This paper deals with a wavelet-based algorithm for automatic detection of isoelectric coordinates of individual QRS loops of VCG record. Fiducial time instants of QRS peak, QRS onset, QRS end, and isoelectric PQ interval are evaluated on three VCG leads ( X , Y , Z ) together with global QRS boundaries of a record to spatiotemporal QRS loops alignment. The algorithm was developed and optimized on 161 VCG records of PTB diagnostic database of healthy control subjects (HC), patients with myocardial infarction (MI) and patients with bundle branch block (BBB) and validated on CSE multilead measurement database of 124 records of the same diagnostic groups. The QRS peak was evaluated correctly for all of 1,467 beats. QRS onset, QRS end were detected with standard deviation of 5,5 ms and 7,8 ms respectively from the referee annotation. The isoelectric 20 ms length PQ interval window was detected correctly between the P end and QRS onset for all the cases. The proposed algorithm complies the ( 2 σ C S E ) limits for the QRS onset and QRS end detection and provides comparable or better results to other well-known algorithms. The algorithm evaluates well a wide QRS based on automated wavelet scale switching. The designed multi-lead approach QRS loop detector accomplishes diagnostic VCG processing, aligned QRS loops imaging and it is suitable for beat-to-beat variability assessment and further automatic VCG classification.

Department of Cybernetics and Biomedical Engineering Faculty of Electrical Engineering and Computer Science VSB Technical University of Ostrava Ostrava Poruba Czechia

Department of Surgical Studies Faculty of Medicine of the University of Ostrava Ostrava Czechia

Faculty of Electrical Engineering and Information Technology University of Žilina Žilina Czechia

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Aldroubi A., Unser M. (1996). Wavelets in medicine and biology. Boca Raton: CRC Press, 616. Google Scholar

Altmann J. (1996). Wavelet basics. online]. [cit. 2015-05-11]: http://www.wavelet.org/tutorial/wbasic.htm. Google Scholar

Arzeno N. M., Poon C. S., Deng Z. D. (2006). Quantitative analysis of QRS detection algorithms based on the first derivative of the ECG. New York, USA: International Conference of the IEEE Engineering in Medicine and Biology Society, 1788–1791. 10.1109/IEMBS.2006.260051 10.1109/IEMBS.2006.260051 | Google Scholar PubMed DOI

Bousseljot R., Kreiseler D., Schnabel A. (1995). Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet. Biomed. Technik/Biomedical Eng. 1, 317–318. 10.1515/bmte.1995.40.s1.317 10.1515/bmte.1995.40.s1.317 | Google Scholar DOI

Burch G. E. (1985). The history of vectorcardiography. Med. Hist. 29, 103–131. 10.1017/S002572730007054X 10.1017/S002572730007054X | Google Scholar PubMed DOI PMC

Chazal P., Celler B. (1996). “Automatic measurement of the QRS onset and offset in individual ECG leads,” in Presented at the 18th Ann. Int. Conf.. (Amsterdam, Netherlands: IEEE Eng. Med. Biol. Soc.). Google Scholar

CSE WORKING PARTY (1985). Recommendations for measurement standards in quantitative electrocardiography. The CSE Working Party. Eur. Heart J. 6, 815 PubMed Abstract | Google Scholar PubMed

Dinh H. A. N., Kumar D. K., Pah N. D., Burton P. (2001). “Wavelets for QRS detection,” in Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Istanbul, Turkey, October 25–28, 2001, 207–211. 10.1109/iembs.2001.1020593 PubMed Abstract | 10.1109/iembs.2001.1020593 | Google Scholar PubMed DOI

Dokur Z., Olmez T., Yazgan E., Ersoy O. K. (1997). Detection of ECG waveforms by neural networks. Med. Eng. Phys. 198, 738–741. 10.1016/s1350-4533(97)00029-5 PubMed Abstract | 10.1016/s1350-4533(97)00029-5 | Google Scholar PubMed DOI

