EEG-based neonatal seizure detection with Support Vector Machines

By Andrey Temko, Eoin M. Thomas, William P. Marnane, Gordon Lightbody, Geraldine B. Boylan. Clinical Neurophysiology, 2011

343 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Pediatrics, Perinatology and Child Health

View Paper: https://www.semanticscholar.org/paper/3a5e0e6da1ac0baaa996d2e94ec19c57f5ea115e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

Citations in the corpus

A review of channel selection algorithms for EEG signal processing (2015, methodology): “The results of these approaches have been compared with the all-channel (eight channels) REACT system [14] results leading to a feasibility of the suggested approaches by Faul and Marnane [15].”
A review of feature extraction and performance evaluation in epileptic seizure detection using EEG (2019, methodology): “For example, 55 features were used with a support vector machine (SVM) and post-processing for neonatal seizure detection, which provided a good detection rate of 89.2% with one false detection per hour [TTM + 11a].”
Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy. (2014, methodology): “Temko, 2011 [21] EEG (8 bipolar)/neonatal seizures Fast Fourier transform used for feature extraction followed by SVM classification.”