Gaussian mixture models of ECoG signal features for improved detection of epileptic seizures

To investigate the potential for improving the performance of the Osorio-Frei seizure detection algorithm (OFA) by incorporating multiple FIR filters operating in parallel and Gaussian mixture models (GMM) for ECoG features distributions, thus creating "hybrid" system.

The "hybrid" algorithm decomposes the signal into four subbands, using wavelets, after which relevant features are extracted for each subband. Following these steps, multivariate GMM are developed for seizure and non-seizure states, using training segments. State classification is based on thresholding of the likelihood ratio of seizure vs. non-seizure data. Multiple comparisons are performed between this "hybrid" and a modified version of the OFA suitable for this purpose, using as indices false positives (FP), false negatives (FN) and speed of detection.

GMM improved speed of detection over the modified OFA at negligible FP levels. The average detection delay from expert visually placed electrographic onset over all seizures was reduced from 4.8 s for modified OFA to 1.8 s for GMM (p < 0.002) Individualized training by subject proved superior to group-based training.

This work introduces multi-feature extraction from ECoG signals together with use of Gaussian mixtures to model them, as tools to improve automated seizure detection. At the clinical level, this approach appears to increase warning time and with it the window during which safety measures and seizure blockage may be implemented, at an affordable computational cost and with negligible FP rate.