Electrocardiogram signal modeling using interacting multiple models

Shwetha Edla, Narayan Kovvali, Antonia Papandreou-Suppappola

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

4 Scopus citations


The automatic classification of different heart diseases for monitoring cardiac health through the use of dynamic modeling of electrocardiogram (ECG) signals would yield innovative findings of immense clinical importance. This has been a difficult problem, however, as ECG signals consist of fiducial points with different morphologies within a single heart beat; the points vary between persons and disease states and cannot be described by a single representation. Current statistical ECG models depend on user-specified parameters and a priori information that requires pre-processing. In this paper, we propose a novel method for dynamically modeling, estimating and classifying ECG signals by representing different heart diseases using the interacting multiple model (IMM) algorithm, which can adaptively choose between different representations depending on the ECG data morphology. Using real ECG signals, we demonstrate that the IMM-based model can accurately represent different morphologies with minimal prior information. Using the estimated model parameters as a low-dimensional feature set, we also showed high classification performance between different cardiac arrhythmias.

Original languageEnglish (US)
Title of host publicationConference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
Number of pages5
StatePublished - Dec 1 2011
Event45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011 - Pacific Grove, CA, United States
Duration: Nov 6 2011Nov 9 2011

Publication series

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


Other45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
Country/TerritoryUnited States
CityPacific Grove, CA

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications


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