This paper is in the following e-collection/theme issue:

mHealth for Data Collection and Research (907) Respiratory Medicine (60) Machine Learning (1373) Novel Sensors and Data Acquisition Methods in Cardiology (190)

Published on in Vol 8, No 7 (2020): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13737, first published .
Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology

Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology

Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology

Authors of this article:

Joseph Prinable1 Author Orcid Image ;   Peter Jones1 Author Orcid Image ;   David Boland1 Author Orcid Image ;   Cindy Thamrin2 Author Orcid Image ;   Alistair McEwan1 Author Orcid Image
Article Authors Cited by (8) Tweetations (4) Metrics

Joseph Prinable   1 , BEng ;   Peter Jones   1 , PhD ;   David Boland   1 , PhD ;   Cindy Thamrin   2 , PhD ;   Alistair McEwan   1 , PhD

1 School of Electrical and Information Engineering, The University of Sydney, Darlington, Australia

2 The Woolcock Institute of Medical Research, The University of Sydney, Glebe, Australia

Corresponding Author:

  • Joseph Prinable, BEng
  • School of Electrical and Information Engineering
  • The University of Sydney
  • Room 402, Building J03
  • Maze Crescent
  • Darlington, 2006
  • Australia
  • Phone: 61 404035701
  • Email: joseph.prinable@sydney.edu.au