@article{6655dcc918194ed18b16a02d3372c8d2,
title = "Neonatal Seizure Detection Using Deep Convolutional Neural Networks",
abstract = "Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.",
author = "Ansari, {Amir H.} and Cherian, {Perumpillichira J.} and Alexander Caicedo and Gunnar Naulaers and {De Vos}, Maarten and {Van Huffel}, Sabine",
note = "Funding Information: Amir H. Ansari and Sabine Van Huffel are supported by: Bijzonder Onderzoeksfonds KU Leuven (BOF): Center of Excellence (CoE) No. PFV/10/002 (OPTEC); SPARKLE: Sensor-based Platform for the Accurate and Remote monitoring of Kinematics Linked to E-health No. IDO-13-0358; “The effect of perinatal stress on the later outcome in preterm babies” No. C24/15/036; TARGID: Development of a novel diagnostic medical device to Funding Information: assess gastric motility No. C32-16-00364; Fonds voor Wetenschappelijk Onderzoek Vlaanderen (FWO): Project No. G.0A5513N (Deep brain stimulation); Agentschap Innoveren & Ondernemen (VLAIO): Project STW 150466-OSA+ and O & O HBC 2016 0184 eWatch; IMEC: Strategic Funding 2017, No. ICON-HBC.2016.0167 SeizeIT; Belgian Federal Science Policy Office: IUAP No. P7/19/ (DYSCO, “Dynamical systems, control and optimization”, 2012–2017); Belgian Foreign Affairs-Development Cooperation: VLIR UOS programs (2013–2019); European Union{\textquoteright}s Seventh Frame-work Programme (FP7/2007-2013): EU MC ITN TRANSACT 2012, No. 316679; The HIP Trial: No. 260777; ERASMUS +: NGDIVS 2016-1-SE01-KA203-022114; European Research Council (ERC) Advanced Grant, No. 339804 BIOTENSORS. This paper reflects only the authors{\textquoteright} views and the Union is not liable for any use that may be made of the contained information. Alexander Caicedo and Amir H. Ansari are supported by IWT PHD Grant: TBM 110697-NeoGuard. Alexander Caicedo is a Postdoctoral Fellow from the Fonds voor Wetenschappelijk Onderzoek Vlaanderen (FWO). Publisher Copyright: {\textcopyright} 2019 The Author(s). Copyright: Copyright 2019 Elsevier B.V., All rights reserved.",
year = "2019",
month = may,
day = "1",
doi = "10.1142/S0129065718500119",
language = "English (US)",
volume = "29",
journal = "International Journal of Neural Systems",
issn = "0129-0657",
publisher = "World Scientific Publishing Co. Pte Ltd",
number = "4",
}