By Topic

Application of pattern recognition and image classification techniques to determine continuous cardiac output from the arterial pressure waveform

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Martin, James F. ; Med. Devices & Diagnostics Div., Eli Lilly & Co., Indianapolis, IN, USA ; Volfson, L.B. ; Kirzon-Zolin, V.V. ; Schukin, V.G.

The shape of the arterial pressure waveform is a nonlinear function of stroke volume, heart rate and many other cardiovascular parameters. Previous attempts have been made to exploit this relationship and derive cardiac output (CO) from the arterial pressure waveform. These classical "pulse-contour" methods utilized simplifying linear assumptions, as a result they failed to adequately estimate CO over a sufficiently wide range of hemodynamic conditions. The authors have applied pattern recognition and image professing techniques to the problem of deriving CO from the arterial pressure waveform, thereby eliminating the need for simplifying assumptions. Computer simulations were used to develop the basic pattern recognition algorithms and compare their performance with that of published classical "pulse-contour" methods. Animal models were subsequently used to demonstrate proof of the concept. For over 200,000 individual heart beats, covering a wide range of hemodynamic conditions, the mean error, in calculated CO compared to ultrasonic flow probe determined CO, was 2.8% with a standard deviation of 9.8%.

Published in:

Biomedical Engineering, IEEE Transactions on  (Volume:41 ,  Issue: 10 )