Annals of Biomedical Engineering

, Volume 38, Issue 12, pp 3744–3755 | Cite as

On Non-Invasive Measurement of Gastric Motility from Finger Photoplethysmographic Signal

  • S. Mohamed Yacin
  • M. Manivannan
  • V. Srinivasa Chakravarthy
Article

Abstract

This article investigates the possibility of extracting gastric motility (GM) information from finger photoplethysmographic (PPG) signals non-invasively. Now-a-days measuring GM is a challenging task because of invasive and complicated clinical procedures involved. It is well-known that the PPG signal acquired from finger consists of information related to heart rate and respiratory rate. This thread is taken further and effort has been put here to find whether it is possible to extract GM information from finger PPG in an easier way and without discomfort to the patients. Finger PPG and GM (measured using Electrogastrogram, EGG) signals were acquired simultaneously at the rate of 100 Hz from eight healthy subjects for 30 min duration in fasting and postprandial states. In this study, we process the finger PPG signal and extract a slow wave that is analogous to actual EGG signal. To this end, we chose two advanced signal processing approaches: first, we perform discrete wavelet transform (DWT) to separate the different components, since PPG and EGG signals are non-stationary in nature. Second, in the frequency domain, we perform cross-spectral and coherence analysis using autoregressive (AR) spectral estimation method in order to compare the spectral details of recorded PPG and EGG signals. In DWT, a lower frequency oscillation (≈0.05 Hz) called slow wave was extracted from PPG signal which looks similar to the slow wave of GM in both shape and frequency in the range (0–0.1953) Hz. Comparison of these two slow wave signals was done by normalized cross-correlation technique. Cross-correlation values are found to be high (range 0.68–0.82, SD 0.12, R = 1.0 indicates exact agreement, p < 0.05) for all subjects and there is no significant difference in cross-correlation between fasting and postprandial states. The coherence analysis results demonstrate that a moderate coherence (range 0.5–0.7, SD 0.13, p < 0.05) exists between EGG and PPG signal in the “slow wave” frequency band, without any significant change in the level of coherence in postprandial state. These results indicate that finger PPG signal contains GM-related information. The findings are sufficiently encouraging to motivate further exploration of finger PPG as a non-invasive source of GM-related information.

Keywords

AR spectral estimation Cross-correlation Discrete wavelet transform Electrogastrography Enteric nervous system Gastric myoelectric activity Magnitude squared coherence Slow wave 

