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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)
This paper presents an energy-efficient medium access control protocol suitable for communication in a wireless body area network for remote monitoring of physiological signals such as EEG and ECG. The protocol takes advantage of the static nature of the body area network to implement the effective time-division multiple access (TDMA) strategy with very(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 paper the benefits of implementation of the Tate pair-ing computation in dedicated hardware are discussed. The main observation lies in the fact that arithmetic architectures in the extension field GF (3 6m) are good candidates for parallelization, leading to a similar calculation time in hardware as for operations over the base field GF (3 m).(More)
In this paper we present a new approach to attacking a modular exponentiation and scalar multiplication based by distinguishing multiplications from squaring operations using the instantaneous power consumption. Previous approaches have been able to distinguish these operations based on information of the specific implementation of the embedded algorithm or(More)
OBJECTIVE This study was undertaken to identify the best performing quantitative EEG features for neonatal seizures detection from a test set of 21. METHODS Each feature was evaluated on 1-min, artefact-free segments of seizure and non-seizure neonatal EEG recordings. The potential utility of each feature for neonatal seizure detection was determined(More)
Adaptive probabilistic modeling of the EEG background is proposed for seizure detection in neonates with hypoxic ischemic encephalopathy. The decision is made based on the temporal derivative of the seizure probability with respect to the adaptively modeled level of background activity. The robustness of the system to long duration "seizure-like" artifacts,(More)