Vol. 12 Núm. 2 (2013): Revista UIS Ingenierías
Artículos

Tratamiento de rizados en la estimación de la densidad espectral de potencia de la señal de ritmo cardiaco

Javier Enrique González-Barajas
Universidad Santo Tomás
Biografía
Edwin Francisco-Forero
Universidad Santo Tomás
Biografía
Iván Marino-Martinez
Universidad Santo Tomás
Biografía

Publicado 2013-11-23

Palabras clave

  • Desidad Espectral de Potencia,
  • muestreo irregular,
  • tiempos RR

Cómo citar

González-Barajas, J. E., Francisco-Forero, E., & Marino-Martinez, I. (2013). Tratamiento de rizados en la estimación de la densidad espectral de potencia de la señal de ritmo cardiaco. Revista UIS Ingenierías, 12(2), 17–27. Recuperado a partir de https://revistas.uis.edu.co/index.php/revistauisingenierias/article/view/17-27

Resumen

El análisis de la Variabilidad de la Frecuencia cardiaca (VFC) está basado en el estudio de los cambios detectados en cada ciclo cardiaco. Estos cambios son estudiados a partir de la señal de ritmo cardiaco y se compone de las medidas adquiridas del tiempo entre las ondas R de la señal electrocardiográfica (ECG). El análisis de la señal de ritmo cardiaco está basado en dos tipos de métodos: cálculos estadísticos (dominio del tiempo) y estimación de la densidad espectral de potencia (Dominio de la frecuencia). La Densidad Espectral de Potencia (PSD) de la señal de ritmo cardiaco puede realizarse a través de métodos aplicados a señales con un tiempo de muestreo irregular. Para este caso, la literatura ha registrado el uso del método de Lomb. El principal objetivo de este trabajo es la presentación de los resultados obtenidos de la implementación de la técnica basada en el promediado de espectros para realizar un tratamiento de la PSD estimada a partir de la señal de ritmo cardiaco. El procedimiento está basado en tomar señales de ritmo cardiaco adquiridas de señales electrocardiograficas con ritmo sinusal normal de la base de datos “Physionet”. Los resultados obtenidos permitieron ilustrar una atenuación del risado de la PSD estimada.

 

