Master's Thesis: Investigation of Epileptic Seizures by Analyzing EEG Data using Machine Learning

Purposes:

  • Identifying epileptic seizures focus
  • Investigating the possibility of seizure prediction

Abstract:

‎In this work‎, ‎first we introduce some concepts and tools to analyze electroencephalography (EEG) data and then we try to investigate two sets of EEG data using these methods‎. ‎First‎, ‎we compute correlation matrix using moving statistics for increments of data set 1 as well as focal and non-focal areas separately and then we find probability density function of eigenvalues of this matrix‎. ‎At last‎, ‎by investigating this function for the second eigenvalue‎, ‎we find that for each patient in the data set the second peak in ictal states for focal areas is higher than the one for non-focal areas‎. ‎In the second part of this work‎, ‎we investigate correlation states between different areas for second data set by employing a tool widely used in the field of Machine Learning namely hierarchical clustering‎. ‎In this method after the computation of moving correlation matrix by using Average-Linkage clustering‎, ‎we try to find correlation states and then time evolution of state of the system‎. ‎By investigating plots corresponding to time evolution of states‎, ‎we conclude that system state in time period of epileptic seizure is different from stable state before the seizure‎. ‎In addition‎, ‎correlation states for post-ictal data are very different from correlation states for other periods of time‎. ‎On the other hand‎, ‎by observing the change of system state in approximately 17 minutes before seizure onset‎, ‎we conclude that this shows the possibility of prediction of the seizure. ‎At last‎, ‎by investigating correlation states in each time period‎, ‎we can observe that focal areas are strongly correlated in all of states and this situation does not depend on the time period that we studied‎.

 

Keywords:

Epilepsy, Epileptic Seizure, focal and extra-focal areas, Pearson Correlation Matrix, Hierarchical Clustering, Average-Linkage Clustering Algorithm, Correlation States, Ictal State

 

 

Date
Year: 
2015
Month: 
SEPTEMBER