Detection of epileptiform activity in EEG signals based on time-frequency and non-linear analysis.

Dragoljub Gajic,Jovan Gligorijevic,Stefano Di Gennaro,Zeljko Djurovic,Ivana Savic-Gajic

doi:10.3389/fncom.2015.00038

Abstract

We present a new technique for detection of epileptiform activity in EEG signals. After preprocessing of EEG signals we extract representative features in time, frequency and time-frequency domain as well as using non-linear analysis. The features are extracted in a few frequency sub-bands of clinical interest since these sub-bands showed much better discriminatory characteristics compared with the whole frequency band. Then we optimally reduce the dimension of feature space to two using scatter matrices. A decision about the presence of epileptiform activity in EEG signals is made by quadratic classifiers designed in the reduced two-dimensional feature space. The accuracy of the technique was tested on three sets of electroencephalographic (EEG) signals recorded at the University Hospital Bonn: surface EEG signals from healthy volunteers, intracranial EEG signals from the epilepsy patients during the seizure free interval from within the seizure focus and intracranial EEG signals of epileptic seizures also from within the seizure focus. An overall detection accuracy of 98.7% was achieved.

Highlights

According to the estimations of the World Health Organization around 50 million people worldwide suffer from epilepsy as the most common disorder of the brain activity (World Health Organization, 2012)
In this paper we present an automated technique for detection of epileptiform activity in EEG signals
After the feature extraction we optimally reduce the feature space dimension to two using scatter matrices and perform classification in the reduced feature space by quadratic classifiers which have already been known as very robust solutions for classification of random feature vectors

Summary

Introduction

According to the estimations of the World Health Organization around 50 million people worldwide suffer from epilepsy as the most common disorder of the brain activity (World Health Organization, 2012) It is characterized by sudden and recurrent seizures which are the result of an excessive and synchronous electrical discharge of a large number of neurons. Partial or focal epileptic seizures involve only a circumscribed region of the brain (epileptic focus) and remain restricted to this region while generalized epileptic seizures involve almost the entire brain. Both classes of epileptic seizures can occur at all ages.

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Conclusion