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Neonatal seizure detection using deep belief networks from multichannel EEG data by K. Visalini; Saravanan Alagarsamy; D. Nagarajan is a Computer Science article available to read on EtoBox.

What is Neonatal seizure detection using deep belief networks from multichannel EEG data about?

Seizures in neonates happen to be one of the most difficult emergency circumstances to deal with. They are the first signs of significant neurological implications and should be precisely diagnosed for prompt medical intervention to avoid serious morbidities and occasional fatalities. In most cases, unlike adults, newborns show practically no physical indications of an epileptic fit. Hitherto, seizure diagnosis has relied solely on consistent laborious manual monitoring and interpretation of long-duration recordings of electroencephalograms (EEG). However, because the brains of the newborns remain nascent during the neonatal period, interpreting the EEG data necessitates the expertise of highly skilled experts. This study demonstrates a Deep Belief Network-based Machine Learning architecture for binary classification of seizure and seizurefree periods. The classifier was trained and its performance was assessed using a publicly available annotated dataset containing EEG recordings of 79 infants collected at Helsinki University Hospital. The proposed model was highly discriminative for seizure detection (AUC: 97.1%). Our architecture establishes a high accuracy of 98.7 percentage, w

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Author

K. Visalini; Saravanan Alagarsamy; D. Nagarajan

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)