Automated EEG background analysis to identify neonates with hypoxic-ischemic encephalopathy treated with hypothermia at risk for adverse outcome: A pilot study

Autor: Anneleen Dereymaeker, Vladimir Matic, Jan Vervisch, Perumpillichira J. Cherian, Amir H. Ansari, Ofelie De Wel, Paul Govaert, Maarten De Vos, Sabine Van Huffel, Gunnar Naulaers, Katrien Jansen

Publikováno v: Pediatrics and Neonatology, Vol 60, Iss 1, Pp 50-58 (2019)

Background: To improve the objective assessment of continuous video-EEG (cEEG) monitoring of neonatal brain function, the aim was to relate automated derived amplitude and duration parameters of the suppressed periods in the EEG background (dynamic I

Externí odkaz: https://doaj.org/article/2aa9945bdbb24814959852473e23cba9

Decomposition of a Multiscale Entropy Tensor for Sleep Stage Identification in Preterm Infants

Autor: Ofelie De Wel, Mario Lavanga, Alexander Caicedo, Katrien Jansen, Gunnar Naulaers, Sabine Van Huffel

Publikováno v: Entropy, Vol 21, Iss 10, p 936 (2019)

Established sleep cycling is one of the main hallmarks of early brain development in preterm infants, therefore, automated classification of the sleep stages in preterm infants can be used to assess the neonate’s cerebral maturation. Tensor algebra

Externí odkaz: https://doaj.org/article/38d5e46669e8455db94d7c5e55c3ac6c

A convolutional neural network outperforming state-of-the-art sleep staging algorithms for both preterm and term infants

Autor: Ofelie De Wel, Sabine Van Huffel, Katrien Jansen, Kirubin Pillay, Amir Hossein Ansari, Anneleen Dereymaeker, Maarten De Vos, Gunnar Naulaers

Publikováno v: Journal of Neural Engineering

OBJECTIVE: To classify sleep states using electroencephalogram (EEG) that reliably works over a wide range of preterm ages, as well as term age. APPROACH: A convolutional neural network is developed to perform 2- and 4-class sleep classification in n

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b68b9e0b60bc2d4e87f005310270fb5d

Análisis de la complejidad del EEG neonatal mediante entropía multiescala: aplicaciones en la maduración cerebral y la clasificación de las etapas del sueño

Autor: Ofelie De Wel, Mario Lavanga, Gunnar Naulaers, Anneleen Dereymaeker, Sabine Van Huffel, Katrien Jansen, Alexander Caicedo Dorado

Publikováno v: Entropy, Vol 19, Iss 10, p 516 (2017)
Entropy
Entropy, 2017, 19 (10), pp.516. ⟨10.3390/e19100516⟩
Entropy; Volume 19; Issue 10; Pages: 516
Repositorio EdocUR-U. Rosario
Universidad del Rosario
instacron:Universidad del Rosario

© 2017 by the authors. Automated analysis of the electroencephalographic (EEG) data for the brain monitoring of preterm infants has gained attention in the last decades. In this study, we analyze the complexity of neonatal EEG, quantified using mult

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c37fc23dc81c6d190de00b21b993d786
https://www.mdpi.com/1099-4300/19/10/516

Quiet sleep detection in preterm infants using deep convolutional neural networks.

Autor: Amir Hossein Ansari, Ofelie De Wel, Mario Lavanga, Alexander Caicedo, Anneleen Dereymaeker, Katrien Jansen, Jan Vervisch, Maarten De Vos, Gunnar Naulaers, Sabine Van Huffel

Publikováno v: Journal of Neural Engineering; Dec2018, Vol. 15 Issue 6, p1-1, 1p