Show simple item record

dc.contributor.authorTüretken, Engin
dc.contributor.authorVan Zaen, Jerome
dc.contributor.authorDelgado-Gonzalo, Ricard
dc.date.accessioned2021-12-09T14:03:59Z
dc.date.available2021-12-09T14:03:59Z
dc.date.issued2019
dc.identifier.citation2019 6th Swiss Conference on Data Science (SDS), Bern (Switzerland), pp. 95-96
dc.identifier.urihttps://yoda.csem.ch/handle/20.500.12839/345
dc.description.abstractThe rapidly-advancing technology of deep learning (DL) into the world of the Internet of Things (IoT) has not fully entered in the fields of m-Health yet. Among the main reasons are the high computational demands of DL algorithms and the inherent resource-limitation of wearable devices. In this paper, we present initial results for two deep learning architectures used to diagnose and analyze sleep patterns, and we compare them with a previously presented hand-crafted algorithm. The algorithms are designed to be reliable for consumer healthcare applications and to be integrated into low-power wearables with limited computational resources.
dc.subjectCNN;RNN;deep learning;embedded;SoC;sleep;polysomnography;e-health;m-health
dc.titleEmbedded Deep Learning for Sleep Staging
dc.typeProceedings Article
dc.type.csemdivisionsDiv-M
dc.type.csemresearchareasDigital Health
dc.type.csemresearchareasData & AI
dc.identifier.urlhttps://ieeexplore.ieee.org/document/8789857/
dc.identifier.doihttps://doi.org/10.1109/SDS.2019.00005


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Research Publications
    The “Research Publications” collection provides bibliographic information for scientific papers including conference proceedings and presentations.

Show simple item record