The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
ECG signal analysis through hidden Markov models
Abstract
This paper presents an original hidden Markov model (HMM) approach for online beat segmentation and classification of electrocardiograms. The HMM framework has been visited because of its ability of beat detection, segmentation and classification, highly suitable to the electrocardiogram (ECG) problem. Our approach addresses a large panel of topics some of them never studied before in other HMM related works: waveforms modeling, multichannel beat segmentation and classification, and unsupervised adaptation to the patient's ECG. The performance was evaluated on the two-channel QT database in terms of waveform segmentation precision, beat detection and classification. Our waveform segmentation results compare favorably to other systems in the literature. We also obtained high beat detection performance with sensitivity of 99.79% and a positive predictivity of 99.96%, using a test set of 59 recordings. Moreover, premature ventricular contraction beats were detected using an original classification strategy. The results obtained validate our approach for real world application.
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Citations
Cites
A Wavelet Tour of Signal Processing
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A Practical Guide to Wavelet Analysis
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Fundamentals of speech recognition
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A Wavelet-Based ECG Delineator: Evaluation on Standard Databases
Martinez, J. P., Almeida, R., Olmos, S., Rocha, A. P., & Laguna, P. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2003.821031 · Cited by 1,605
Detection of ECG characteristic points using wavelet transforms
Li, C., Zheng, C., & Tai, C. (1995) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.362922 · Cited by 1,597
The principles of software QRS detection
Kohler, B.-U., Hennig, C., & Orglmeister, R. (2002) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/51.993193 · Cited by 1,242
A patient-adaptable ECG beat classifier using a mixture of experts approach
Hu, Y. H., Palreddy, S., & Tompkins, W. J. (1997) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.623058 · Cited by 614
Laguna, P., Mark, R. G., Goldberg, A., & Moody, G. B. (2002) · Computers in Cardiology 1997 · doi:10.1109/cic.1997.648140 · Cited by 606
Automatic Detection of Wave Boundaries in Multilead ECG Signals: Validation with the CSE Database
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An approach to cardiac arrhythmia analysis using hidden Markov models
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Cited by
Swallow segmentation with artificial neural networks and multi-sensor fusion
Lee, J., Steele, C. M., & Chau, T. (2009) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2009.07.001 · Cited by 40
Metadata
- Authors
- Andreao, R. V., Dorizzi, B., & Boudy, J.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2006-08
- Volume
- 53
- Issue
- 8
- Pages
- 1541-1549
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2167274237
- Cited by
- 293
- References
- 23
- FWCI
- 8.53
- Type
- journal article
- Language
- en
- Open access
- closed
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