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Estimation of Respiratory Information from the Built-In Pressure Sensors of a Dialysis Machine

Sandberg, Frida LU ; Holmer, Mattias LU ; Olde, Bo and Solem, Kristian LU (2014) Computing in Cardiology 2014 41. p.853-856
Abstract
The purpose of the present study is to determine the feasibility of estimating respiratory information from the built-in pressure sensors of a dialysis machine. The study database consists of simultaneous recordings of pressure signals and capnographic signals from 6 patients during 7 hemodialysis treatment sessions. Respiration rates were estimated using respiratory induced variations in the beat- to-beat interval series of the cardiac component of the pressure signal and respiratory induced baseline varia- tions in the pressure signal, respectively. The estimated respiration rates were compared to a reference respira- tion rate determined from the capnograhpic signal. The root-mean-square error of the estimated respiration rate from the... (More)
The purpose of the present study is to determine the feasibility of estimating respiratory information from the built-in pressure sensors of a dialysis machine. The study database consists of simultaneous recordings of pressure signals and capnographic signals from 6 patients during 7 hemodialysis treatment sessions. Respiration rates were estimated using respiratory induced variations in the beat- to-beat interval series of the cardiac component of the pressure signal and respiratory induced baseline varia- tions in the pressure signal, respectively. The estimated respiration rates were compared to a reference respira- tion rate determined from the capnograhpic signal. The root-mean-square error of the estimated respiration rate from the baseline variations of the pressure signal was 2.10 breaths/min; the corresponding error of the estimated res- piration rate from the beat-to-beat interval series of the cardiac component was 4.95 breaths/min. The results sug- gest that it is possible to estimate respiratory information from the pressure sensors. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
[Host publication title missing]
editor
Murray, Alan and
volume
41
pages
4 pages
publisher
Computing in Cardiology
conference name
Computing in Cardiology 2014
conference location
Cambridge, Massachusetts, United States
conference dates
2014-09-07 - 2014-09-10
external identifiers
  • scopus:84931383330
ISSN
0276-6574
language
English
LU publication?
yes
id
dacbea45-23e3-4603-831a-4840f6e5e2db (old id 8054096)
alternative location
http://www.cinc.org/archives/2014/pdf/0853.pdf
date added to LUP
2015-10-12 11:38:11
date last changed
2019-02-20 06:25:59
@inproceedings{dacbea45-23e3-4603-831a-4840f6e5e2db,
  abstract     = {The purpose of the present study is to determine the feasibility of estimating respiratory information from the built-in pressure sensors of a dialysis machine. The study database consists of simultaneous recordings of pressure signals and capnographic signals from 6 patients during 7 hemodialysis treatment sessions. Respiration rates were estimated using respiratory induced variations in the beat- to-beat interval series of the cardiac component of the pressure signal and respiratory induced baseline varia- tions in the pressure signal, respectively. The estimated respiration rates were compared to a reference respira- tion rate determined from the capnograhpic signal. The root-mean-square error of the estimated respiration rate from the baseline variations of the pressure signal was 2.10 breaths/min; the corresponding error of the estimated res- piration rate from the beat-to-beat interval series of the cardiac component was 4.95 breaths/min. The results sug- gest that it is possible to estimate respiratory information from the pressure sensors.},
  author       = {Sandberg, Frida and Holmer, Mattias and Olde, Bo and Solem, Kristian},
  editor       = {Murray, Alan},
  issn         = {0276-6574},
  language     = {eng},
  location     = {Cambridge, Massachusetts, United States},
  pages        = {853--856},
  publisher    = {Computing in Cardiology},
  title        = {Estimation of Respiratory Information from the Built-In Pressure Sensors of a Dialysis Machine},
  volume       = {41},
  year         = {2014},
}