Electrocardiogram signal modeling with adaptive parameter estimation using sequential bayesian methods

The automatic classification of electrocardiogram (ECG) signals is of great clinical significance in eliminating the strenuous process of manually annotating ECG recordings. Although statistical models describing ECG signal dynamics currently exist, they depend considerably on a priori information and user-specified model parameters. Also, ECG beat morphologies, which vary greatly across different individuals and disease states, cannot easily be described by a single representation. In this paper, we propose sequential Bayesian based methods to effectively model and adaptively select parameters of ECG signals. We first consider an adaptive framework based on a sequential Bayesian tracking method that adaptively selects the best cardiac parameters by minimizing the estimation error and does not require early-stage processing to obtain prior signal information. We then present ECG modeling techniques using the interacting multiple model (IMM) and sequential Markov chain Monte Carlo (SMCMC) methods combined with simultaneous model selection. Both these methods can adaptively choose between different representations to model various ECG beat morphologies without requiring prior ECG information. The performance of the proposed algorithms is demonstrated using real ECG data. Finally, we develop a Bayesian maximum-likelihood based classifier to classify different types of cardiac arrhythmias using which, correction classification rates of 90% and 98% are obtained, when considering features obtained from the estimated model parameters of the adaptive framework, and both the IMM and SMCMC methods, respectively.