NDLI: Automated Prediction of Epileptic Seizures in Rats with Recurrence Quantification Analysis

Content Provider	IEEE Xplore Digital Library
Author	Gaoxiang Ouyang Lijuan Xie Huanwen Chen Xiaoli Li Xinping Guan Huihua Wu
Copyright Year	2005
Description	Author affiliation: Inst. of Electr. Eng., Yanshan Univ., Hebei (Gaoxiang Ouyang)
Abstract	The prediction of epileptic seizures is a very important issue in the neural engineering. This is because it may improve the life quality of the patients who are suffering from uncontrolled epilepsy. In our earlier work, we found that the dynamical characteristics of EEG data with recurrence quantification analysis (RQA), also called complexity measure, can identify the differences among inter-ictal, pre-ictal and ictal phases. In this paper, we propose an automated technique with complexity measure of EEG recording to detect pre-ictal phase. Using the EEG recorded from rats with experimentally induced generalized epilepsy, it is found the method can detect the complexity changes of the neural activity prior to epileptic seizures. We suggest that the new method could be considered as an alternative of epileptic seizures prediction in practice
Starting Page	153
Ending Page	156
File Size	255964
Page Count	4
File Format	PDF
ISBN	0780387414
DOI	10.1109/IEMBS.2005.1616365
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-01-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Epilepsy Rats Electroencephalography Phase measurement Phase detection Chaotic communication Prediction methods Neural engineering Medical treatment Data mining
Content Type	Text
Resource Type	Article

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