G. B. Boylan

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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)
In this study neonatal EEG has been analysed with information theory, complexity, SVD-based and nonlinear dynamic systems theory, or chaos theory, approaches. The analysis has been carried out to determine, given the amount of extra time needed to generate the chaos theory results, if they are considerably better than their information theory, complexity(More)
Newborn EEG is a complex multiple channel signal that displays nonstationary and nonlinear characteristics. Recent studies have focussed on characterizing the manifestation of seizure on the EEG for the purpose of automated seizure detection. This paper describes a novel model of newborn EEG that can be used to improve seizure detection algorithms. The new(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)
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)
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 systems(More)
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