The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A Wavelet-Based ECG Delineator: Evaluation on Standard Databases
Abstract
In this paper, we developed and evaluated a robust single-lead electrocardiogram (ECG) delineation system based on the wavelet transform (WT). In a first step, QRS complexes are detected. Then, each QRS is delineated by detecting and identifying the peaks of the individual waves, as well as the complex onset and end. Finally, the determination of P and T wave peaks, onsets and ends is performed. We evaluated the algorithm on several manually annotated databases, such as MIT-BIH Arrhythmia, QT, European ST-T and CSE databases, developed for validation purposes. The QRS detector obtained a sensitivity of Se = 99.66% and a positive predictivity of P+ = 99.56% over the first lead of the validation databases (more than 980,000 beats), while for the well-known MIT-BIH Arrhythmia Database, Se and P+ over 99.8% were attained. As for the delineation of the ECG waves, the mean and standard deviation of the differences between the automatic and manual annotations were computed. The mean error obtained with the WT approach was found not to exceed one sampling interval, while the standard deviations were around the accepted tolerances between expert physicians, outperforming the results of other well known algorithms, especially in determining the end of T wave.
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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
A comparison of the noise sensitivity of nine QRS detection algorithms
Friesen, G. M., Jannett, T. C., Jadallah, M. A., Yates, S. L., Quint, S. R., & Nagle, H. T. (1990) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.43620 · Cited by 1,043
Cited by
Wavelet transforms and the ECG: a review
Addison, P. S. (2005) · Physiological Measurement · doi:10.1088/0967-3334/26/5/r01 · Cited by 908
Gil, E., Orini, M., Bailón, R., Vergara, J. M., Mainardi, L., & Laguna, P. (2010) · Physiological Measurement · doi:10.1088/0967-3334/31/9/015 · Cited by 461
Self-citationDrowsiness detection using heart rate variability
Vicente, J., Laguna, P., Bartra, A., & Bailón, R. (2016) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-015-1448-7 · Cited by 255
Self-citationMachine learning in the electrocardiogram
Mincholé, A., Camps, J., Lyon, A., & Rodríguez, B. (2019) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2019.08.008 · Cited by 147
Machine learning improves risk stratification after acute coronary syndrome
Myers, P. D., Scirica, B. M., & Stultz, C. M. (2017) · Scientific Reports · doi:10.1038/s41598-017-12951-x · Cited by 75
“Overall, approximately 45 minutes of data were required on average to obtain the requisite number of beats where the beginning and ending of each ST segment could be unambiguously identified using an automated method for identifying individual components of each beat 38 .”
Metadata
- Authors
- Martinez, J. P., Almeida, R., Olmos, S., Rocha, A. P., & Laguna, P.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2004-04
- Volume
- 51
- Issue
- 4
- Pages
- 570-581
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2127510558
- Cited by
- 1,605
- References
- 2
- FWCI
- 18.59
- Type
- journal article
- Language
- en
- Open access
- closed
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