SVM-Based System for Prediction of Epileptic Seizures from iEEG Signal

Abstract

This paper describes a data-analytic modeling approach for prediction of epileptic seizures from intracranial electroencephalogram (iEEG) recording of brain activity. Even though it is widely accepted that statistical characteristics of iEEG signal change prior to seizures, robust seizure prediction remains a challenging problem due to subject-specific nature of data-analytic modeling. Methods: Our work emphasizes understanding of clinical considerations important for iEEG-based seizure prediction, and proper translation of these clinical considerations into data-analytic modeling assumptions. Several design choices during pre-processing and post-processing are considered and investigated for their effect on seizure prediction accuracy.

Existing System

Relative Spectral Power Features, Knn-Based Under sampling.

Proposed System

This paper describes a data-analytic modeling approach for seizure prediction from canine iEEG recordings. Using canine data (from dogs with epilepsy) is important due to the biological similarity of canine and human seizures, and the availability of high-quality canine iEEG data. The subject-specific or patient-specific nature of data-analytic modeling implies the need for long recordings of iEEG used as labeled training data. This provides additional motivation for using available canine data sets with months of recorded iEEG data. An SVM-based system for seizure prediction using iEEG signals is presented. The system is designed based on proper understanding of clinical considerations and their formalization into data-analytic modeling assumptions. Two important properties of our seizure prediction system are subject-specific modeling and using heavily unbalanced training data. This system also has several novel data-analytic interpretations and improvements. During the training stage, a binary classifier is estimated using unbalanced interictal and preictal data. However, a balanced validation data set is used for model selection.

Architecture

TRAINING STAGE

PREDICTION/OPERATION STAGE