EEG-based neonatal seizure detection with Support Vector Machines

OBJECTIVE: The study presents a multi-channel patient-independent neonatal seizure detection system based on the Support Vector Machine (SVM) classifier. METHODS: A machine learning algorithm (SVM) is used as a classifier to discriminate between seizure and non-seizure EEG epochs. Two post-processing steps are proposed to increase both the temporal precision and the robustness of the system. The resulting system is validated on a large clinical dataset of 267 h of EEG data from 17 full-term newborns with seizures. RESULTS: The performance of the system using event-based metrics is reported. The system showed the best up-to-date performance of a neonatal seizure detection system. The system was able to achieve an average good detection rate of ~89% with one false seizure detection per hour, ~96% with two false detections per hour, or ~100% with four false detections per hour. An analysis of errors revealed sources of misclassification in terms of both missed seizures and false detections. CONCLUSIONS: The results obtained with the proposed SVM-based seizure detection system allow for its practical application in neonatal intensive care units. SIGNIFICANCE: The proposed SVM-based seizure detection system can greatly assist clinical staff, in a neonatal intensive care unit, to interpret the EEG. The system allows control of the final decision by choosing different confidence levels which makes it flexible for clinical needs. The obtained results may provide a reference for future seizure detection systems.