The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
An approach to cardiac arrhythmia analysis using hidden Markov models
Abstract
This paper describes a new approach to ECG arrhythmia analysis based on "hidden Markov modeling" (HMM), a technique successfully used since the mid-1970's to model speech waveforms for automatic speech recognition. Many ventricular arrhythmias can be classified by detecting and analyzing QRS complexes and determining R-R intervals. Classification of supraventricular arrhythmias, however, often requires detection of the P wave in addition to the QRS complex. The hidden Markov modeling approach combines structural and statistical knowledge of the ECG signal in a single parametric model. Model parameters are estimated from training data using an iterative, maximum likelihood reestimation algorithm. Initial results suggest that this approach may provide improved supraventricular arrhythmia analysis through accurate representation of the entire beat including the P wave.
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Cites
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
An introduction to hidden Markov models
Rabiner, L., & Juang, B. (1986) · IEEE ASSP Magazine · doi:10.1109/massp.1986.1165342 · Cited by 4,775
Cited by
Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks
Kiranyaz, S., Ince, T., & Gabbouj, M. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2468589 · Cited by 1,912
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
ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform
Martis, R. J., Acharya, U. R., & Min, L. C. (2013) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2013.01.005 · Cited by 722
LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices
Saadatnejad, S., Oveisi, M., & Hashemi, M. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2911367 · Cited by 406
Convolutional Neural Networks for patient-specific ECG classification
Kiranyaz, S., Ince, T., Hamila, R., & Gabbouj, M. (2015) · 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318926 · Cited by 309
Metadata
- Authors
- Coast, D. A., Stern, R. M., Cano, G. G., & Briller, S. A.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 1990
- Volume
- 37
- Issue
- 9
- Pages
- 826-836
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W1978142137
- Cited by
- 475
- References
- 2
- FWCI
- 1.83
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: An approach to cardiac arrhythmia analysis using hidden Markov models. https://app.cochat.ai/papers/10.1109/10.58593
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- Coast, D. A., Stern, R. M., Cano, G. G., & Briller, S. A. (1990). An approach to cardiac arrhythmia analysis using hidden Markov models. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/10.58593
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
