P- and T-Wave Delineation in ECG Signals Using a Bayesian Approach and a Partially Collapsed Gibbs Sampler
Authors:
- 1 December 2010
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Biomedical Engineering
- Vol. 57 (12) , 2840-2849
Abstract
Detection and delineation of P- and T-waves are important issues in the analysis and interpretation of electrocardiogram (ECG) signals. This paper addresses this problem by using Bayesian inference to represent a priori relationships among ECG wave components. Based on the recently introduced partially collapsed Gibbs sampler principle, the wave delineation and estimation are conducted simultaneously by using a Bayesian algorithm combined with a Markov chain Monte Carlo method. This method exploits the strong local dependency of ECG signals. The proposed strategy is evaluated on the annotated QT database and compared to other classical algorithms. An important feature of this paper is that it allows not only for the detection of P- and T-wave peaks and boundaries, but also for the accurate estimation of waveforms for each analysis window. This can be useful for some ECG analysis that require wave morphology information.Keywords
This publication has 30 references indexed in Scilit:
- Table of contentsIEEE Transactions on Biomedical Engineering, 2009
- Improving ECG Beats Delineation With an Evolutionary Optimization ProcessIEEE Transactions on Biomedical Engineering, 2008
- Model-based ECG fiducial points extraction using a modified extended Kalman filter structure2008
- Joint Segmentation of Piecewise Constant Autoregressive Processes by Using a Hierarchical Model and a Bayesian Sampling ApproachIEEE Transactions on Signal Processing, 2007
- Nested median filtering for detecting T-wave offset in ECGsElectronics Letters, 2002
- A new approach for TU complex characterizationIEEE Transactions on Biomedical Engineering, 2000
- General Methods for Monitoring Convergence of Iterative SimulationsJournal of Computational and Graphical Statistics, 1998
- Syntactic pattern recognition of the ECGIEEE Transactions on Pattern Analysis and Machine Intelligence, 1990
- Detection of the P and T waves in an ECGComputers and Biomedical Research, 1989
- A Real-Time QRS Detection AlgorithmIEEE Transactions on Biomedical Engineering, 1985