Adaptive parameter estimation of cardiovascular signals using sequential Bayesian techniques

Shwetha Edla, Jun Jason Zhang, John Spanias, Narayan Kovvali, Antonia Papandreou-Suppappola, Chaitali Chakrabarti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationConference Record of the 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010
Pages374-378
Number of pages5
DOIs
StatePublished - Dec 1 2010
Event44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010 - Pacific Grove, CA, United States
Duration: Nov 7 2010Nov 10 2010

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010
Country/TerritoryUnited States
CityPacific Grove, CA
Period11/7/1011/10/10

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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