Classification of low-density EEG for epileptic seizures by energy and fractal features based on EMD
Luis Alfredo Moctezuma
Marta Molinas
1.Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim 7434, Norway2.Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim 7434, Norway
摘要：We 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 algorithm.
机标关键词：
分类号：R742.1(神经病学与精神病学)
论文发表日期：2020-05-30
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：11( 180-190 )
英文信息