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Computación y Sistemas

Print version ISSN 1405-5546

Comp. y Sist. vol.8 n.4 México Apr./Jun. 2005

 

Artículos

 

Evaluación de un Detector de Complejo QRS Basado en la Wavelet de Haar, Usando las Bases de Datos MIT–BIH de Arritmias y Europea del Segmento ST y de la Onda T

 

A QRS Detector Based on Haar Wavelet, Evaluation with MIT–BIH Arrhythmia and European ST–T Databases

 

Alfonso Gutiérrez1, Mauricio Lara2 y Pablo R. Hernández2

 

1 Centro de Investigación en Computación – IPN, Departamento de Electrónica.
Av. Juan de Dios Bátiz s/n, México DF, 07738, México.
Tel. (52) (55) 5729 6000 Ext. 56616
e–mail: agutierr@cic.ipn.mx

2 CINVESTAV del IPN, Departamento de Ingeniería Eléctrica.
Av. IPN 2508, México DE 07300, México.

 

Artículo recibido en agosto 16, 2002; aceptado en marzo 09, 2005

 

Resumen

Se desarrolló e implementó como filtro digital recursivo para ser usado en un monitor electrocardiográfico de isquemia cardiaca, un detector en línea de complejos QRS basado en la wavelet de Haar. Se determinó el desempeño del detector usando los archivos disponibles en PhysioNet de las bases datos MIT–BIH de arritmias y Europea del segmento ST y de la onda T. El detector resultante es rápido en la ejecución, fácil de implementar, no acumula error y presentó tasas de error del 1.19% y 0.19% al ser evaluado con las bases mencionadas.

Mediante el coeficiente de correlación y la diferencia máxima en amplitud, se estimó la distorsión causada por los errores de detección en la morfología de latidos promedio. Así, se concluyó que el detector propuesto es apropiado para ser usado en un sistema de monitoreo de isquemia cardíaca y, en general, en cualquier sistema basado en latidos promedio.

Palabras Clave: QRS, Wavelet, Haar, ECG, Isquemia, Monitor.

 

Abstract

In order to be used in a myocardial ischemia monitoring system, an on line QRS complex detector based on Haar wavelet was developed and implemented as a recursive digital filter. The detector performance was determined using the available PhysioNet records of the MIT–BIH arrhythmia and European ST–T databases. The resultant detector is fast in execution, easy to implement, and it does not lead to accumulative error, producing 1.19% and 0.19% error rates with MIT–BIH and European ST–T databases respectively.

The morphological distortion caused in averaged beats by the detection errors was estimated using the correlation coefficient and the maximal amplitude difference. Thus, it was concluded that the proposed detector is proper to be used by an ischemia monitoring system and, in general, by any system based on averaged beats.

Keywords: QRS, Wavelet, Haar, ECG, Ischemia, Monitor.

 

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Referencias

1. Dellborg M., M. Riha and K. Swedberg for the TEAHAT Study Group. "Dynamic QRS–Complex and ST–Segment Monitoring in Acute Myocardial Infarction During Recombinant Tissue–Type Plasminogen Activator Therapy", Am. J. Cardiol., Vol. 67, No. 5, 1991, pp. 343–349.        [ Links ]

2. Goldberger A. L., L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C. K. Peng, H. E. Stanley, "PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals".Circulation, Vol. 101, No. 23, 2000, pp. e215–e220.        [ Links ]

3. Gutiérrez A, P. R. Hernández, M. Lara and S. J. Pérez, "A QRS Detection Algorithm Based on Haar Wavelet", Computers in Cardiology 25, 1998, pp. 353–356.        [ Links ]

4. Hamilton P. S. and W. J. Tompkins, "Quantitative Investigation on QRS Detection Rules Using the MIT/BIH Arrhythmia Database", IEEE Trans. Biomed. Eng., Vol. 33, No. 12, 1986, pp. 1157–1165.        [ Links ]

5. Kadambe S., R. Murray and G. F. Boudreaux–Bartels, "Wavelet Transform–Based QRS Complex Detector", IEEE Trans. Biomed. Eng.,Vol. 46, No. 7, 1999, pp. 838–848.        [ Links ]

6. Kleiger R. E., J. P. Miller, J. T. Bigger, A. J. Moss and the Multicenter Post–Infarction Research Group, "Decreased Heart Rate Variabilíty and lts Association with Increased Mortality After Acute Myocardial Infarction", Am. J. Cardiol., Vol. 59, No. 4, 1987, pp. 256–262.        [ Links ]

7. Krucoff M. W., C. E. Green, L. F. Satler, F. C. Miller, R. S. Pallas, K. M. Kent, A. A. Del Negro, D. L. Pearle, R. D. Fletcher and C. E. Rackley, "Noninvasive Detection of Coronary Artery Patency Using Continuous ST–Segment Monitoring", Am. J. Cardiol., Vol. 57, No. 11, 1986, pp. 916–922.        [ Links ]

8. Li C., C. Zheng and C. Tai, "Detection of ECG Characteristic Points Using Wavelet Transforms", IEEE Trans. Biomed. Eng., Vol. 42, No. 1, 1995, pp. 21–28.        [ Links ]

9. Misiti M., Y. Misiti, G. Oppenheim and J. M. Poggi, Wavelet Toolbox for use whit Matlab, The Math Works Inc., 1996.        [ Links ]

10. Moody G. B., Evaluating ECG Analyzers, Harvard–MIT Division of Health Sciences and Technology, Cambridge, MA, USA. http://ecg.mit.edu/dbag/eval.htm        [ Links ]

11. Pan J. and W. J. Tompkins, "A Real–Time QRS Detection Algorithm", IEEE Trans. Biomed. Eng., Vol. 32, No. 3, 1985, pp. 230– 236.        [ Links ]

12. Rioul O. and M. Vetterli, "Wavelets and Signal Processing", IEEE SP Magazine, Vol. 8, No. 5, 1991, pp. 14–38.        [ Links ]

13. Xue Q., Y. H. Hu and W. J. Tompkins, "Neural–network–based adaptive matched filtering for QRS detection", IEEE Trans. Biomed. Eng., Vol 39, No. 4, 1992, pp. 317–329.        [ Links ]

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