References

  1. 1.
    Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Control AC-19:716–723, 1974.CrossRefGoogle Scholar
  2. 2.
    Akay, M. Wavelet applications in medicine. IEEE Spectr. 34(5):50–56, 1997.CrossRefGoogle Scholar
  3. 3.
    Allen, J. Photoplethysmography and its application in clinical physiological measurement. Physiol. Meas. 28(3):R1–R39, 2007.CrossRefPubMedGoogle Scholar
  4. 4.
    Allen, J., and A. Murray. Similarity in bilateral photoplethysmographic peripheral pulse wave characteristics at the ears, thumbs and toes. Physiol. Meas. 21:369–377, 2000.CrossRefPubMedGoogle Scholar
  5. 5.
    Alos, R., E. Garcia-Granero, J. Calvete, and N. Uribe. The use of photoplethysmography to predict anastomotic viability after segmental intestinal ischemia in dogs. Eur. J. Surg. 159:35–41, 1993.PubMedGoogle Scholar
  6. 6.
    Alvarez, W. C. The electrogastrogram and what it shows. J. Am. Med. Assoc. 78:116–119, 1922.Google Scholar
  7. 7.
    Amara Grap. An introduction to wavelets. IEEE Comput. Sci. Eng. 2:2, 1995.Google Scholar
  8. 8.
    Bendat, J. S., and A. G. Piersol. Engineering Applications of Correlation and Spectral Analysis (2nd ed.). New York: Wiley, 1993.Google Scholar
  9. 9.
    Bendat, J., and A. Piersol. Random Data: Analysis and Measurement Procedures (3rd ed.). New York: Wiley, 2000.Google Scholar
  10. 10.
    Brij, N. S., and K. T. Arvind. Optimal selection of wavelet basis function applied to ECG signal denoising. Digit. Signal Process. 16:275–287, 2006.CrossRefGoogle Scholar
  11. 11.
    Brillinger, D. Time Series: Data Analysis and Theory. New York: Holt, Rinehart and Winston, 1975.Google Scholar
  12. 12.
    Cazares, S., M. Moulden, C. W. G. Redman, and L. Tarassenko. Tracking poles with an autoregressive model: a confidence index for the analysis of the intrapartum cardiotocogram. Med. Eng. Phys. 23:603–614, 2001.CrossRefPubMedGoogle Scholar
  13. 13.
    Challoner, A. V. J. Photoelectric plethysmography for estimating cutaneous blood flow. In: Non-Invasive Physiological Measurements, Vol. 1, edited by P. Rolfe. London: Academic Press, 1979, pp. 125–151.Google Scholar
  14. 14.
    Chen, J., and R. W. McCallum. Response of the electric activity in the human stomach to water and a solid meal. Med. Biol. Eng. Comput. 29:351–357, 1991.CrossRefPubMedGoogle Scholar
  15. 15.
    Chen, J. D., W. R. Stewart, and R. W. McCallum. Spectral analysis of episodic rhythmic variations in the cutaneous electrogastrogram. IEEE Trans. Biomed. Eng. 40(2):128–135, 1993.CrossRefPubMedGoogle Scholar
  16. 16.
    Daubechies, I. The wavelet transform time-frequency localization and signal analysis. IEEE Trans. Inf. Theory 36(5):961–1005, 1990.CrossRefGoogle Scholar
  17. 17.
    Dean, C., E. D. Übeyli, and I. Cosic. Wavelet transform feature extraction from human PPG, ECG, and EEG signal responses to ELF PEMF exposures: a pilot study. Digit. Signal Process. 18:861–874, 2008.CrossRefGoogle Scholar
  18. 18.
    Dirgenali, F., S. Kara, and S. Okkesim. Estimation of wavelet and short-time Fourier transform sonograms of normal and diabetic subjects’ electrogastrogram. Comput. Biol. Med. 36:1289–1302, 2006.CrossRefPubMedGoogle Scholar
  19. 19.
    Fleming, S. G., and L. Tarassenko. A comparison of signal processing techniques for the extraction of breathing rate from the photoplethysmogram. Int. J. Biol. Med. Sci. 2(4):232–236, 2007.Google Scholar
  20. 20.
    Fujimura, J., M. Camilleri, P. A. Low, V. Novak, P. Novak, and T. L. Opfer-Gehrking. Effect of perturbations and a meal on superior mesenteric artery flow in patients with orthostatic hypotension. J. Auton. Nerv. Syst. 67:15–23, 1997.CrossRefPubMedGoogle Scholar
  21. 21.
    Garcia-Granero, E., S. A. Garcia, R. Alos, J. Calvete, B. Flor-Lorente, J. Willatt, and S. Lledo. Use of PPG to determine gastrointestinal perfusion pressure: an experimental Canine model. Dig. Surg. 20:222–228, 2003.CrossRefPubMedGoogle Scholar
  22. 22.
    Girault, J. M., D. Kouame, A. Ouahabi, and F. Patat. Microemboli detection: an ultrasound Doppler signal processing viewpoint. IEEE Trans. Biomed. Eng. 47:1431–1439, 2000.CrossRefPubMedGoogle Scholar
  23. 23.
    Guyton, A. C., and E. H. John. Textbook of Medical Physiology (11th ed.). Philadelphia: Elsevier/Saunders, 2006.Google Scholar
