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.
Similar content being viewed by others
References
Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Control AC-19:716–723, 1974.
Akay, M. Wavelet applications in medicine. IEEE Spectr. 34(5):50–56, 1997.
Allen, J. Photoplethysmography and its application in clinical physiological measurement. Physiol. Meas. 28(3):R1–R39, 2007.
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.
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.
Alvarez, W. C. The electrogastrogram and what it shows. J. Am. Med. Assoc. 78:116–119, 1922.
Amara Grap. An introduction to wavelets. IEEE Comput. Sci. Eng. 2:2, 1995.
Bendat, J. S., and A. G. Piersol. Engineering Applications of Correlation and Spectral Analysis (2nd ed.). New York: Wiley, 1993.
Bendat, J., and A. Piersol. Random Data: Analysis and Measurement Procedures (3rd ed.). New York: Wiley, 2000.
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.
Brillinger, D. Time Series: Data Analysis and Theory. New York: Holt, Rinehart and Winston, 1975.
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.
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.
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.
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.
Daubechies, I. The wavelet transform time-frequency localization and signal analysis. IEEE Trans. Inf. Theory 36(5):961–1005, 1990.
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.
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.
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.
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.
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.
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.
Guyton, A. C., and E. H. John. Textbook of Medical Physiology (11th ed.). Philadelphia: Elsevier/Saunders, 2006.
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.
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.
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.
Hertzman, A. B., and C. R. Spielman. Observations on the finger volume pulse recorded photoelectrically. Am. J. Physiol. 119:334–335, 1937.
Hyndman, B. W., R. I. Kitney, and B. Sayers. Spontaneous rhythms in physiological control systems. Nature 233:339–341, 1971.
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.
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.
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.
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.
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.
Kay, S. Modern Spectral Estimation. Englewood Cliffs, NJ: Prentice Hall, 1988.
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.
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.
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.
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.
Liang, F., and H. Liu. A closed-loop lumped parameter computational model for human cardiovascular system. JSME Int. J. C 48:4, 2005.
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.
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.
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.
Marple, S. L. Digital Spectral Analysis with Applications. Englewood Cliffs: Prentice-Hall, 1987.
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.
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.
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.
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.
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.
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.
Proakis, J. G., and D. G. Manolakis. Digital Signal Processing: Principles Algorithms and Applications (3rd ed.). India: Prentice-Hall, 1997.
Roberts, V. C. Photoplethysmography—fundamental aspects of the optical properties of blood in motion. Trans. Instrum. Meas. Control 4:101–106, 1982.
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.
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.
Semmlow, J. L. Biosignal and Medical Image Processing (2nd ed.). Boca Rotan: CRC Press, 2009.
Smout, A. J., E. J. Van der Schee, and J. L. Grashuis. What is measured in electrogastrography? Dig. Dis. Sci. 25:179–187, 1980.
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.
Texter, E. C. Small intestinal blood flow. Dig. Dis. Sci. 8(7):587–613, 1963.
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.
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.
Unser, M., and A. Aldroubi. A review of wavelets in biomedical applications. Proc. IEEE 84(4):626–638, 1996.
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.
Acknowledgments
We thank Applied Mechanics Department of Indian Institute of Technology Madras and Government of India for funding this work. We sincerely acknowledge all the volunteers who have participated in this study by sparing their valuable time and effort to make it successful. Authors would like to thank the unknown and anonymous reviewers for their invaluable comments to improve the standard of article.
Author information
Authors and Affiliations
Corresponding author
Additional information
Associate Editor Leonidas D. Iasemidis oversaw the review of this article.
Rights and permissions
About this article
Cite this article
Mohamed Yacin, S., Manivannan, M. & Srinivasa Chakravarthy, V. On Non-Invasive Measurement of Gastric Motility from Finger Photoplethysmographic Signal. Ann Biomed Eng 38, 3744–3755 (2010). https://doi.org/10.1007/s10439-010-0113-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10439-010-0113-4