Studying the effects of compression in EEG-based wearable sleep monitoring systems
File(s)
Published version
Author(s)
Liu, Deland
Imtiaz, Syed
Type
Journal Article
Abstract
Long-term sleep monitoring through the use of wearable EEG-based systems generates large volumes of data that need to be either locally stored or wireless transmitted. Compression of data can play a vital role to reduce the power consumption of these already resource-constrained systems. While compression methods can result in significantly reduced data storage and transmission requirements, the loss in signal information can have an impact on the algorithms used to extract the key sleep parameters. This paper studies the impact of six different state-of-the-art compression methods, including wavelet, SPIHT, filter and predictor-based methods, analysing their effects on the reconstructed signal quality particularly for automatic sleep staging applications. It looks at how the overall sleep staging accuracy as well as the detection accuracy of different sleep stages is reduced as a result of different EEG compression methods. It shows that the SPIHT and predictor-based compression methods outperform wavelet and filter-based methods in preserving the relevant signal features. It also shows that compression ratios of up to 65 can be achieved using the QSPIHT method with less than 10% loss in overall sleep staging accuracy.
Date Issued
2020-09-14
Date Acceptance
2020-09-09
Citation
IEEE Access, 2020, 8, pp.168486-168501
ISSN
2169-3536
Publisher
Institute of Electrical and Electronics Engineers
Start Page
168486
End Page
168501
Journal / Book Title
IEEE Access
Volume
8
Copyright Statement
© 2020 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
License URL
Subjects
08 Information and Computing Sciences
09 Engineering
10 Technology
Publication Status
Published