TY - GEN
T1 - Improved neonatal seizure detection using adaptive learning
AU - Ansari, A. H.
AU - Cherian, P. J.
AU - Caicedo, A.
AU - De Vos, M.
AU - Naulaers, G.
AU - Van Huffel, S.
N1 - Funding Information:
* This research is supported by Research Council KUL (BOF): CoE PFV/10/002 (OPTEC), SPARKLE IDO-13-0358, C24/15/036; VLAIO: projects: SWT 150466 - OSA+; 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); EU: EU MC ITN TRANSACT 2012, #316679, European Research Council: ERC Advanced Grant, #339804 BIOTENSORS. AC is a postdoctoral research of the FWO and supported by HIP Trial (FP7/2007-2013) #260777
Publisher Copyright:
© 2017 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/9/13
Y1 - 2017/9/13
N2 - In neonatal intensive care units performing continuous EEG monitoring, there is an unmet need for around-the-clock interpretation of EEG, especially for recognizing seizures. In recent years, a few automated seizure detection algorithms have been proposed. However, these are suboptimal in detecting brief-duration seizures (< 30s), which frequently occur in neonates with severe neurological problems. Recently, a multi-stage neonatal seizure detector, composed of a heuristic and a data-driven classifier was proposed by our group and showed improved detection of brief seizures. In the present work, we propose to add a third stage to the detector in order to use feedback of the Clinical Neurophysiologist and adaptively retune a threshold of the second stage to improve the performance of detection of brief seizures. As a result, the false alarm rate (FAR) of the brief seizure detections decreased by 50% and the positive predictive value (PPV) increased by 18%. At the same time, for all detections, the FAR decreased by 35% and PPV increased by 5% while the good detection rate remained unchanged.
AB - In neonatal intensive care units performing continuous EEG monitoring, there is an unmet need for around-the-clock interpretation of EEG, especially for recognizing seizures. In recent years, a few automated seizure detection algorithms have been proposed. However, these are suboptimal in detecting brief-duration seizures (< 30s), which frequently occur in neonates with severe neurological problems. Recently, a multi-stage neonatal seizure detector, composed of a heuristic and a data-driven classifier was proposed by our group and showed improved detection of brief seizures. In the present work, we propose to add a third stage to the detector in order to use feedback of the Clinical Neurophysiologist and adaptively retune a threshold of the second stage to improve the performance of detection of brief seizures. As a result, the false alarm rate (FAR) of the brief seizure detections decreased by 50% and the positive predictive value (PPV) increased by 18%. At the same time, for all detections, the FAR decreased by 35% and PPV increased by 5% while the good detection rate remained unchanged.
UR - http://www.scopus.com/inward/record.url?scp=85032179057&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85032179057&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2017.8037441
DO - 10.1109/EMBC.2017.8037441
M3 - Conference contribution
C2 - 29060482
AN - SCOPUS:85032179057
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 2810
EP - 2813
BT - 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Y2 - 11 July 2017 through 15 July 2017
ER -