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dc.contributor.authorBraun, Fabian
dc.contributor.authorRenevey, Philippe
dc.contributor.authorVan Zaen, Jérôme
dc.contributor.authorLemkaddem, Alia
dc.contributor.authorTüretken, Engin
dc.contributor.authorDunbar, Andrea
dc.contributor.authorDelgado-Gonzalo, Ricard
dc.contributor.authorLemay, Mathieu
dc.contributor.authorDe Jaegere, Kurt
dc.contributor.authorHorvath, Christian M.
dc.contributor.authorRoth Wälti, Corinne
dc.contributor.authorBrill, Anne-Kathrin
dc.contributor.authorOtt, Sebastian R.
dc.date.accessioned2022-02-14T17:08:15Z
dc.date.available2022-02-14T17:08:15Z
dc.date.issued2019-10
dc.identifier.citationASMT'19, Ascona (Switzerland), pp. 36
dc.identifier.urihttps://yoda.csem.ch/handle/20.500.12839/956
dc.description.abstractPolysomnography (PSG) is the current state of the art for sleep staging but has several drawbacks: it requires a multitude of sensors and needs experienced technicians, which makes it expensive. It is further not suited for long-term monitoring in ambulatory environments [1]. Recent advances in optical heart rate monitoring allow for devices which are low-cost and suitable for ambulatory sleep staging. Even though not expected to completely replace PSG, such devices would allow for reliable pre-screening of several consecutive nights of larger populations.
dc.titleSleep Staging Using Deep Neural Networks
dc.typeProceedings Article
dc.type.csemdivisionsDiv-E
dc.type.csemresearchareasDigital Health


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