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