By Topic

Adaptive parameter estimation of cardiovascular signals using sequential Bayesian techniques

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

6 Author(s)
Edla, S. ; Sch. of Electr., Arizona State Univ., Tempe, AZ, USA ; Zhang, J.J. ; Spanias, J. ; Kovvali, N.
more authors

Parameter estimation of biological signals such as the electrocardiogram (ECG) is of key clinical significance and can be used to monitor cardiac health and diagnose heart diseases. However, statistical ECG models with unknown parameters depend upon a priori parameters such as mean cardiac frequency and user-specified parameters such as the number of harmonics in the ECG model. These parameters can vary from patient to patient and with different disease stages. In this paper, we propose a sequential Bayesian tracking method to adaptively select the best cardiac parameters in order to minimize the parameter estimation error. Our results using real ECG data demonstrate the importance of the adaptive algorithm for selecting cardiac parameters at each time instant and show how these parameters can be used to classify different types of ECG signals.

Published in:

Signals, Systems and Computers (ASILOMAR), 2010 Conference Record of the Forty Fourth Asilomar Conference on

Date of Conference:

7-10 Nov. 2010