Access the full text.
Sign up today, get DeepDyve free for 14 days.
Loading next page...
/lp/springer-journals/a-parametric-framework-for-modelling-of-bioelectrical-signals-SYMd3bdUqT
References (6)
-
Y. Muhammad, P. Annus, Y. Moullec, T. Rang (2016)
A parametric framework for the development of bioelectrical applications: application to a bio-impedance signal simulator, 65
-
Y. Mughal, P. Annus, M. Min, R. Gordon (2014)
An overview of the impedance models of the thorax and the origin of the impedance cardiography signal for modelling of the impedance signals2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES)
-
Y. Mughal (2014)
Decomposing of Cardiac and Respiratory Signals from Electrical Bio-impedance Data Using Filtering Method -
Y. Mughal, Y. Moullec, P. Annus, M. Min (2015)
Development of a Bio-Impedance Signal Simulator on the Basis of the Regression Based Model of the Cardiac and Respiratory Impedance Signals -
M. Min, O. Martens, T. Parve (2000)
Lock-in measurement of bio-impedance variationsMeasurement, 27
-
Y. Mughal, Yannick Moullec, P. Annus, A. Krivoshei (2015)
A bio-impedance signal simulator (BISS) for research and training purposes2015 26th Irish Signals and Systems Conference (ISSC)
- Publisher
- Springer Singapore
- Copyright
- © Springer Science+Business Media Singapore 2016
- ISBN
- 978-981-287-968-4
- Pages
- 53 –76
- DOI
- 10.1007/978-981-287-969-1_4
- Publisher site
- See Chapter on Publisher Site
Abstract
[As the basis for the implementation, the EBI measurement method was selected to measure the impedance cardiography (ICG) and impedance respirogram (IRG) signals in order to develop the corresponding signal models; for this purpose, different curve-fitting methods are discussed.]
Published: Jan 6, 2016
Keywords: Fourier Series; Polynomial Model; Separation Algorithm; Impedance Cardiography; Cardiac Signal