Name
Title | ||
|---|---|---|
Electrocardiogram Signal Modeling With Adaptive Parameter Estimation Using Sequential Bayesian Methods |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TSP.2014.2312316 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
Bayes methods,Markov processes,Monte Carlo methods,adaptive estimation,electrocardiography,maximum likelihood sequence estimation,medical signal processing,parameter estimation,signal classification,signal representation,Bayesian maximum-likelihood based classifier,ECG beat morphology,ECG signal classification,IMM,SMCMC method,adaptive parameter estimation,cardiac arrhythmias parameter,electrocardiogram signal classification,estimation error minimizing,interacting multiple model,manually annotating ECG recording,sequential Bayesian tracking method,sequential Markov chain Monte Carlo method,signal representation,statistical model,user-specified model parameter,Electrocardiogram signal,Monte Carlo techniques,automatic classification,interacting multiple models,parameter estimation,sequential Bayesian tracking | Markov process,Pattern recognition,Markov chain Monte Carlo,Computer science,A priori and a posteriori,Model selection,Statistical model,Artificial intelligence,Estimation theory,Classifier (linguistics),Bayesian probability | Journal |
Volume | Issue | ISSN |
62 | 10 | 1053-587X |
Citations | PageRank | References |
4 | 0.40 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shwetha Edla | 1 | 4 | 0.40 |
| Narayan Kovvali | 2 | 25 | 5.57 |
| Antonia Papandreou-Suppappola | 3 | 234 | 29.88 |