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      Neonatal Seizure Detection Using Deep Convolutional Neural Networks

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          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.

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          Most cited references 45

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          CNN Features Off-the-Shelf: An Astounding Baseline for Recognition

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            Face recognition: a convolutional neural-network approach.

            We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer.
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              Monotone Piecewise Cubic Interpolation

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                Author and article information

                Journal
                International Journal of Neural Systems
                Int. J. Neur. Syst.
                World Scientific Pub Co Pte Lt
                0129-0657
                1793-6462
                May 15 2019
                May 2019
                May 15 2019
                May 2019
                : 29
                : 04
                : 1850011
                Affiliations
                [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
                Article
                10.1142/S0129065718500119
                © 2019

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