Classification of low-density EEG for epileptic seizures by energy and fractal features based on EMD.

Luis Alfredo Moctezuma,Marta Molinas

doi:10.7555/jbr.33.20190009

Abstract

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.

Highlights

Humans of all ages and both sexes may experience sudden seizures, presenting as electrical discharges of a set of neurons inside the brain
The epileptogenic focus in approximately 33% of epilepsy patients is located in the temporal lobe[4] and their condition is referred to as temporal-lobe epilepsy (TLE)[5]
We performed classification experiments using the characterized EEG signals and reduction of the number of instances based on clustering and developed and tested a method for creating a general model to detect epileptic seizures

Summary

Introduction

Humans of all ages and both sexes may experience sudden seizures, presenting as electrical discharges of a set of neurons inside the brain. Such unprovoked seizures represent a widespread disorder, known as epilepsy, and affect people around the world[1]. The epileptiform can be categorized into ictal, interictal, and postictal periods, which are often time-consuming to identify by visual inspection. This may result in an incorrect interpretation of the EEG signals and inappropriate management of patients (caused by under/over medication)[2]. The epileptogenic focus in approximately 33% of epilepsy patients is located in the temporal lobe[4] and their condition is referred to as temporal-lobe epilepsy (TLE)[5]

Methods

Results

Conclusion