Seizure detection in clinical EEG based on multi-feature integration and SVM

Authors:
Shanshan Chen;Qingfang Meng;Weidong Zhou;Xinghai Yang
Affiliations:
School of Information Science and Engineering, University of Jinan, Jinan, China,Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, China;School of Information Science and Engineering, University of Jinan, Jinan, China,Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, China;School of Information Science and Engineering, Shandong University, Jinan, China;School of Information Science and Engineering, University of Jinan, Jinan, China,Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, China
Venue:
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
Year:
2013

Citing 2
Cited 0

Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy

Expert Systems with Applications: An International Journal
Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recurrence Quantification Analysis (RQA) was a nonlinear analysis method and widely used to analyze EEG signals. In this work, a feature extraction method based on the RQA measures was proposed to detect the epileptic EEG from EEG recordings. To combine the time-frequency characteristic of epileptic EEG, variation coefficient and fluctuation index were used to analyze epileptic EEG. The multi-feature combination of RQA and linear parameters had better performance in analyzing the nonlinear dynamic characteristics and time-frequency characteristic of epileptic EEG. For features selection and improving the classification accuracy, a support vector machine (SVM) classifier was used. The experimental results showed that the proposed method could classify the ictal EEG and interictal EEG with accuracy of 97.98%.