Predicting Epileptic Seizure by Recurrence Quantification Analysis of Single-Channel EEG

Authors:
Tianqiao Zhu;Liyu Huang;Shouhong Zhang;Yuangui Huang
Affiliations:
School of Electronic Engineering, Xidian University, Xi'an, China 710071;School of Electronic Engineering, Xidian University, Xi'an, China 710071;School of Electronic Engineering, Xidian University, Xi'an, China 710071;Xijing Hospital, The Fourth Military Medical University, Xi'an, China 710032
Venue:
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Year:
2008

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Abstract

This paper presents a promising approach, based on the combination of two different approaches, including recurrence quantification analysis(RQA) of single-channel EEG and artificial neural network (ANN), which predicts seizure onsets in 17 consenting patients with generalized epilepsy. Eight channels of EEG were collected in each patient in Epilepsy Center of Xijing Hospital. The seven measures were extracted from the raw EEGs by RQA, and then a four layer(7-6-2-1) ANN was employed for prediction, using the measures as the inputs.The performance obtained for the proposed scheme in predicting succedent seizures is: sensitivity 50~72.2%, specificity 61.9~73.8% and accuracy 58.3~70%, depending on the different EEG leads.