Improved neonatal seizure detection using adaptive learning.
Annu Int Conf IEEE Eng Med Biol Soc
; 2017: 2810-2813, 2017 Jul.
Article
em En
| MEDLINE
| ID: mdl-29060482
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Convulsões
Tipo de estudo:
Diagnostic_studies
Limite:
Humans
/
Newborn
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article