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dc.contributor.authorTüretken, Engin
dc.contributor.authorVan Zaen, Jerome
dc.contributor.authorDelgado-Gonzalo, Ricard
dc.identifier.citation2019 6th Swiss Conference on Data Science (SDS), Bern (Switzerland), pp. 95-96
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.csemresearchareasDigital Health
dc.type.csemresearchareasData & AI

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