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Multichannel Sleep Stage Classification and Transfer Learning using Convolutional Neural Networks

Andreotti, Fernando, Phan, Huy, Cooray, Navin, Lo, Christine, Hu, Michele T.M., De Vos, Maarten (2018) Multichannel Sleep Stage Classification and Transfer Learning using Convolutional 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. 171-174. IEEE, Honolulu, Hawaii ISBN 978-1-5386-3646-6. (doi:10.1109/EMBC.2018.8512214)

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http://dx.doi.org/10.1109/EMBC.2018.8512214

Abstract

Current sleep medicine relies on the analysis of polysomnographic measurements, comprising amongst others electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals. This analysis currently requires supervision of a trained expert. Convolutional neural networks (CNN) provide an interesting framework to automated classification of sleep epochs based on raw EEG, EOG and EMG waveforms. In this study, we apply CNN approaches from the literature to four databases from pathological and physiological subjects. The best performing model resulted in Cohen’s Kappa of k = 0.75 on healthy subjects and k = 0.64 on patients suffering from a variety of sleep disorder. Further, we show the advantages of using additional sensor data such as EOG and EMG. Last, to cope with smaller datasets of less prevalent diseases, we propose a transfer learning procedure using large freely available databases for pre-training. This procedure is demonstrated using a private REM Behaviour Disorder database, improving sleep classification by 24.4%.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/EMBC.2018.8512214
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Huy Phan
Date Deposited: 25 Feb 2019 14:20 UTC
Last Modified: 03 Jun 2019 09:27 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/72663 (The current URI for this page, for reference purposes)
Phan, Huy: https://orcid.org/0000-0003-4096-785X
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