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AuthorBen Said, Ahmed
AuthorMohamed, Amr
AuthorElfouly, Tarek
Available date2021-01-25T06:45:46Z
Publication Date2017
Publication Name2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 2017
ResourceScopus
URIhttp://dx.doi.org/10.1109/IWCMC.2017.7986507
URIhttp://hdl.handle.net/10576/17431
AbstractThe emergence of mobile health (mHealth) systems has risen the challenges and concerns due to the sensitivity of the data involved in such systems. It is essential to ensure that these data are well delivered to the health monitoring center for accurate and perfect diagnosis and follow-up. Due to the wireless network constraints, these requirements become more challenging. In this paper, we propose a deep learning approach for EEG data compression in mHealth system. We show that the stacked autoencoder neural network architecture is efficient for EEG data compression. We conduct a comprehensive comparative study that demonstrates the effectiveness of our system for EEG compression in addition to preserving the total energy consumption.
SponsorThis publication was made possible by NPRP grant #7-684-1-127 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectCompression
EEG
MHealth
Stacked autoencoder
TitleDeep learning approach for EEG compression in mHealth system
TypeConference Paper
Pagination1508-1512
dc.accessType Abstract Only


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