DocumentCode :
56849
Title :
Real-time mining of epileptic seizure precursors via nonlinear mapping and dissimilarity features
Author :
Nesaei, Sahar ; Sharafat, Ahmad R.
Author_Institution :
Dept. of Electr. & Comput. Eng., Tarbiat Modares Univ., Tehran, Iran
Volume :
9
Issue :
3
fYear :
2015
fDate :
5 2015
Firstpage :
193
Lastpage :
200
Abstract :
We propose a novel approach for detecting precursors to epileptic seizures in intracranial electroencephalograms (iEEGs), which is based on the analysis of system dynamics. In the proposed scheme, the largest Lyapunov exponent (LLE) of wavelet entropy of the segmented EEG signals are considered as the discriminating features. Such features are processed by a support vector machine classifier, whose outcomes (the label and its probability for each LLE) are post-processed and fed into a novel decision function to determine whether the corresponding segment of the EEG signal contains a precursor to an epileptic seizure. The proposed scheme is applied to the Freiburg data set, and the results show that seizure precursors are detected in a time frame that unlike other existing schemes is very much convenient to patients, with the sensitivity of 100% and negligible false positive detection rates.
Keywords :
Lyapunov methods; biomedical transducers; electroencephalography; entropy; medical signal detection; signal classification; support vector machines; wavelet transforms; EEG signal segmentation; Freiburg data set; LLE; decision function; epileptic seizure prediction; iEEG; intracranial electroencephalograms; largest Lyapunov exponent; nonlinear analysis; precursor detection; support vector machine classifier; system dynamics analysis; time frame detection; wavelet entropy;
fLanguage :
English
Journal_Title :
Signal Processing, IET
Publisher :
iet
ISSN :
1751-9675
Type :
jour
DOI :
10.1049/iet-spr.2013.0297
Filename :
7103403
Link To Document :
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