| dc.contributor.author | Türetken, Engin | |
| dc.contributor.author | Van Zaen, Jerome | |
| dc.contributor.author | Delgado-Gonzalo, Ricard | |
| dc.date.accessioned | 2021-12-09T14:03:59Z | |
| dc.date.available | 2021-12-09T14:03:59Z | |
| dc.date.issued | 2019 | |
| dc.identifier.citation | 2019 6th Swiss Conference on Data Science (SDS), Bern (Switzerland), pp. 95-96 | |
| dc.identifier.uri | https://yoda.csem.ch/handle/20.500.12839/345 | |
| dc.description.abstract | The 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.subject | CNN;RNN;deep learning;embedded;SoC;sleep;polysomnography;e-health;m-health | |
| dc.title | Embedded Deep Learning for Sleep Staging | |
| dc.type | Proceedings Article | |
| dc.type.csemdivisions | Div-M | |
| dc.type.csemresearchareas | Digital Health | |
| dc.type.csemresearchareas | Data & AI | |
| dc.identifier.url | https://ieeexplore.ieee.org/document/8789857/ | |
| dc.identifier.doi | https://doi.org/10.1109/SDS.2019.00005 | |
