Incorporating structural information from the multichannel EEG improves patient-specific seizure detection

A novel patient-specific seizure detection algorithm is presented. As the spatial distribution of the ictal pattern is characteristic for a patient's seizures, this work incorporates such information into the data representation and provides a learning algorithm exploiting it.

The proposed training algorithm uses nuclear norm regularization to convey structural information of the channel-feature matrices extracted from the EEG. This method is compared to two existing approaches utilizing the same feature set, but integrating the multichannel information in a different manner. The performances of the detectors are demonstrated on a publicly available dataset containing 131 seizures recorded in 892 h of scalp EEG from 22 pediatric patients.

The proposed algorithm performed significantly better compared to the reference approaches (p=0.0170 and p=0.0002). It reaches a median performance of 100% sensitivity, 0.11h(-1) false detection rate and 7.8s alarm delay, outperforming a method in the literature using the same dataset.

The strength of our method lies within conveying structural information from the multichannel EEG. Such formulation automatically includes crucial spatial information and improves detection performance.

Our solution facilitates accurate classification performance for small training sets, therefore, it potentially reduces the time needed to train the detector before starting monitoring.