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dc.contributor.authorMoctezuma, Luis Alfredo
dc.contributor.authorMolinas Cabrera, Maria Marta
dc.description.abstractWe are here to present a new method for the classification of epileptic seizures from electroencephalogram (EEG) signals. It consists of applying empirical mode decomposition (EMD) to extract the most relevant intrinsic mode functions (IMFs) and subsequent computation of the Teager and instantaneous energy, Higuchi and Petrosian fractal dimension, and detrended fluctuation analysis (DFA) for each IMF. We validated the method using a public dataset of 24 subjects with EEG signals from 22 channels and showed that it is possible to classify the epileptic seizures, even with segments of six seconds and a smaller number of channels (e.g., an accuracy of 0.93 using five channels). We were able to create a general machine-learning-based model to detect epileptic seizures of new subjects using epileptic-seizure data from various subjects, after reducing the number of instances, based on the k-means algorithmen_US
dc.rightsNavngivelse-Ikkekommersiell-DelPåSammeVilkår 4.0 Internasjonal*
dc.subjectepileptic seizure, electroencephalograms, empirical mode decomposition, detrended fluctuation analysis, energy distribution, fractal dimensionen_US
dc.titleClassification of low-density EEG-based epileptic seizures by energy and fractal features based on EMDen_US
dc.typeJournal articleen_US
dc.source.journalJournal of Biomedical Researchen_US
cristin.unitnameInstitutt for teknisk kybernetikk

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Navngivelse-Ikkekommersiell-DelPåSammeVilkår 4.0 Internasjonal
Med mindre annet er angitt, så er denne innførselen lisensiert som Navngivelse-Ikkekommersiell-DelPåSammeVilkår 4.0 Internasjonal