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Referencias

  1. XIAOMEN Cui; Giaomeng, “A NEW real-time ECG R-wave detection algorithm,” Strategic Technology (IFOST), 2011 6th International Forum on , vol.2, no.1, pp.1252,1255, 22-24 Aug. 2011.
  2. LIHUANG She; Guohua Wang; Shi Zhang; Jinshuan Zhao, “An Adaptive Threshold Algorithm Combining Shifting Window Difference and Forward-Backward Difference in Real-Time R-Wave Detection,” Image and Signal Processing, 2009. CISP ‘09. 2nd International Congress on , vol.1, no.1, pp.1,4, 17-19 Oct. 2009.
  3. GONZÁLEZ J, “Cálculo del umbral para la detección de la onda R del complejo Cardiaco”, Tecno Lógicas, vol.17,no 32,pp 47-55, 2014.
  4. ZHONG Yue; Lisheng Xu; Li Yan; Yanhua Shen; Shuhong Wang, “Adaptive R-wave detection method in dynamic ECG with heavy EMG artifact,” Information and Automation (ICIA), 2012 International Conference on , vol.1, no.1, pp.83,87, 6-8 June 2012. Malik M. Clinical Guide to Cardiac Autonomic Tests. Kluwer Academic Publisher. Netherland 2010. Pag 149.
  5. LAGUNA, P.; Moody, G.B.; Mark, R.G., “Power spectral density of unevenly sampled heart rate data,” Engineering in Medicine and Biology Society, 1995., IEEE 17th Annual Conference , vol.1, no.1, pp.157,158 vol.1, 20-25 Sep 1995.
  6. CESARELLI, M.; Romano, M.; Ruffo, M.; Bifulco, P.; Pasquariello, G.; Fratini, A., “PSD modifications of FHRV due to CTG storage rate,” Information Technology and Applications in Biomedicine, 2009. ITAB 2009. 9th International Conference on, vol.1, no.1, pp.1,4, 4-7 Nov. 2009.
  7. ZHIGUA Zhang; Shing-Chow Chan; Chong Wang, “A New Regularized Adaptive Windowed Lomb Periodogram for Time–Frequency Analysis of Nonstationary Signals With Impulsive Components,” Instrumentation and Measurement, IEEE Transactions on , 2012, vol.61, no.8, pp.2283,2304
  8. GOMBARSKA, D.; Horicka, M., “Evaluation of heart rate variability in time — Frequency domain,” ELEKTRO, 2012 , vol.1, no.1, pp.415,418
  9. THONG, T.; Yung, I.O.; Zajdel, D.P.; Ellingson, R.M.; McNames, James; Aboy, M.; Oken, B.S., “Heart rate variability analysis of effect of nicotine using periodograms,” Engineering in Medicine and Biology Society, 2004. IEMBS ‘04. 26th Annual International Conference of the IEEE , vol.1, no.1, pp.294,297, 1-5 Sept. 2004.
  10. BANSANL, D.; Khan, M.; Salhan, A.K., “A Review of Measurement and Analysis of Heart Rate Variability,” Computer and Automation Engineering, 2009. ICCAE ‘09. International Conference on , vol.1, no.1, pp.243,246, 8-10 March 2009 PASSLER, S.; Noack, A.; Poll, R.; Fischer, W.- J., “Validation of the use of heart rate variability measurements during meal intake in humans,”Computing in Cardiology Conference (CinC), 2013 , vol.1, no.1, pp.999,1002, 22-25 Sept. 2013.
  11. N. R. Lomb, “Least-squares frequency analysis of unequally spaced data,” Astrophysics and Space Science, vol. 39, no. 2, pp. 447–462, 1976.
  12. PRESS, W., Numerical Recipes 3r Edition. Hong Kong, China: Cambridge University Press,2007. Pp685.
  13. LAGUNA P., G. B. Moody and R.G. Mark. Power Spectra Density of Unevenly Sampled Data by LeastSquare Analysis: Performance and Application to Heart Rate Signals. IEEE Trans. onBiomedicalEngineering Vol. 45, No. 6. June. pp. 698-715. 1998.
  14. SHAO-YEN Tseng; Wai-Chi Fang; , “An effective heart rate variability processor design based on timefrequency analysis algorithm using windowed Lomb periodogram,” Biomedical Circuits and Systems Conference (BioCAS), 2010 IEEE , 2010, vol.1, no.1, pp.82-85, 3-5 Nov.
  15. ZHIGUO Zhang; Shing-Chow Chan; , “Robust adaptive Lomb periodogram for time-frequency analysis of signals with sinusoidal and transient components,” Acoustics, Speech, and Signal Processing, 2005. Proceedings.
  16. (ICASSP ‘05). IEEE International Conference on , vol.4, no., pp. iv/493- iv/496 Vol. 4, 18-23.
  17. ZHANG, Z.G.; Cai, X.L.; Chan, S.C.; Hu, Y.; Hu, L.; Chang, C.Q.; , “Time-frequency coherence analysis of multi-channel eventrelated potential using adaptive windowed Lomb periodogram,” Neural Engineering, 2009. NER ‘09. 4th International IEEE/EMBS Conference on ,2009, vol., no., pp.657-660, April 29 .
  18. ZHANG, Z.G.; Chan, S.C.; , “Harmonic analysis of power system signals using a new regularized adaptive windowed Lomb periodogram,” Green Circuits and Systems (ICGCS), 2010 International Conference on ,2010, vol., no., pp.567-572, 21-23 .
  19. THONG, T.; McNames, J.; Aboy, M.; , “LombWechperiodogram for non-uniform sampling,” Engineering in Medicine and Biology Society, 2004. IEMBS ‘04. 26th Annual International Conference of the IEEE , 2004,vol.1, no.1, pp.271-274, 1-5
  20. HUI-BOMENG; Yan-li Gao; Zhi-qiang Liu; Yanfang Yu; Jian-hua Wu; , “Analysis of Turbulent Characteristics of Unevenly Velocity Signals in KSM Used Empirical Mode Decomposition and Lomb Periodogram,” Information Engineering and Computer Science (ICIECS), 2010 2nd International Conference on ,2010, vol.1, no.1, pp.1-4, 25-26.
  21. VASU, V.; Fox, N.; Heneghan, C.; Sezer, S.; , “Using the Lomb periodogram for non-contact estimation of respiration rates,” Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE , 2010,vol.1, no.1, pp.2407-2410, Aug. 31
  22. THON, T.; McNames, James; Aboy, M., “Lomb-Wech periodogram for non-uniform sampling,” Engineering in Medicine and Biology Society, 2004. IEMBS ‘04. 26th Annual International Conference of the IEEE , 2004,vol.1, no., pp.271,274, 1-5 .
  23. ZHIGUO Zhang; Shing-Chow Chan; Chong Wang, “A New Regularized Adaptive Windowed Lomb Periodogram for Time–Frequency Analysis of Nonstationary Signals With Impulsive Components,” Instrumentation and Measurement, IEEE Transactions on ,2012, vol.61, no.8, pp.2283,2304, Aug.
  24. ZHANG, Z.G.; Chan, S.C., «Harmonic analysis of power system signals using a new regularized adaptive windowed Lomb periodogram,” Green Circuits and Systems (ICGCS), 2010 International Conference on ,2010, vol., no., pp.567,572, 21-23.
  25. SHAO-YEN Tseng; Wai-Chi Fang, “An effective heart rate variability processor design based on timefrequency analysis algorithm using windowed Lomb periodogram,” Biomedical Circuits and Systems Conference (BioCAS), 2010 IEEE ,2010, vol.1, no.1, pp.82,85, 3-5.
  26. BANSAL, D.; Khan, M.; Salhan, A.K., «A Review of Measurement and Analysis of Heart Rate Variability,» Computer and Automation Engineering, 2009. ICCAE ‹09. International Conference on ,2009 vol.1, no.1, pp.243,246, 8-10.
  27. Physionet. in: http://www.physionet.org/. Consultada en marzo 29 de 2014.
  28. GOLDMITH RL, Bigger JT, Steinman RC, et al. Comparison of 24-hour parasympathetic activity in endurance-trained and untrained young men. J Am Coll Cardiol 1992; 20. pp.552-558.
  29. BIGGER JT, Fleiss LF, Steinman RC, Rolnitzky LM, Schneider WJ, Stein PK. RR variability in healthy, middle-age persons compared with patients with chronic coronary heart disease or recent acute myocardial infarction. Circulation 1995; 91. pp.1936-1943.
  30. STEIN PK, Ehsani AA, Domitrovich PP, Kleiger RE, Rottman JN. The effect of exercise training on heart rate variability in healthy older adults. Am Heart J 1999; pp.138:567-576.
  31. MIETUS JE, Peng C-K, Henry I, Goldsmith RL, Goldberger AL. The pNNx files: re-examining a widely used heart rate variability measure. Heart 2002; 88: pp.378-380.