Open AccessReal‐time mining of epileptic seizure precursors via nonlinear mapping and dissimilarity features
Author(s) -
Nesaei Sahar,
Sharafat Ahmad R.
Publication year - 2015
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2013.0297
Subject(s) - epilepsy , epileptic seizure , computer science , nonlinear system , pattern recognition (psychology) , artificial intelligence , data mining , neuroscience , psychology , physics , quantum mechanics
We propose a novel approach for detecting precursors to epileptic seizures in intracranial electroencephalograms ( i EEGs), 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.