  24. 24.
    Haghighi-Mood, A., and J. N. Tony. The-varying filtering of the first and second heart sounds. In: 18th Annual International Conference Proceedings of the IEEE EMBS Conference, Amsterdam, pp. 950–9516, 1996.Google Scholar
  25. 25.
    Haghighi-Mood, A., and J. N. Tony. Coherence analysis of multichannel heart sound recording. In: IEEE Transactions on Computers in Cardiology, pp. 377–380, 1996.Google Scholar
  26. 26.
    Halliday, D. M., J. R. Rosenberg, A. M. Amjad, P. Breeze, B. A. Conway, and S. F. Farmer. A framework for the analysis of mixed time series/point process data: theory and application to the study of physiological tremor, single motor unit discharges and electromyograms. Prog. Biophys. Mol. Biol. 64:237–278, 1995.CrossRefPubMedGoogle Scholar
  27. 27.
    Hertzman, A. B., and C. R. Spielman. Observations on the finger volume pulse recorded photoelectrically. Am. J. Physiol. 119:334–335, 1937.Google Scholar
  28. 28.
    Hyndman, B. W., R. I. Kitney, and B. Sayers. Spontaneous rhythms in physiological control systems. Nature 233:339–341, 1971.CrossRefPubMedGoogle Scholar
  29. 29.
    Johansson, A., and P. A. Oberg. Estimation of respiratory volumes from the photoplethysmographic signal. Part 1: experimental results. Med. Biol. Eng. Comput. 37:42–47, 1999.CrossRefPubMedGoogle Scholar
  30. 30.
    Johansson, A., and P. A. Oberg. Estimation of respiratory volumes from the photoplethysmographic signal. Part 2: a model study. Med. Biol. Eng. Comput. 37:48–53, 1999.CrossRefPubMedGoogle Scholar
  31. 31.
    Jönsson, B., C. Laurent, M. Vegfors, and L. G. Lindberg. A new probe for ankle systolic pressure measurement using photoplethysmography. Ann. Biomed. Eng. 33:232–239, 2005.CrossRefPubMedGoogle Scholar
  32. 32.
    Kamal, A. A. R., J. B. Hatness, G. Irving, and A. J. Means. Skin photoplethysmography—a review. Comput. Methods Programs Biomed 28:257–269, 1989.CrossRefPubMedGoogle Scholar
  33. 33.
    Kara, S., F. Dirgenali, and S. Okkesim. Detection of gastric dysrhythmia using WT and ANN in diabetic gastroparesis patients. Comput. Biol. Med. 36:276–290, 2006.CrossRefPubMedGoogle Scholar
  34. 34.
    Kay, S. Modern Spectral Estimation. Englewood Cliffs, NJ: Prentice Hall, 1988.Google Scholar
  35. 35.
    Kraitl, J., H. Ewald, and H. Gehring. Analysis of time series for non-invasive characterization of blood components and circulation patterns. Nonlinear Anal. Hybrid Syst. 2:441–455, 2008.CrossRefGoogle Scholar
  36. 36.
    Kvandal, P., S. A. Landsverk, A. Bernjak, U. Benko, A. Stefanovska, H. D. Kvernmo, and K. A. Kirkebøen. Low frequency oscillations of the laser Doppler perfusion signal in human skin. Microvasc. Res. 72(3):120–127, 2006.CrossRefPubMedGoogle Scholar
  37. 37.
    Kyriacou, P. A., A. Crerar-Gilber, R. M. Langford, and D. P. Jones. Electro-optical techniques for the investigation of photoplethysmographic signals in human abdominal organs. J. Phys. Conf. Ser. 45:232–238, 2006.CrossRefGoogle Scholar
  38. 38.
    Lee. J., and K. H. Chon. Respiratory rate extraction via an autoregressive model using the optimal parameter search criterion. Ann. Biomed. Eng. (in press). doi: 10.1007/s10439-010-0080-9.
  39. 39.
    Liang, F., and H. Liu. A closed-loop lumped parameter computational model for human cardiovascular system. JSME Int. J. C 48:4, 2005.CrossRefGoogle Scholar
  40. 40.
    Lin, Z., and J. D. Chen. Time–frequency representation of the electrogastrogram—application of the exponential distribution. IEEE Trans. Biomed. Eng. 41:267–275, 1994.CrossRefPubMedGoogle Scholar
  41. 41.
    Lin, Y.-D., W.-T. Liu, C.-C. Tsai, and W.-H. Chen. Coherence analysis between respiration and PPG signal by bivariate AR model. Conf. Proc. World Acad. Sci. Eng. Technol. 53:847–852, 2009.Google Scholar
  42. 42.
    Linkens, D. A., and S. P. Datardina. Estimation of frequencies of gastrointestinal electrical rhythms using autoregressive modeling. Med. Biol. Eng. Comput. 16:262–268, 1978.CrossRefPubMedGoogle Scholar
  43. 43.
    Marple, S. L. Digital Spectral Analysis with Applications. Englewood Cliffs: Prentice-Hall, 1987.Google Scholar
  44. 44.
