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dc.contributor.authorJorge, Joao
dc.contributor.authorProenca, Martin
dc.contributor.authorAguet, Clementine
dc.contributor.authorVan Zaen, Jerome
dc.contributor.authorBonnier, Guillaume
dc.contributor.authorRenevey, Phillipe
dc.contributor.authorLemkaddem, Alia
dc.contributor.authorSchoettker, Patrick
dc.contributor.authorLemay, Mathieu
dc.date.accessioned2022-02-14T17:07:52Z
dc.date.available2022-02-14T17:07:52Z
dc.date.issued2020
dc.identifier.citation2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society, Montreal, QC (Canada), pp. 910-913
dc.identifier.isbn978-1-72811-990-8
dc.identifier.urihttps://yoda.csem.ch/handle/20.500.12839/740
dc.titleMachine Learning Approaches For Improved Continuous, Non-occlusive Arterial Pressure Monitoring Using Photoplethysmography
dc.typeProceedings Article
dc.type.csemdivisionsDiv-E
dc.type.csemresearchareasDigital Health
dc.identifier.urlhttps://ieeexplore.ieee.org/document/9176512/
dc.identifier.doi10.1109/EMBC44109.2020.9176512


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