Evaluation of multi-component electrocardiogram beat detection algorithms: Implications of three different noise artifacts

Computers in Cardiology

Volumn 34, Issue , 2007, Pages 521-524

  Last, T ^a,b   Nugent, C D ^a   Owens, F J ^a   Finlay, D D ^a  

^a UNIVERSITY OF ULSTER   (United Kingdom)

^b NONE   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BASELINE DRIFTS; BEAT DETECTIONS; CARDIAC CYCLES; COMMON SOURCES; ECG BEAT DETECTIONS; ECG RECORDINGS; ELECTRODE-SKIN IMPEDANCES; ELECTROMYOGRAPHIC; MOTION ARTIFACTS; MULTI COMPONENTS; MUSCLE CONTRACTIONS; NOISE ARTIFACTS; NOISE SOURCES; SIGNAL-TO-NOISE RATIOS;

ACOUSTIC INTENSITY; ACOUSTIC NOISE; CARDIOLOGY; ELECTROCARDIOGRAPHY; ELECTROCHROMIC DEVICES; SIGNAL TO NOISE RATIO;

SIGNAL DETECTION;

EID: 62949136033     PISSN: 02766574     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIC.2007.4745537     Document Type: Conference Paper

References (17)

1. Nugent CD, Webb JAC, Wright GTH, Black ND. Electrocardiogram I: Pre-processing prior to classification. Automedica, 1998, 16:263-288.
2. Köhler BU, Henning C, Orglmeister R. The principles of Software QRS Detection: Reviewing and Comparing Algorithms for Detecting this Important ECG Waveform. IEEE Engineering in Medicine and Biology, vol. 2, pp.42-57, January/February 2001.
3. Pan J, Tompkins WJ. A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering, 1985, 32:230-236.
4. Abboud S, Sadeh D. The use of cross-correlation function for the alignment of ECG waveforms and rejection of extrasystoles. Computers and Biomedical Research, 1984, 17:258-266.
5. Last T, Nugent CD, Owens FJ. Multi-component based cross correlation beat detection in electrocardiogram analysis. BioMedical Engineering OnLine, 2004, 3:26.
6. Last T, Nugent CD, Owens FJ. Multi-Component Based Neural Network Beat Detection in Electrocardiogram Analysis. Computers in Cardiology, 2006, 33:573-576.
7. Xue Q, Hu YH, Tompkins WJ., Neural-network based adaptive matched filtering for QRS detection. Med. Biol. Eng. Conf., Montreal, Quebec, Canada, 1997, pp.1051-1052.
8. Zhang Q, Manriquez I, Mdeique A, Papelier C, Sorine Y. An Algorithm for Robust and Efficient Location of T- Wave Ends in Electrocardiograms. IEEE Transactions on Biomedical Engineering, vol. 53, issue 12, pp.2544-2552, 2006.
9. Matinez JP, Almeida R, Olmos S, Laguna P. A wavelet-based ECG delineator: evaluation on standard databases. IEEE Transactions on Biomedical Engineering, vol. 51, 4:570-581, 2004.
10. Weng B, Blanco-Velasco M, Barner KE. Baseline Wander Correction in ECG by the Empirical Mode Decomposition. IEEE Bioengineering Conference 2006, pp.135-136, 2006.
11. Amit J, Nimunkar, Tompkins WJ. R-peak Detection and Signal Averaging for Simulated Stress ECG using EMD. IEEE International Conference of Engineering in Medicine and Biology Society, 2007, pp.1261-1264.
12. Friesen GM, Jannett TC, Jadallah MA, Yates SL, Quint SR, Nagle HT. A comparison of the noise sensitivity of nine QRS detection algorithms. IEEE Transactions on Biomedical Engineering, vol.37, no.1, pp. 85-98, 1990.
13. Laguna P, Mark RG, Goldberger A, Moody DG. A database for evaluation of algorithms for measurement the QT and other waveform intervals in the ECH. Computers in Cardiology, 1997, 24:673-676.
14. Massachusetts Institute of Technology. MIT-BIH ECG database. http://ecg.mit.edu/.
15. Massachusetts Institute of Technology. PhysioNet Resource Center. http://www.physionet.org/.
16. Last T, Nugent CD, Owens FJ. Evaluation of electrocardiogram beat detection algorithms: patient specific versus generic training. International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, pp.3208-3211.
17. ANSI/AAMI EC57: Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms (AAMI Recommended Practice / American National Standard), 1998, Available: http://www.aami.org.