Gatzoulis K. A., Arsenos P., Trachanas K., Dilaveris P., Antoniou C., Tsiachris D., et al. (2018). Signal‐averaged electrocardiography: Past, present, and future. J. Arrhythm. 343, 222–229. 10.1002/joa3.12062 PubMed Abstract | 10.1002/joa3.12062 | Google Scholar PubMed DOI PMC

Goldberger A. L., Amaral L. A. N., Glass L., Hausdorff J. M., Ivanov P. Ch., Mark R. G., et al. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 101 (23), e215–e220. 10.1161/01.cir.101.23.e215 PubMed Abstract | 10.1161/01.cir.101.23.e215 | Google Scholar PubMed DOI

Guven O., Eftekhar A., Hoshyar R., Frattini G., Kindt W., Constandinou T. G. (2014). “Realtime ECG baseline removal: An isoelectric point estimation approach,” in 2014 IEEE Biomedical Circuits and Systems Conference (BioCAS) Proceedings, Lausanne, Switzerland, October 22–24, 2014, 29–32. 10.1109/BioCAS.2014.6981637 10.1109/BioCAS.2014.6981637 | Google Scholar DOI

Klingfield P., Gettes L. S., Bailey J. J., Childers R., Deal B. J., Hancock E. W., et al. (2007). Recommendations for the standardization and interpretation of the electrocardiogram: Part I: The electrocardiogram and its technology: A scientific statement from the American heart association electrocardiography and arrhythmias committee, council on clinical cardiology; the American college of cardiology foundation; and the heart rhythm society: Endorsed by the international society for computerized electrocardiology. Circulation 115 (10), 1306–1324. 10.1161/CIRCULATIONAHA.106.180200 PubMed Abstract | 10.1161/CIRCULATIONAHA.106.180200 | Google Scholar PubMed DOI

Kumar A., Berwal D., Kumar Y. (2018). Design of high-performance ECG detector for implantable cardiac pacemaker systems using biorthogonal wavelet transform. Circuits Syst. Signal Process. 379, 3995–4014. 10.1007/s00034-018-0754-3 10.1007/s00034-018-0754-3 | Google Scholar DOI

Laguna P., Jané R., CanimaL P. (1994). Automatic detection of wave boundaries in multilead ECG signals: Validation with the CSE database. Comput. Biomed. Res. 27 (1), 45–60. 10.1006/cbmr.1994.1006 PubMed Abstract | 10.1006/cbmr.1994.1006 | Google Scholar PubMed DOI

Louis A. K., Maass D., Rieder A. W. (1997). Theory and applications. Chichester, England. Google Scholar

Mahmoodabadi S. Z., Ahmadian A., Abolhasani M. D., Eslami M., Bidgoli H J. (2005). “ECG feature extraction based on multiresolution wavelet transform,” in 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, September 1–4, 2005, 3902–3905. 10.1109/IEMBS.2005.1615314 10.1109/IEMBS.2005.1615314 | Google Scholar PubMed DOI

Malmivuo J., Plonsey R. (1995). Bioelectromagnetism: Principles and applications of bioelectric and biomagnetic fields, XXII. New York: Oxford University Press, 482. Google Scholar

Martínez J., Almeida R., Olmos S., Rocha A. P., Laguna P. (2004). A wavelet-based ECG delineator: Evaluation on standard databases. IEEE Trans. Biomed. Eng. 514, 570–581. 10.1109/TBME.2003.821031 10.1109/TBME.2003.821031 | Google Scholar PubMed DOI

Matveev M., Krasteva V., Naydenov S., Donova Ta. (2007). Possibilities of signal-averaged orthogonal and vector electrocardiography for locating and evaluating the size of acute myocardial infarction. J. Electrocardiol. 40, S62–S63. 10.1016/j.jelectrocard.2007.03.059, 10.1016/j.jelectrocard.2007.03.059 | Google Scholar PubMed DOI

