALTHCARE (MOBIHEALTH) | 2014年

关键词：

epileptic seizures; PNES; vasovagal syncope; classification; machine learning;

D O I：

10.4108/icst.mobihealth.2014.257352

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, the classification of epileptic and non-epileptic events from multi-channel EEG data is investigated using a large number of time and frequency domain features. In contrast to most of the evaluations found in the literature, in this paper the non-epileptic class consists of two types of paroxysmal episodes of loss of consciousness namely the psychogenic non epileptic seizure (PNES) and the vasovagal syncope (VVS). For the classification, several classification algorithms were explored. The classification models were evaluated on EEG epochs from 11 subjects in an inter-subject cross-validation setting and the best among them achieved classification accuracies of 86% (Bayesian Network), 83% (Random Committee) and 74% (Random Forest).

引用

页码：87 / 90

页数：4

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