Forecasting Seizures Using Intracranial EEG Measures and SVM in Naturally Occurring Canine Epilepsy

Abstract

Management of drug resistant focal epilepsy would be greatly assisted by a reliable warning system capable of alerting patients prior to seizures to allow the patient to adjust activities or medication. Such a system requires successful identification of a preictal, or seizure-prone state. Identification of preictal states in continuous long- duration intracranial electroencephalographic (iEEG) recordings of dogs with naturally occurring epilepsy was investigated using a support vector machine (SVM) algorithm. The dogs studied were implanted with a 16-channel ambulatory iEEG recording device with average channel reference for a mean (st. dev.) of 380.4 (+87.5) days producing 220.2 (+104.1) days of intracranial EEG recorded at 400 Hz for analysis. The iEEG records had 51.6 (+52.8) seizures identified, of which 35.8 (+30.4) seizures were preceded by more than 4 hours of seizure-free data. Recorded iEEG data were stratified into 11 contiguous, non-overlapping frequency bands and binned into one-minute synchrony features for analysis. Performance of the SVM classifier was assessed using a 5-fold cross validation approach, where preictal training data were taken from 90 minute windows with a 5 minute pre-seizure offset. Analysis of the optimal preictal training time was performed by repeating the cross validation over a range of preictal windows and comparing results. We show that the optimization of feature selection varies for each subject, i.e. algorithms are subject specific, but achieve prediction performance significantly better than a time-matched Poisson random predictor (p<0.05) in 5/5 dogs analyzed.

Fig 1. Approximate placement and numbering of sixteen implanted electrode contacts relative to the canine cortical anatomy.

Fig 2. Receiver-operating characteristic curves for the five analyzed canines.

Curves were generated by varying the threshold required to initiate a seizure warning.

Fig 3. Performance of the SVM-correlation seizure prediction method varies with the choice of the preictal training window.

The horizontal axis scales the preictal analysis window in minutes, and the vertical axis shows lead seizure sensitivity for the algorithm, if the algorithm threshold is tuned to maintain time in warning at 30%.

Fig 4. Performance of the SVM-correlation seizure prediction method varies with changes in the frequency band analyzed.

The horizontal axis shows the frequency band analyzed in hertz, while the vertical axis shows lead seizure sensitivity for the algorithm, if the algorithm threshold is tuned to maintain time in warning at 30%.