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DNN Filter Bank Improves 1-Max Pooling CNN for Single-Channel EEG Automatic Sleep Stage Classification

Phan, Huy, Andreotti, Fernando, Cooray, Navin, Chén, Oliver Y., De Vos, Maarten (2018) DNN Filter Bank Improves 1-Max Pooling CNN for Single-Channel EEG Automatic Sleep Stage Classification. In: 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Learning from the Past, Looking to the Future. . pp. 453-456. IEEE, Honolulu, Hawaii E-ISBN 978-1-5386-3646-6. (doi:10.1109/EMBC.2018.8512286)

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http://dx.doi.org/10.1109/EMBC.2018.8512286

Abstract

We present in this paper an efficient convolutional neural network (CNN) running on time-frequency image features for automatic sleep stage classification. Opposing to deep architectures which have been used for the task, the proposed CNN is much simpler. However, the CNN’s convolutional layer is able to support convolutional kernels with different sizes, and therefore, capable of learning features at multiple temporal resolutions. In addition, the 1-max pooling strategy is employed at the pooling layer to better capture the shift-invariance property of EEG signals. We further propose a method to discriminatively learn a frequency-domain filter bank with a deep neural network (DNN) to preprocess the time-frequency image features. Our experiments show that the proposed 1-max pooling CNN performs comparably with the very deep CNNs in the literature on the Sleep-EDF dataset. Preprocessing the time-frequency image features with the learned filter bank before presenting them to the CNN leads to significant improvements on the classification accuracy, setting the state-of-the-art performance on the dataset.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/EMBC.2018.8512286
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Huy Phan
Date Deposited: 20 Feb 2019 23:11 UTC
Last Modified: 03 Jun 2019 09:27 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/72661 (The current URI for this page, for reference purposes)
Phan, Huy: https://orcid.org/0000-0003-4096-785X
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