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

Research output: Contribution to book/anthology/report/conference proceeding › Book chapter › Research › peer-review

Original language	English
Title of host publication	2016 Computing in Cardiology Conference (CinC)
Publication date	23 Jan 2018
DOIs	https://doi.org/10.22489/cinc.2018.203
Publication status	Published - 23 Jan 2018

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

author = "Christian Ulrich and Martin Jensen and Rolf Oberlin and Kouhyar Tavakolian and Farzad Khosrow-Khavar and Andrew Blaber and Kasper S{\o}rensen and Samuel Schmidt",

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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)

ER -

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

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