Electrocardiogram signal modeling using interacting multiple models

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.