Determining the Respiratory State From a Seismocardiographic Signal - A Machine Learning Approach

Christian Ulrich; Martin Jensen; Rolf Oberlin; Kouhyar Tavakolian; Farzad Khosrow-Khavar; Andrew Blaber; Kasper Sørensen; Samuel Schmidt

doi:10.22489/cinc.2018.203

Determining the Respiratory State From a Seismocardiographic Signal - A Machine Learning Approach

Christian Ulrich, Martin Jensen, Rolf Oberlin, Kouhyar Tavakolian, Farzad Khosrow-Khavar, Andrew Blaber, Kasper Sørensen, Samuel Schmidt

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Bidrag til bog/antologi › Forskning › peer review

Originalsprog	Engelsk
Titel	2016 Computing in Cardiology Conference (CinC)
Publikationsdato	23 jan. 2018
DOI	https://doi.org/10.22489/cinc.2018.203
Status	Udgivet - 23 jan. 2018

Adgang til dokumentet

10.22489/cinc.2018.203

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https://app.dimensions.ai/details/publication/pub.1111900032

Citationsformater

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title = "Determining the Respiratory State From a Seismocardiographic Signal - A Machine Learning Approach",

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AU - Ulrich, Christian

AU - Jensen, Martin

AU - Oberlin, Rolf

AU - Tavakolian, Kouhyar

AU - Khosrow-Khavar, Farzad

AU - Blaber, Andrew

AU - Sørensen, Kasper

AU - Schmidt, Samuel

PY - 2018/1/23

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BT - 2016 Computing in Cardiology Conference (CinC)

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