Exploring temporal information in neonatal seizures using a dynamic time warping based SVM kernel.
Comput Biol Med
; 82: 100-110, 2017 03 01.
Article
em En
| MEDLINE
| ID: mdl-28167405
ABSTRACT
Seizure events in newborns change in frequency, morphology, and propagation. This contextual information is explored at the classifier level in the proposed patient-independent neonatal seizure detection system. The system is based on the combination of a static and a sequential SVM classifier. A Gaussian dynamic time warping based kernel is used in the sequential classifier. The system is validated on a large dataset of EEG recordings from 17 neonates. The obtained results show an increase in the detection rate at very low false detections per hour, particularly achieving a 12% improvement in the detection of short seizure events over the static RBF kernel based system.
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Base de dados:
MEDLINE
Assunto principal:
Algoritmos
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Reconhecimento Automatizado de Padrão
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Diagnóstico por Computador
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Epilepsia Neonatal Benigna
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Eletrocardiografia
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Máquina de Vetores de Suporte
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article