OBJECTIVE To evaluate 3 published automated algorithms for detecting seizures in neonatal EEG. METHODS One-minute, artifact-free EEG segments consisting of either EEG seizure activity or non-seizure EEG activity were extracted from EEG recordings of 13 neonates. Three published neonatal seizure detection algorithms were tested on each EEG recording. In an attempt to obtain improved detection ...

Automated methods of neonatal EEG seizure detection attempt to highlight the evolving, stereotypical, pseudo-periodic, nature of EEG seizure while rejecting the nonstationary, modulated, coloured stochastic background in the presence of various EEG artefacts. An important aspect of neonatal seizure detection is, therefore, the accurate representation and detection of pseudo-periodicity in the n...

objective the aim of the present study was to evaluate the etiology and frequency of neonatal seizure in hospitalized neonates. materials and methods in this descriptive, cross-sectional study, we evaluated 1295 neonates with seizures admitted to neonatal and nicu wards in our center. data was collected on age, sex, birth weight, serum levels of calcium, glucose, and sodium, ct scan findings, h...

This paper critically reviews the current literature and evidence on diagnosis, classification, clinical approach, management of neonatal seizures. A stepwise approach for treatment seizures is presented. In a recent randomized controlled trial comparing phenobarbital to levetiracetam as first-choice anti-seizure medication with primary outcome seizure free 24 h, former was found superior (80% ...

Correspondence to: Dr Topun Austin, Neonatal Intensive Care Unit, The Rosie Hospital, Cambridge University Hospitals, NHS Foundation Trust, Hills Road, Cambridge CB2 2QQ, UK. Email: topun.austin@ addenbrookes.nhs.uk Tel: 01223 216677 Fax: 01223 0217064. Seizures in the newborn infant brain represent a major unsolved problem in neonatal medicine. Neonatal seizures are poorly classified, frequent...

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. Th...

how to cite this article: eghbalian f, rasuli b, monsef f. frequency, causes, and findings of brain ct scans of neonatal seizure at besat hospital, hamadan, iran. iran j child neurol. 2015 winter;9(1):56-63. abstract objective neonatal seizures are the most common neurological symptoms and often signal an underlying serious neurologic condition. this study determines the frequency of neonatal s...

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 for performance assessment of neonatal s...

Neonatal seizures occur in their majority close temporal relation to an acute brain injury or systemic insult, and are accordingly defined as symptomatic provoked seizures. However less frequently, unprovoked may also present the neonatal period secondary structural abnormalities, thus corresponding epilepsies, genetic conditions, epilepsies. Unprovoked should be considered clinical manifestati...