A NEW PARAMETRIC FEATURE DESCRIPTOR FOR THE CLASSIFICATION OF EPILEPTIC AND CONTROL EEG RECORDS IN PEDIATRIC POPULATION

被引：42

作者：

Cabrerizo, Mercedes ^{[1
]}

Ayala, Melvin ^{[1
]}

Goryawala, Mohammed ^{[1
]}

Jayakar, Prasanna ^{[2
]}

Adjouadi, Malek ^{[1
]}

机构：

[1] Florida Int Univ, Ctr Adv Technol & Educ, Coll Engn & Comp, Miami, FL 33174 USA

[2] Miami Childrens Hosp, Inst Brain, Miami, FL 33155 USA

来源：

INTERNATIONAL JOURNAL OF NEURAL SYSTEMS | 2012年 / 22卷 / 02期

基金：

美国国家科学基金会;

关键词：

EEG data; temporal and frequency parameters; artificial neural networks; support vector machines; EEG classification; epileptic versus control EEG; NONLINEAR DECISION FUNCTIONS; SPIKING NEURAL-NETWORKS; SEIZURE DETECTION; SIGNALS; TIME; IDENTIFICATION; COHERENCE;

D O I：

10.1142/S0129065712500013

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This study evaluates the sensitivity, specificity and accuracy in associating scalp EEG to either control or epileptic patients by means of artificial neural networks (ANNs) and support vector machines (SVMs). A confluence of frequency and temporal parameters are extracted from the EEG to serve as input features to well-configured ANN and SVM networks. Through these classification results, we thus can infer the occurrence of high-risk (epileptic) as well as low risk (control) patients for potential follow up procedures.

引用

页数：16

共 35 条

[1] APPLICATION OF RECURRENCE QUANTIFICATION ANALYSIS FOR THE AUTOMATED IDENTIFICATION OF EPILEPTIC EEG SIGNALS [J].