    Mizuno-Matsumoto, Y., S. Tamura, Y. Sato, R. A. Zoroofi, T. Yoshimine, A. Kato, M. Taniguchi, M. Takeda, T. Inouye, H. Tatsumi, S. Shimojo, and H. Miyahara. Propagating process of epileptiform discharges using wavelet-cross-correlation analysis in MEG. In: Recent Advances in Biomagnetism, edited by T. Yoshimoto. Sendai: Tohoku University Press, 1999, pp. 782–785.Google Scholar
  45. 45.
    Nawap, S. H., and T. F. Quatieri. Short time Fourier transform. In: Advanced Topics in Signal Processing, edited by J. S. Lim, and A. V. Oppenheim. Englewood Cliffs, NJ: Prentice-Hall, 1988, pp. 239–337.Google Scholar
  46. 46.
    Nilsson, L., A. Johansson, and S. Kalman. Monitoring of respiratory rate in postoperative care using a new photoplethysmographic technique. J. Clin. Monit. 16:309–315, 2000.CrossRefGoogle Scholar
  47. 47.
    Nitzan, M., S. Turivnenko, A. Milston, A. Babchenko, and Y. Mahler. Low-frequency variability in the blood volume pulse measured by photoplethysmography. J. Biomed. Opt. 1:223–229, 1996.CrossRefGoogle Scholar
  48. 48.
    Nitzan, M., A. Babchenko, B. Khanokh, and D. Landau. The variability of the photoplethysmographic signal: a potential method for the evaluation of the autonomic nervous system. Physiol. Meas. 19:93–102, 1998.CrossRefPubMedGoogle Scholar
  49. 49.
    Parkman, H. P. Electrogastrography: a document prepared by the gastric section of the American Motility Society Clinical Testing Task force. Neurogastroenterol. Motil. 15:89–102, 2003.CrossRefPubMedGoogle Scholar
  50. 50.
    Proakis, J. G., and D. G. Manolakis. Digital Signal Processing: Principles Algorithms and Applications (3rd ed.). India: Prentice-Hall, 1997.Google Scholar
  51. 51.
    Roberts, V. C. Photoplethysmography—fundamental aspects of the optical properties of blood in motion. Trans. Instrum. Meas. Control 4:101–106, 1982.CrossRefGoogle Scholar
  52. 52.
    Rossi, P., G. I. Andriesse, P. L. Oey, G. H. Wieneke, J. M. M. Roelofs, and L. M. A. Akkermans. Stomach distension increases efferent muscle sympathetic nerve activity and blood pressure in healthy humans. J. Neurol. Sci. 161:148–155, 1998.CrossRefPubMedGoogle Scholar
  53. 53.
    Scalassara, P. R., C. D. Maciel, R. C. Guido, J. C. Pereira, E. S. Fonseca, A. N. Montagnoli, S. Barbon, Jr., L. S. Vieira, and F. L. Sanchez. Autoregressive decomposition and pole tracking applied to vocal fold nodule signals. Pattern Recognit. Lett. 28:1360–1367, 2007.CrossRefGoogle Scholar
  54. 54.
    Semmlow, J. L. Biosignal and Medical Image Processing (2nd ed.). Boca Rotan: CRC Press, 2009.Google Scholar
  55. 55.
    Smout, A. J., E. J. Van der Schee, and J. L. Grashuis. What is measured in electrogastrography? Dig. Dis. Sci. 25:179–187, 1980.CrossRefPubMedGoogle Scholar
  56. 56.
    Stefanovska, A., M. Bračič, and H. D. Kvernmo. Wavelet analysis of oscillations in the peripheral blood circulation measured by laser Doppler technique. IEEE Trans. Biomed. Eng. 46(10):1230–1239, 1999.CrossRefPubMedGoogle Scholar
  57. 57.
    Texter, E. C. Small intestinal blood flow. Dig. Dis. Sci. 8(7):587–613, 1963.CrossRefGoogle Scholar
  58. 58.
    Thomas, K. A., M. Moosikasuwan, D. S. Samir, and S. D. Kedar. Length-normalized pulse photoplethysmography: a noninvasive method to measure blood hemoglobin. Ann. Biomed. Eng. 30:1291–1298, 2002.CrossRefGoogle Scholar
  59. 59.
    Übeyli, E. D., D. Cvetkovic, and I. Cosic. AR spectral analysis technique for human PPG, ECG and EEG signals. J. Med. Syst. 32(3):201–206, 2008.CrossRefPubMedGoogle Scholar
  60. 60.
    Unser, M., and A. Aldroubi. A review of wavelets in biomedical applications. Proc. IEEE 84(4):626–638, 1996.CrossRefGoogle Scholar
  61. 61.
    Wukitsch, M. W., M. T. Petterson, D. R. Tobler, and J. A. Pologe. Pulse oximetry: analysis of theory, technology and practice. J. Clin. Monit. 4:290–301, 1988.CrossRefPubMedGoogle Scholar

Copyright information

© Biomedical Engineering Society 2010

Authors and Affiliations

  • S. Mohamed Yacin
    • 1
  • M. Manivannan
    • 1
  • V. Srinivasa Chakravarthy
    • 2
  1. 1.Touch Lab, Department of Applied MechanicsIndian Institute of Technology MadrasChennaiIndia
  2. 2.Computational Neuroscience Lab, Department of BiotechnologyIndian Institute of Technology MadrasChennaiIndia

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