The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
A Nonlinear Bayesian Filtering Framework for ECG Denoising
Open accessAbstract
In this paper, a nonlinear Bayesian filtering framework is proposed for the filtering of single channel noisy electrocardiogram (ECG) recordings. The necessary dynamic models of the ECG are based on a modified nonlinear dynamic model, previously suggested for the generation of a highly realistic synthetic ECG. A modified version of this model is used in several Bayesian filters, including the Extended Kalman Filter, Extended Kalman Smoother, and Unscented Kalman Filter. An automatic parameter selection method is also introduced, to facilitate the adaptation of the model parameters to a vast variety of ECGs. This approach is evaluated on several normal ECGs, by artificially adding white and colored Gaussian noises to visually inspected clean ECG recordings, and studying the SNR and morphology of the filter outputs. The results of the study demonstrate superior results compared with conventional ECG denoising approaches such as bandpass filtering, adaptive filtering, and wavelet denoising, over a wide range of ECG SNRs. The method is also successfully evaluated on real nonstationary muscle artifact. This method may therefore serve as an effective framework for the model-based filtering of noisy ECG recordings.
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Cites
Probability, Random Variables, and Stochastic Processes.
Abrahams, J., & Papoulis, A. (1984) · Journal of the American Statistical Association · doi:10.2307/2288754 · Cited by 16,226
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000) · Circulation · doi:10.1161/01.cir.101.23.e215 · Cited by 14,809
Fundamentals of Statistical Signal Processing: Estimation Theory
Sengijpta, S. K. (1995) · Technometrics · doi:10.1080/00401706.1995.10484391 · Cited by 11,076
De-noising by soft-thresholding
Donoho, D. L. (1995) · IEEE Transactions on Information Theory · doi:10.1109/18.382009 · Cited by 9,619
Probability, Random Variables, and Stochastic Processes
Miller, I. (1966) · Technometrics · doi:10.1080/00401706.1966.10490365 · Cited by 8,452
GELB, A. (1974) · CERN Document Server (European Organization for Nuclear Research) · Cited by 6,405
A new method for the nonlinear transformation of means and covariances in filters and estimators
Julier, S., Uhlmann, J., & Durrant-Whyte, H. F. (2000) · IEEE Transactions on Automatic Control · doi:10.1109/9.847726 · Cited by 3,735
Kalman Filtering and Neural Networks
(2001) · Wiley eBooks · doi:10.1002/0471221546 · Cited by 2,386
A new approach for filtering nonlinear systems
Julier, S. J., Uhlmann, J. K., & Durrant-Whyte, H. F. (2005) · Proceedings of 1995 American Control Conference - ACC'95 · doi:10.1109/acc.1995.529783 · Cited by 1,926
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Cichocki, A., & Amari, S. (2002) · Wiley eBooks · doi:10.1002/0470845899 · Cited by 1,863
Cited by
EEG artifact removal—state-of-the-art and guidelines
Urigüen, J. A., & Garcia-Zapirain, B. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/3/031001 · Cited by 963
Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains
Kabir, M. A., & Shahnaz, C. (2012) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2011.11.003 · Cited by 444
Zhang, Z., Jung, T.-P., Makeig, S., & Rao, B. D. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2226175 · Cited by 340
Xu, K., Jiang, X., Lin, S., Dai, C., & Chen, W. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2988097 · Cited by 37
Metadata
- Authors
- Sameni, R., Shamsollahi, M. B., Jutten, C., & Clifford, G. D.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2007-12
- Volume
- 54
- Issue
- 12
- Pages
- 2172-2185
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2157209869
- Cited by
- 502
- References
- 31
- FWCI
- 7.99
- Type
- journal article
- Language
- en
- Open access
- green
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