Mazomenos E. B. (2012). “A time-domain morphology and gradient based algorithm for ECG feature extraction,” in Industrial Technology (ICIT), 2012 IEEE International Conference (IEEE; ), 117–122. 10.1109/icit.2012.6209924 | Google Scholar DOI

Medteq (2022). ECG filters. [cit. 2022-05-10]. Available at: http://www.medteq.info/med/ECGFilters Google Scholar

Pan J., Tompkins W. J. (1985). A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 3, 230–236. 10.1109/TBME.1985.325532 PubMed Abstract | 10.1109/TBME.1985.325532 | Google Scholar PubMed DOI

Pavlov Z., Abel H. (1975). Physiological correlations of the heart generator system, the electrical properties of the heart and body structures. Theory of cardioelectric space. Adv. Cardiol. 19, 10–11. 10.1159/000399609 10.1159/000399609 | Google Scholar PubMed DOI

Penhaker M., Darebníková M., Jurek F., Augustynek M. (2014). “Evaluation of electrocardiographic leads and establishing significance intra-individuality,” in Innovations in bio-inspired computing and applications. Advances in intelligent systems and computing, Cham, Germany. Editors Abraham A., Krömer P., Snášel V., 237 10.1007/978-3-319-01781-5_27 | Google Scholar DOI

Sahambi J. S., Tandon S., Bhatt R. K. P. (1997). Using wavelet transforms for ECG characterization. An on-line digital signal processing system. IEEE Eng. Med. Biol. Mag. 16 (1), 77–83. 10.1109/51.566158 PubMed Abstract | 10.1109/51.566158 | Google Scholar PubMed DOI

Sivannarayana N., Reddy D. C. (1999). Biorthogonal wavelet transforms for ECG parameters estimation. Med. Eng. Phys. 213, 167–174. 10.1016/s1350-4533(99)00040-5 PubMed Abstract | 10.1016/s1350-4533(99)00040-5 | Google Scholar PubMed DOI

Soria-Olivas E., Martinez-SoberM., Calpe-Maravilla J., Guerrero-Martinez J. F., Chorro-Gasco J., Espi-Lopez J. (1998). Application of adaptive signal processing for determining the limits of P and T waves in an ECG. IEEE Trans. Biomed. Eng. 45, 1077–1080. 10.1109/10.704877 PubMed Abstract | 10.1109/10.704877 | Google Scholar PubMed DOI

Vila J., Gang Y., Presedo J., Fernandez-Delgado M., Malik M. (2000). A new approach for TU complex characterization. IEEE Trans. Biomed. Eng. 47, 764–772. 10.1109/10.844227 PubMed Abstract | 10.1109/10.844227 | Google Scholar PubMed DOI

Vullings H., Verhaegen M., Verbruggen H. (1998). “Automated ECG segmentation with dynamic time warping,” in Proc. 20th ann. Int. Conf. IEEE engineering in medicine and biology soc.. Hong Kong, China: (Hong Kong; ), 163 Google Scholar

Willems J. L., Arnaud P., Jan H., Bourdillon P. J., Degani R., Denis B., et al. (1985). Establishment of a reference library for evaluating computer ECG measurement programs. Comput. Biomed. Res. 18, 439–457. 10.1016/0010-4809(85)90021-7 PubMed Abstract | 10.1016/0010-4809(85)90021-7 | Google Scholar PubMed DOI

Willems J. L., Arnaud P. V., Jan H., Degani R., Macfarlane P. W., Zywietz C. (1990). Common standards for quantitative electrocardiography: Goals and main results. Methods Inf. Med. 29 (4), 263–271. 10.1055/s-0038-1634793 PubMed Abstract | 10.1055/s-0038-1634793 | Google Scholar PubMed DOI

Zhao G., Jiang D., Diao J., Qian L. (2004). “Application of wavelet time-frequency analysis on fault diagnosis for steam turbine;” in 5th International Conference of Acoustical and Vibratory Surveillance Methods and Diagnostic Techniques, France, CETIM, Senlis, France, 11–13. Google Scholar

Representative QRS loop of the VCG record evaluation