TY - GEN
T1 - Automatic quiet sleep detection based on multifractality in preterm neonates
T2 - 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
AU - Lavanga, M.
AU - De Wel, O.
AU - Caicedo, A.
AU - Heremans, E.
AU - Jansen, K.
AU - Dereymaeker, A.
AU - Naulaers, G.
AU - Van Huffel, S.
N1 - Funding Information:
VI. ACKNOWLEDGEMENTS This research is supported by Bijzonder Onderzoeksfonds KU Leuven (BOF): The effect of perinatal stress on the later outcome in preterm babies (# C24/15/036); iMinds Medical Information Technologies (SBO-2016); Belgian Federal Science Policy Office, IUAP # P7/19/ (DYSCO, ‘Dynamical systems, control and optimization’, 2012-2017); Belgian Foreign Affairs-Development Cooperation (VLIR UOS programs (2013-2019)); ERC Advanced Grant: BIOTENSORS (n◦ 339804). A.C. is a post-doc fellow at Fonds voor We-tenschappelijk Onderzoek-Vlaanderen (FWO), supported by Flemish government. M.L. is a SB PhD fellow at Fonds voor Wetenschappelijk Onderzoek-Vlaanderen (FWO), supported by Flemish government.
Publisher Copyright:
© 2017 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/9/13
Y1 - 2017/9/13
N2 - This study investigates the multifractal formalism framework for quiet sleep detection in preterm babies. EEG recordings from 25 healthy preterm infants were used in order to evaluate the performance of multifractal measures for the detection of quiet sleep. Results indicate that multifractal analysis based on wavelet leaders is able to identify quiet sleep epochs, but the classifier performances seem to be highly affected by the infant's age. In particular, from the developed classifiers, the lowest area under the curve (AUC) has been obtained for EEG recordings at very young age (≤ 31 weeks post-menstrual age), and the maximum at full-term age (≥ 37 weeks post-menstrual age). The improvement in classification performances can be due to a change in the multifractality properties of neonatal EEG during the maturation of the infant, which makes the EEG sleep stages more distinguishable.
AB - This study investigates the multifractal formalism framework for quiet sleep detection in preterm babies. EEG recordings from 25 healthy preterm infants were used in order to evaluate the performance of multifractal measures for the detection of quiet sleep. Results indicate that multifractal analysis based on wavelet leaders is able to identify quiet sleep epochs, but the classifier performances seem to be highly affected by the infant's age. In particular, from the developed classifiers, the lowest area under the curve (AUC) has been obtained for EEG recordings at very young age (≤ 31 weeks post-menstrual age), and the maximum at full-term age (≥ 37 weeks post-menstrual age). The improvement in classification performances can be due to a change in the multifractality properties of neonatal EEG during the maturation of the infant, which makes the EEG sleep stages more distinguishable.
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U2 - 10.1109/EMBC.2017.8037246
DO - 10.1109/EMBC.2017.8037246
M3 - Conference contribution
C2 - 29060290
AN - SCOPUS:85031932125
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 2010
EP - 2013
BT - 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 July 2017 through 15 July 2017
ER -