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Sequential Markov chain Monte Carlo filter with simultaneous model selection for electrocardiogram signal modeling.
- Source :
-
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2012; Vol. 2012, pp. 4291-4. - Publication Year :
- 2012
-
Abstract
- Constructing statistical models of electrocardiogram (ECG) signals, whose parameters can be used for automated disease classification, is of great importance in precluding manual annotation and providing prompt diagnosis of cardiac diseases. ECG signals consist of several segments with different morphologies (namely the P wave, QRS complex and the T wave) in a single heart beat, which can vary across individuals and diseases. Also, existing statistical ECG models exhibit a reliance upon obtaining a priori information from the ECG data by using preprocessing algorithms to initialize the filter parameters, or to define the user-specified model parameters. In this paper, we propose an ECG modeling technique using the sequential Markov chain Monte Carlo (SMCMC) filter that can perform simultaneous model selection, by adaptively choosing from different representations depending upon the nature of the data. Our results demonstrate the ability of the algorithm to track various types of ECG morphologies, including intermittently occurring ECG beats. In addition, we use the estimated model parameters as the feature set to classify between ECG signals with normal sinus rhythm and four different types of arrhythmia.
- Subjects :
- Computer Simulation
Humans
Markov Chains
Monte Carlo Method
Reproducibility of Results
Sensitivity and Specificity
Algorithms
Arrhythmias, Cardiac diagnosis
Diagnosis, Computer-Assisted methods
Electrocardiography methods
Models, Statistical
Pattern Recognition, Automated methods
Signal Processing, Computer-Assisted
Subjects
Details
- Language :
- English
- ISSN :
- 2694-0604
- Volume :
- 2012
- Database :
- MEDLINE
- Journal :
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
- Publication Type :
- Academic Journal
- Accession number :
- 23366876
- Full Text :
- https://doi.org/10.1109/EMBC.2012.6346915