Prediction of Epileptic Seizure by Analysing Time Series EEG Signal Using k-NN Classifier.

Md Kamrul Hasan,M A Rashid,Mohiuddin Ahmad,Md Asif Ahamed

doi:10.1155/2017/6848014

Abstract

Electroencephalographic signal is a representative signal that contains information about brain activity, which is used for the detection of epilepsy since epileptic seizures are caused by a disturbance in the electrophysiological activity of the brain. The prediction of epileptic seizure usually requires a detailed and experienced analysis of EEG. In this paper, we have introduced a statistical analysis of EEG signal that is capable of recognizing epileptic seizure with a high degree of accuracy and helps to provide automatic detection of epileptic seizure for different ages of epilepsy. To accomplish the target research, we extract various epileptic features namely approximate entropy (ApEn), standard deviation (SD), standard error (SE), modified mean absolute value (MMAV), roll-off (R), and zero crossing (ZC) from the epileptic signal. The k-nearest neighbours (k-NN) algorithm is used for the classification of epilepsy then regression analysis is used for the prediction of the epilepsy level at different ages of the patients. Using the statistical parameters and regression analysis, a prototype mathematical model is proposed which helps to find the epileptic randomness with respect to the age of different subjects. The accuracy of this prototype equation depends on proper analysis of the dynamic information from the epileptic EEG.

Highlights

Epilepsy is a long-lasting neurological disorder categorized by repeated, gratuitous seizures, electrophysiological disturbances in the human brain which may range from brief gaps of attention or muscle bumps to severe and prolonged seizures
The epileptic EEG data is processed for the achievement of the feature vector and a template as mentioned in Table 1 is formed for the train of k-nearest neighbours (k-nearest neighbour (NN)) network
The electrophysiological activity of the brain called EEG signal can analyze for the prediction and diagnosis of epilepsy of the living animals

Summary

Introduction

Epilepsy is a long-lasting neurological disorder categorized by repeated, gratuitous seizures, electrophysiological disturbances in the human brain which may range from brief gaps of attention or muscle bumps to severe and prolonged seizures. Characteristics of seizures vary and depend on where in the brain the disturbance first starts and how far it spreads Temporary symptoms occur, such as loss of awareness or consciousness and disturbances of movement, sensation (including vision, hearing, and taste), mood, or other cognitive functions. The risk of premature death in people with epilepsy is up to 3 times higher than the general population, with the highest rates found in low- and middle-income countries and rural versus urban areas. A great proportion of the causes of death related to epilepsy in low- and middle-income countries are potentially preventable, such as falls, drowning, burns, and prolonged seizures [8,9,10]. We use six features for the classification, and among these features, entropy has the higher ranked features that is used for the regression model for prediction of level of epilepsy

Mathematical Background of Classifier and Statistical Features

Proposed Research Architecture

Results and Discussions

Conclusions