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Improved neonatal seizure detection using adaptive learning
Título de la revista
Autores
Ansari, A. H.
Cherian, P. J.
Caicedo, A.
De Vos, M.
Naulaers, G.
Van Huffel, S.
Fecha
2017-09-14
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IEEE
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Abstract
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.
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Keywords
Pediatrics , Detectors , Electroencephalography , Feature extraction , Monitoring , Training , Sensitivity
