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Automatic Sleep Stage Classification Using Single-Channel EEG: Learning Sequential Features with Attention-Based Recurrent Neural Networks

Phan, Huy, Andreotti, Fernando, Cooray, Navin, Chén, Oliver Y., De Vos, Maarten (2018) Automatic Sleep Stage Classification Using Single-Channel EEG: Learning Sequential Features with Attention-Based Recurrent Neural Networks. In: 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Learning from the Past, Looking to the Future. . pp. 1452-1455. IEEE, Honolulu, Hawaii E-ISBN 978-1-5386-3646-6. (doi:10.1109/EMBC.2018.8512480)

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http://dx.doi.org/10.1109/EMBC.2018.8512480

Abstract

We propose in this work a feature learning approach using deep bidirectional recurrent neural networks (RNNs) with attention mechanism for single-channel automatic sleep stage classification. We firstly decompose an EEG epoch into multiple small frames and subsequently transform them into a sequence of frame-wise feature vectors. Given the training sequences, the attention-based RNN is trained in a sequence-to-label fashion for sleep stage classification. Due to discriminative training, the network is expected to encode information of an input sequence into a high-level feature vector after the attention layer. We, therefore, treat the trained network as a feature extractor and extract these feature vectors for classification which is accomplished by a linear SVM classifier. We also propose a discriminative method to learn a filter bank with a DNN for preprocessing purpose. Filtering the frame-wise feature vectors with the learned filter bank beforehand leads to further improvement on the classification performance. The proposed approach demonstrates good performance on the Sleep-EDF dataset.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/EMBC.2018.8512480
Divisions: Faculties > Sciences > School of Computing > Data Science
Depositing User: Huy Phan
Date Deposited: 20 Feb 2019 23:04 UTC
Last Modified: 15 Jan 2020 12:08 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/72660 (The current URI for this page, for reference purposes)
Phan, Huy: https://orcid.org/0000-0003-4096-785X
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