A Flexible Deep Learning Architecture for Temporal Sleep Stage Classification Using Accelerometry and Photoplethysmography-Reference-Cited by-同舟云学术

A Flexible Deep Learning Architecture for Temporal Sleep Stage Classification Using Accelerometry and Photoplethysmography

Author:

Olsen Mads¹^ORCID,Zeitzer Jamie M.²^ORCID,Richardson Risa N.³,Davidenko Polina²,Jennum Poul J.⁴,Sorensen Helge B. D.⁵,Mignot Emmanuel²^ORCID

Affiliation:

1. Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA

2. Department of Psychiatry and Behavioral Sciences, Stanford University, USA

3. Division of Pulmonary and Sleep Medicine, Morsani College of Medicine, University of South Florida, USA

4. Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, Denmark

5. Biomedical Signal Processing & AI Research Group, Department of Health Technology, Technical University of Denmark, Denmark

Funder

Augustinus Fonden

Knud Højgaards Fond

Marie and M.B. Richters Fond

Danmark Amerika Fonden

Reinholdt W. Jorck og Hustrus Fond

Otto Mønsted Fonden

Copenhagen Center for Health Technology

Danmarks Tekniske Universitet

Klarman Family Foundation

Patient-Centered Outcomes Research Institute

Amazfit

Zepp Inc. brand

Emmanuel Mignot

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering

Link

Reference40 articles.

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