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2020, Number 3

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Rev Cubana Invest Bioméd 2020; 39 (3)

Orthogonalization of electrocardiographic derivations

Guerrero SG, Noriega AM
Full text How to cite this article

Language: Spanish
References: 14
Page: 1-16
PDF size: 411.02 Kb.


Key words:

electrocardiographic signal multiderivation delineator, wavelet transform, electrocardiographic signal derivation orthogonalization.

ABSTRACT

Introduction: The wavelet transform-based multiderivation electrocardiographic (ECG) signal delineator has high spatial resolution and makes it possible to eliminate interderivation differences traditionally appearing in uniderivation methods. But this requires electrocardiographic signal derivations orthogonal to one another to obtain a spatial loop.
Objective: Develop orthogonalization methods of two or three electrographic signal derivations allowing generalization of the wavelet transform-based multiderivation delineator in any electrographic signal database with more than one derivation.
Methods: Three orthogonalization methods were implemented for electrocardiographic signal derivations: vector projection-based two-derivation orthogonalization, principal component-based orthogonalization, and orthogonalization based on the Gram-Schmidt classic method.
Results: A comparison was performed between the operation of the ECG multiderivation delineator when used with each orthogonalization method. The comparison was based on estimation of the arithmetic mean and standard deviation bearing in mind different combinations of derivations from both databases for each of the marks analyzed. The best results were obtained with the principal component analysis method and the worst ones with the two-derivation orthogonalization method.
Conclusions: The orthogonalization algorithms obtaining the best results were those based on three orthogonal derivations, in which decomposition into principal components was slightly higher. This is therefore considered to be the most appropriate method for generalization of the multiderivation delineator.


REFERENCES

  1. Serra MA, Serra M, Viera M. Las enfermedades crónicas no transmisibles: magnitud actual y tendencias futuras. Rev Finlay. 2018 [acceso: 22/01/2020]; 8(2):140-8. Disponible en: Disponible en: https://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S2221-24342018000200008

  2. Asamblea Mundial de la Salud. Informe sobre los resultados de la OMS. Presupuesto por programas 2018-2019: examen de mitad de periodo. Organización Mundial de Salud. 2019 [acceso: 22/01/2020];72. Disponible en: Disponible en: https://apps.who.int/iris/handle/10665/328788

  3. Martínez JP, Almeida R, Olmos S, Rocha AP, Laguna P. A Wavelet-Based ECG Delineator: Evaluation on Standard Databases. IEEE Trans Biomed Eng. 2004 [acceso: 22/01/2020];51(4):570-81. Disponible en: Disponible en: http://diec.unizar.es/~laguna/personal/publicaciones/wavedet_tbme04.pdf

  4. Almeida R, Martínez JP, Rocha AP, Laguna P. Multilead ECG delineation using spatially projected leads from wavelet transform loops. IEEE Trans Biomed Eng . 2009 [acceso: 20/01/2020];56(8):1996-2005. Disponible en: Disponible en: https://ieeexplore.ieee.org/document/4915796

  5. Almeida R. ECG Characterization: Application to QT Interval Variability [PhD]. [Portugal]: Universidade do Porto; 2007.

  6. Mallat S, Zhong S. Characterization for signals from multiscale edge. IEEE Trans Pattern Anal Mach Intell. 1992 [acceso: 21/01/2020]; 14:710-32. Disponible en: Disponible en: https://www.di.ens.fr/~mallat/papiers/MallatEdgeCharact92.pdf

  7. Malmivuo J, Plonsey R. Bioelectromagnetism - Principles and Applications of Bioelectric and Biomagnetic Fields. New York: Oxford University Press; 1995.

  8. Berati G. Gram - Schmidt Process in Different Pararell Platform. Int J Adv Res Artif Intell. 2015 [acceso: 21/01/2020]; 4(6):35-9. Disponible en: Disponible en: https://thesai.org/Downloads/IJARAI/Volume4No6/Paper_6-Gram_Schmidt_Process_in_Different_Parallel_Platforms.pdf

  9. Lever J, Krzywinski M, Altman N. Points of Significance. Principal component analysis. Nat Methods. 2017 [acceso: 22/01/2020]; 14(7):641-2. Disponible en: Disponible en: https://www.nature.com/articles/nmeth.4346

  10. Jolliffe IT, Cadima J. Principal component analysis: a review and recent developments. Phil Trans R Soc. 2016 [acceso: 20/01/2020]; 374:1-16. Disponible en: Disponible en: https://royalsocietypublishing.org/doi/10.1098/rsta.2015.0202

  11. Recommendations for measurement standards in quantitative electrocardiography. The CSE Working Party. Eur Heart J. 1985;6(10):815-25.

  12. Christov I, Otsinsky I, Simova I, Prokopova R, Trendafilova E, Naydenov S. Dataset of manually measured QT intervals in the electrocardiogram. Biomedical Eng Online. 2006 [acceso: 25/01/2020]; 5(31). Disponible en: Disponible en: https://biomedical-engineering-online.biomedcentral.com/articles/10.1186/1475-925X-5-3

  13. Jasak Z. Benford’s Law and Wilcoxon Test. J Math Sci Adv Appl. 2018 [acceso: 23/01/2020]; 52:69-81. Disponible en: Disponible en: http://scientificadvances.co.in/admin/img_data/1278/images/JMSAA7100121981ZoranJasak.pdf

  14. Noriega M, Carcases E, Durán K, Marañón EJ, Martínez JP, Almeida R. Instantaneous respiratory rate estimation from multilead ECG delineation using VCG directions, in Computers in Cardiology. IEEE Comput Soc Press. 2016 [acceso: 25/01/2020]; 43:397-400. Disponible en: Disponible en: https://zaguan.unizar.es/record/63118/files/texto_completo.pdf




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Rev Cubana Invest Bioméd. 2020;39