Charles Sturt University
Charles Sturt University

Research Project: Epilepsy Seizure Detection

M. Paul and M.Z Parvez

Background

Approximately 50 million people worldwide, that is 1% of the world' population, have epilepsy  (source: World Health Organization). Epilepsy is a neurological disorder that affects people of all ages, causing recurring seizures due to abnormal electrical activity in the brain. There is no cure for epilepsy however for 70% of patients,  anti-epileptic medication can control seizures. 

Electroencephalography (EEG) is one of the main diagnostic tests for epilepsy. EEG is a non-invasive technique used to measure and record the electrical activity in various regions of the brain. Several electrodes are attached to the scalp and measure electrical impulses. The electrodes are connected by wires to a machine which amplifies and record the resulting patterns of electrical impulses: the electroencephalogram (EEG). 

EEG Recording Cap 

A Cap Fitted with Electrodes for an EEG (Image: Chris Hope - http://www.flickr.com/photos/tim_uk/8135755109/)

The brain activity of an epileptic patient has four major stages:

  1. preictal stage: the time before the seizure,
  2. ictal stage: the actual seizure,
  3. postictal stage: the period after the seizure, lasting usually between 5 and 60 minutes.
  4. interictal stage characterised by normal brain activity, is the time between seizures (postictal to preictal stage).

The goal of this research is to analyse the EEG signals to be able to detect the electrical changes in the brain prior to the onset of a seizure, in the preictal stage.

The Challenge

EEG signals vary among individuals, with the location and the number of the electrodes used.
The following graph shows the EEG signal measured during a seizure and between seizures. Although the amplitude of the signal is higher during a seizure, there are no distinctive differences between the two signals.

EEG Signal 

EEG Signal Showing a Preictal and Interictal Period from the Same Patient (Source: Epilepsy Center of the University Hospital of Freiburg.)

Method

Paul's team used several signal processing and machine learning techniques. The following method is the most promising and consists of two steps:

  1. feature extraction and classification based on Discrete Wavelet Transform (DWT)
  2. classification of preictal and interictal EEG signals using a Least Square-Support Vector Machine (LS-SVM) technique which is supervised learning technique.

The results showed that this method performs consistently and the classification of octal and preictal signals performance is 93.12% and 93.79% for the frontal and temporal lobe signals respectively in terms of sensitivity, specificity and accuracy. 

Future Work

The technique needs to be applied to datasets obtained from different patients to improve and strengthen this technique.

Publications

  • M. Z. Parvez, M. Paul, "Epileptic Seizure Prediction by Exploiting Spatiotemporal Relationship of EEG Signals using Phase Correlation," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. PP, no. 99.
  • M. Z. Parvez, M. Paul , "Prediction and Detection of Epileptic Seizure," chapter in "Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes", edited by Wahiba Ben Abdessalem Karâa and Nilanjan Dey, (ABBE) book series [IGI Global], pp 314-336, 2015. 
  • M. Z. Parvez, M. Paul, "Epileptic Seizure Detection by Exploiting Temporal Correlation of Electroencephalogram Signals," IET Signal Processing, vol. 9, no 6, pp. 467-475, 2015
  • M. Z. Parvez, M. Paul, "Novel approaches of EEG signal classification using IMF bandwidth and DCT frequency", Biomedical Engineering: Applications, Basis and Communications, vol. 27, no. 03, 2015.
  • M. Z. Parvez, M. Paul, "Epileptic Seizure Prediction by Exploiting Signal Transitions Phenomena", 17th International Conference on Bioinformatics and Biological Engineering (ICBBE-2015), Melbourne, December 13-14, 2015. 
  • M. Z. Parvez, M. Paul, "Epileptic Seizure Detection by Analyzing EEG Signals using Different Transformation Techniques," Neurocomputing, vol. 145, pp. 190-200, December 2014.
  • M. Z. Parvez, M. Paul, M. Antolovich, "Detection of Pre-stage of Epileptic Seizure by Exploiting Temporal Correlation of EMD Decomposed EEG Signals," Journal of Medical and Bioengineering, vol. 4 (2), pp. 110-116, 2014.
  • M. Z. Parvez, M. Paul, "EEG Signal Classification using Frequency Band Analysis towards Epileptic Seizure Prediction," 16th International on Computer and Information Technology (ICCIT), pp.126-130, Khulna, 8-10 March 2014.
  • M.Z. Parvez, M. Paul, "Classification of Ictal and Interictal EEG signals", IASTED Conference on Biomedical Engineering, 791-031, 2013.
  • M. Z. Parvez,  M. Paul,  "Features extraction and classification for Ictal and Interictal EEG signals using EMD and DCT", 15th International Conference on Computer and Information Technology (ICCIT), pp. 132-137, 2012.

Contacts

Dr Manoranjan Paul, Mohammad Zavid Parvez
School of Computing and Mathematics, Charles Sturt University