Real-time mining of epileptic seizure precursors via nonlinear mapping and dissimilarity features
- Author(s): Sahar Nesaei 1 and Ahmad R. Sharafat 1
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Affiliations:
1:
Department of Electrical and Computer Engineering, Tarbiat Modares University, P.O. Box 14115-194, Tehran, Iran
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Affiliations:
1:
Department of Electrical and Computer Engineering, Tarbiat Modares University, P.O. Box 14115-194, Tehran, Iran
- Source:
Volume 9, Issue 3,
May 2015,
p.
193 – 200
DOI: 10.1049/iet-spr.2013.0297 , Print ISSN 1751-9675, Online ISSN 1751-9683
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© The Institution of Engineering and Technology
Received
16/01/2013,
Accepted
12/05/2014,
Revised
10/01/2014,
Published
17/04/2015
Errata or comment
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.
Inspec keywords: medical signal detection; electroencephalography; support vector machines; entropy; Lyapunov methods; wavelet transforms; biomedical transducers; signal classification
Other keywords: epileptic seizure prediction; decision function; EEG signal segmentation; wavelet entropy; intracranial electroencephalograms; time frame detection; LLE; system dynamics analysis; nonlinear analysis; largest Lyapunov exponent; precursor detection; iEEG; Freiburg data set; support vector machine classifier
Subjects: Knowledge engineering techniques; Patient diagnostic methods and instrumentation; Function theory, analysis; Digital signal processing; Signal detection; Integral transforms; Electrodiagnostics and other electrical measurement techniques; Biology and medical computing; Integral transforms; Bioelectric signals
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