Neonatal Seizure Detection Using Deep Convolutional Neural Networks

Amir H. Ansari, Perumpillichira J. Cherian, Alexander Caicedo, Gunnar Naulaers, Maarten De Vos, Sabine Van Huffel

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

71 Citas (Scopus)

Resumen

Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.

Idioma originalInglés estadounidense
Número de artículo1850011
PublicaciónInternational Journal of Neural Systems
Volumen29
N.º4
DOI
EstadoPublicada - may 1 2019
Publicado de forma externa

All Science Journal Classification (ASJC) codes

  • Redes de ordenadores y comunicaciones

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