Apr 25, 2014

Multiband entropy-based feature-extraction method for automatic identification of epileptic focus based on high- frequency components in interictal iEEG

BioRxiv : the Preprint Server for Biology
Sacha LaurentAndrzej Cichocki

Abstract

Presurgical investigations for categorizing focal patterns are crucial, leading to localization and surgical removal of the epileptic focus. This paper presents a machine learning approach using information theoretic features extracted from high-frequency subbands to detect the epileptic focus from interictal intracranial electroencephalogram (iEEG). It is known that high-frequency subbands (>80 Hz) include important biomarkers such as high-frequency oscillations (HFOs) for identifying epileptic focus commonly referred to as the seizure on- set zone (SOZ). In this analysis, the multi-channel interictal iEEG signals were splitted into segments and each segment was decomposed into multiple high-frequency subbands. Thedifferent types of entropy were calculated for each of the subbands and the sparse linear dis- criminant analysis (sLDA) was applied to select the prominent entropy features. Due to the imbalance of SOZ and non-SOZ channels in iEEG data, the use of machine learning tech- niques is always tricky. To deal with the imbalanced learning problem, an adaptive synthetic oversampling approach (ADASYN) with radial basis function kernel-based SVM was used to detect the focal segments. Finally, the epileptic focus was identified...Continue Reading

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Mentioned in this Paper

Laboratory Procedures
Species
Patterns
Simulation

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