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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

Estimation of blood pressure parameters using ex-Gaussian model

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DOI: http://dx.doi.org/10.15439/2016F525

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 1427–1430 ()

Full text

Abstract. The paper presents example of model-based estimation of blood pressure parameters (onset, systolic and diastolic pressure) from continuous measurements. First, the signal was low pass filtered and its quality was estimated. Good quality periods were divided into beats using electrocardiogram. Next, beginning of the each beat of the blood pressure signal, was approximated basing on the function created from sum of two independent distributions: Gaussian and exponential. The nonlinear least square method was used to fit measurement data to the model. The initial conditions for the fitting procedure were selected for each beat basing on its parameters. Finally, the diastolic and systolic values of blood pressure and onset were determined.

References

  1. W. Zong, G. B. Moody, and R. G. Mark, “Reduction of false arterial blood pressure alarms using signal quality assessment and relationships between the electrocardiogram and arterial blood pressure”, Med. Biol. Eng. & Comput., vol. 42, pp. 698–706 September 2004. http://dx.doi.org/10.1007/BF02347553
  2. J. X. Sun, A. T. Reisner, R. G. Mark, “A signal abnormality index for arterial blood pressure waveforms”, Computers in Cardiology, vol. 33, pp. 13–16, 2006.
  3. W. Zong, T. Heldt, G. B. Moody, R. G. Mark, “An Open-source Algorithm to Detect Onset of Arterial Blood Pressure Pulses”, Computers in Cardiology, vol. 30, pp. 259–262, 2003. http://dx.doi.org/10.1109/CIC.2003.1291140
  4. M. Aboy, J. McNames, T. Thong, D. Tsunami, M. S. Ellenby, and B. Goldstein, “An Automatic Beat Detection Algorithm for Pressure Signals”, Trans. on Biomed. Eng. , vol. 52, no. 10, pp. 1662–1670 October 2005. http://dx.doi.org/10.1109/TBME.2005.855725
  5. B. N. Li, M. C. Dong, M. I. Vai, “On an automatic delineator for arterial blood pressure waveforms”, Biomedical Signal Processing and Control, vol. 5, pp. 76–81, 2010. http://dx.doi.org/10.1016/j.bspc.2009.06.002
  6. A. Pachauri and M. Bhuyan, “Wavelet Transform Based Arterial Blood Pressure Waveform Delineator”, International Journal of Biology and Biomedical Engineering, Issue 1, vol. 6, pp. 15–25, 2012.
  7. P. Xu, M. Bergsneider, X. Hu, “Pulse onset detection using neighbor pulse-based signal enhancement”, Med. Eng. & Phys., vol. 31, pp. 337–345, 2009. http://dx.doi.org/10.1016/j.medengphy.2008.06.005
  8. L. Yanga, M. Zhaoa, C. Penga, X. Hub, H. Fengc, Z. Ji, “Waveform descriptor for pulse onset detection of intracranial pressure signal”, Med. Eng. & Phys., vol. 34, pp. 179–186, 2012. http://dx.doi.org/10.1016/j.medengphy.2011.07.008
  9. E. Kazanavicius, R. Gircys, A. Vrubliauskas, S. Lugin, “Mathematical methods for determining the foot point of the arterial pulse wave and evaluation of proposed methods”, Information Technology and Control, vol. 34, no. 1, pp. 29–36, 2005.
  10. A. Fanelli, T. Heldt, “Signal quality quantification and waveform reconstruction of arterial blood pressure recordings”, in Conf. Proc. IEEE Eng. Med. Biol. Soc. 2014, pp. 2233–6. http://dx.doi.org/10.1109/EMBC.2014.6944063
  11. P. Augustyniak, “Recovering The Precise Heart Rate From Sparsely Sampled Electrocardiograms”, in Computers in Medicine, Łódź 23-25.09.1999, pp. 59-65.
  12. K. Lan, J. W. Jorgenson, “A hybrid of exponential and gaussian functions as a simple model of asymmetric chromatographic peaks”, Journal of Chromatography A, vol. 915, pp. 1–13, 2001. http://dx.doi.org/10.1016/S0021-9673(01)00594-5
  13. A. Golubev, “Exponentially modified Gaussian (EMG) relevance to distributions related to cell proliferation and differentiation”, Journal of Theoretical Biology, vol. 262, pp. 257–266, 2010. http://dx.doi.org/10.1016/j.jtbi.2009.10.005
  14. A. Poliński, J. Kot, A. Meresta, “Analysis of correlation between heart rate and blood pressure”, Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 417–420, 2011.
  15. M. Kaczmarek, A. Bujnowski, J. Wtorek, A. Poliński, “Multimodal Platform for Continuous Monitoring of the Elderly and Disabled”, J. Med. Imaging Health Inform., vol. 2, no. 1, pp. 56–63, March 2012. http://dx.doi.org/http://dx.doi.org/10.1166/jmihi.2012.1061
  16. J. Wtorek, A. Bujnowski, J. Rumiński, A. Poliński, M. Kaczmarek, A. Nowakowski, “Assessment of Cardiovascular Risk in Assisted Living”, Metrol. Meas. Syst. vol. 19, issue 2, pp. 231–244, May 2012. http://dx.doi.org/10.2478/v10178-012-0020-0 10.2478/v10178-012-0020-0
  17. A. Bujnowski, J. Rumiński, P. Przystup, K. Czuszyński,T. Kocejko, “Self Diagnostics Using Smart Glasses - preliminary study”, 9th International Conference on Human System Interaction (HSI2016), pp. 511–517, http://dx.doi.org/10.1109/HSI.2016.7529682
  18. A. Bujnowski, J. Rumiński, M. Kaczmarek, K. Czuszyński, P. Przystup, “Cardiovascular data analysis using electronic wearable eyeglasses - preliminary study”, Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 1449–1452, 2016.
  19. J. Pan and W. J. Tompkins, “A Real-Time QRS Detection AlgorithmA Real-Time QRS Detection Algorithm” IEEE Trans. on Biomed. Eng. , vol. 32, no. 3, pp. 230–236 March 1985. http://dx.doi.org/10.1109/TBME.1985.325532
  20. A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. Ch. 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 101(23):e215-e220 [Circulation Electronic Pages; http://circ.ahajournals.org/cgi/content/full/101/23/e215]; 2000 (June 13).