Neonatal Seizure Detection Using Deep Convolutional Neural Networks

Author:

Ansari Amir H.12,Cherian Perumpillichira J.34,Caicedo Alexander12,Naulaers Gunnar56,De Vos Maarten7,Van Huffel Sabine12

Affiliation:

1. Department of Electrical Engineering, KU Leuven, 3001 Leuven, Belgium

2. IMEC VZW, 3001 Leuven, Belgium

3. Department of Neurology, Erasmus University Medical Center, 3015 CE Rotterdam, The Netherlands

4. Department of Medicine, McMaster University, Hamilton, ON, Canada L8S 4L8 Canada

5. Neonatal Intensive Care Unit, University Hospitals Leuven, Belgium

6. Department of Development and Regeneration, KU Leuven, 3000 Leuven, Belgium

7. Department of Engineering, University of Oxford, Oxford OX1 3PJ, UK

Abstract

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.

Publisher

World Scientific Pub Co Pte Lt

Subject

Computer Networks and Communications,General Medicine

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