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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(More)
OBJECTIVE This study discusses an appropriate framework to measure system performance for the task of neonatal seizure detection using EEG. The framework is used to present an extended overview of a multi-channel patient-independent neonatal seizure detection system based on the Support Vector Machine (SVM) classifier. METHODS The appropriate framework(More)
OBJECTIVE To describe a novel neurophysiology based performance analysis of automated seizure detection algorithms for neonatal EEG to characterize features of detected and non-detected seizures and causes of false detections to identify areas for algorithmic improvement. METHODS EEGs of 20 term neonates were recorded (10 seizure, 10 non-seizure).(More)
Automated analysis of the neonatal EEG has the potential to assist clinical decision making for neonates with hypoxic-ischaemic encephalopathy. This paper proposes a method of automatically grading the degree of abnormality in an hour long epoch of neonatal EEG. The automated grading system (AGS) was based on a multi-class linear classifier grading of(More)
Background and Aim: Since there is compelling evidence that seizures are harmful to the immature developing brain, accurate seizure detection at the cotside is imperative. Multichannel EEG is a reliable method for confirming neonatal seizures but interpretation requires expertise. We recently developed a neonatal seizure detection algorithm (NSDA) to aid(More)
— Two systems based on different classifiers are compared for the task of neonatal seizure detection. Support vector machines and Gaussian mixture models are presented as examples of discriminative and generative approaches to classification. The performance of both systems is assessed using a number of metrics, the results of which indicate that both(More)
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