The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
ECG beat detection using filter banks
Abstract
We have designed a multirate digital signal processing algorithm to detect heart beats in the electrocardiogram (ECG). The algorithm incorporates a filter bank (FB) which decomposes the ECG into subbands with uniform frequency bandwidths. The FB-based algorithm enables independent time and frequency analysis to be performed on a signal. Features computed from a set of the subbands and a heuristic detection strategy are used to fuse decisions from multiple one-channel beat detection algorithms. The overall beat detection algorithm has a sensitivity of 99.59% and a positive predictivity of 99.56% against the MIT/BIH database. Furthermore this is a real-time algorithm since its beat detection latency is minimal. The FB-based beat detection algorithm also inherently lends itself to a computationally efficient structure since the detection logic operates at the subband rate. The FB-based structure is potentially useful for performing multiple ECG processing tasks using one set of preprocessing filters.
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Citations
Cites
A Real-Time QRS Detection Algorithm
Pan, J., & Tompkins, W. J. (1985) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1985.325532 · Cited by 7,855
Self-citation
Cited by
Heartbeat Classification Using Morphological and Dynamic Features of ECG Signals
Ye, C., Kumar, B. V. K. V., & Coimbra, M. T. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2213253 · Cited by 522
A novel application of deep learning for single-lead ECG classification
Mathews, S. M., Kambhamettu, C., & Barner, K. E. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.013 · Cited by 322
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
Luft, C. D. B., & Bhattacharya, J. (2015) · Scientific Reports · doi:10.1038/srep15717 · Cited by 143
Using photoplethysmography in heart rate monitoring of patients with epilepsy
van Andel, J., Ungureanu, C., Aarts, R., Leijten, F., & Arends, J. (2015) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2015.02.018 · Cited by 39
Metadata
- Authors
- Afonso, V. X., Tompkins, W. J., Nguyen, T. Q., & Luo, S.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 1999
- Volume
- 46
- Issue
- 2
- Pages
- 192-202
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2104614052
- Cited by
- 728
- References
- 1
- FWCI
- 2.94
- Type
- journal article
- Language
- en
- Open access
- closed
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- Afonso, V. X., Tompkins, W. J., Nguyen, T. Q., & Luo, S. (1999). ECG beat detection using filter banks. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/10.740882
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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.
