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Annals of Biomedical Engineering

, Volume 38, Issue 3, pp 1048–1059 | Cite as

Baseline Characteristics of Dual-Axis Cervical Accelerometry Signals

  • Ervin Sejdić
  • Vicki Komisar
  • Catriona M. Steele
  • Tom Chau
Article

Abstract

Dual-axis swallowing accelerometry is a promising noninvasive tool for the assessment of difficulties during deglutition. The resting and anaerobic characteristics of these signals, however, are still unknown. This paper presents a study of baseline characteristics (stationarity, spectral features, and information content) of dual-axis cervical vibrations. In addition, modeling of a data acquisition system was performed to annul any undesired instrumentation effects. Two independent data collection procedures were conducted to fulfil the goals of the study. For baseline characterization, data were acquired from 50 healthy adult subjects. To model the data acquisition (DAQ) system, ten recordings were obtained while the system was exposed to random small vibrations. The inverse filtering approach removed extraneous effects introduced by the DAQ system. Approximately half of the filtered signals were stationary in nature. All signals exhibited a level of statistical dependence between the two axes. Also, there were very low frequency oscillations present in these signals that may be attributable to vasomotion of blood vessels near the thyroid cartilage, blood flow, and respiration. Demographic variables such as age and gender did not statistically influence baseline information-theoretic signal characteristics. However, participant age did affect the baseline spectral characteristics. These findings are important to the further development of diagnostic devices based on dual-axis swallowing vibration signals.

Keywords

Dual-axis swallowing accelerometry signals Baseline Auto-regressive modeling Stationarity Information-theoretic analysis 

Notes

Acknowledgments

This research was funded in part by the Ontario Centres of Excellence, the Toronto Rehabilitation Institute, Bloorview Kids Rehab, and the Canada Research Chairs Program.

References

  1. 1.
    Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Contr. 19(6):716–723, 1974.CrossRefGoogle Scholar
  2. 2.
    Alves, N., and T. Chau, Stationarity distributions of mechanomyogram signals from isometric contractions of extrinsic hand muscles during functional grasping. J. Electromyogr. Kinesiol. 18(3):509–515, 2008.CrossRefPubMedGoogle Scholar
  3. 3.
    Barron, A., J. Rissanen, and B. Yu, The minimum description length principle in coding and modeling. IEEE Trans. Inf. Theory, 44(6):2743–2760, 1998.CrossRefGoogle Scholar
  4. 4.
    Bendat, J. S., and A. G. Piersol. Random Data: Analysis and Measurement Procedures, 2nd edn. New York, NY: Wiley, 1986.Google Scholar
  5. 5.
    Brockwell, P. J., and R. A. Davis., Time Series: Theory and Methods, 2nd ed. New York, NY: Springer-Verlag, 1991.CrossRefGoogle Scholar
  6. 6.
    Cao, H., B. R. Ellis, and J. D. Littler, The use of the maximum entropy method for the spectral analysis of wind-induced data recorded on buildings. J. Wind Eng. Industr. Aerodyn. 72:81–93, 1997.CrossRefGoogle Scholar
  7. 7.
    Chau, T., D. Chau, M. Casas, G. Berall, and D. J. Kenny, Investigating the stationarity of paediatric aspiration signals. IEEE Trans. Neural Syst. Rehabil. Eng. 13(1):99–105, 2005.CrossRefPubMedGoogle Scholar
  8. 8.
    Cichero, J. A. Y. and B. E. Murdoch. Acoustic signature of the normal swallow: characterization by age, gender, and bolus volume. Ann. Otol. Rhinol. Laryngol. 111(7 Pt 1):623–632, 2002.PubMedGoogle Scholar
  9. 9.
    Clancy, E. A., and N. Hogan. Single site electromyograph amplitude estimation. IEEE Trans. Biomed. Eng. 41(2):159–167, 1994.CrossRefPubMedGoogle Scholar
  10. 10.
    Colantuoni, A., S. Bertuglia, and M. Intaglietta. Quantitation of rhythmic diameter changes in arterial microcirculation. Am. J. Physiol. Heart Circ. Physiol. 246(4):508–517, 1984.Google Scholar
  11. 11.
    Cover, T. M., and J. A. Thomas. Elements of Information Theory, Wiley Series in Telecommunications. New York, NY: Wiley, 1991.Google Scholar
  12. 12.
    Das, A., N. P. Reddy, and J. Narayanan, Hybrid fuzzy logic committee neural networks for recognition of swallow acceleration signals. Comput. Meth. Progr. Biomed. 64(2):87–99, 2001.CrossRefGoogle Scholar
  13. 13.
    Donoho, D. L. De-noising by soft-thresholding. IEEE Trans. Inf. Theory, 41(3):613–627, 1995.CrossRefGoogle Scholar
  14. 14.
    Donoho, D. L., and I. M. Johnstone. Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3):425–455, 1994.CrossRefGoogle Scholar
  15. 15.
    Ishida, R., J. B. Palmer, and K. M. Hiiemae, Hyoid motion during swallowing: factors affecting forward and upward displacement. Dysphagia, 17(4):262–272, 2002.CrossRefPubMedGoogle Scholar
  16. 16.
    Kay, S. M. Modern Spectral Estimation: Theory and Application. Englewood Cliffs, NJ: Prentice Hall, 1988.Google Scholar
  17. 17.
    Kay, S. M., and S. L. Marple, Spectrum analysis—a modern perspective. Proc. IEEE 69(11): 1380–1419, 1981.CrossRefGoogle Scholar
  18. 18.
    Kim, Y., and G. H. McCullough, Maximum hyoid displacement in normal swallowing. Dysphagia 23(3):274–279, 2008.CrossRefPubMedGoogle Scholar
  19. 19.
    Lee, J., S. Blain, M. Casas, D. Kenny, G. Berall, and T. Chau. A radial basis classifier for the automatic detection of aspiration in children with dysphagia. J. NeuroEng. Rehabil. 3:14, 2006. doi: 10.1186/1743-0003-3-14.Google Scholar
  20. 20.
    Lee, J., C. M. Steele, and T. Chau, Time and time-frequency characterization of dual-axis swallowing accelerometry signals. Physiol. Measure 29(9):1105–1120, 2008.CrossRefGoogle Scholar
  21. 21.
    Lees, R. S. Phonoangiography: qualitative and quantitative. Ann. Biomed. Eng. 12(1):55–62, 1984.CrossRefPubMedGoogle Scholar
  22. 22.
    Li, S. Z. Content-based classification and retrieval of audio using the nearest feature line method. IEEE Trans. Speech Audio Process. 8(5):619–625, 2000.CrossRefGoogle Scholar
  23. 23.
    Lilliefors, H. W. On the Kolmogorov–Smirnov test for normality with mean and variance unknown. J. Am. Stat. Assoc. 62(318):399–402, 1967.CrossRefGoogle Scholar
  24. 24.
    Ljung, L. System Identification: Theory for the User, 2nd edn. Upper Saddle River, NJ: Prentice-Hall, 1999.Google Scholar
  25. 25.
    Logemann, J. A. Evaluation and Treatment of Swallowing Disorders, 2nd ed. Austin, TX: PRO-ED, 1998.Google Scholar
  26. 26.
    Mann, H. B., and D. R. Whitney, On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18(1):50–60, 1947.CrossRefGoogle Scholar
  27. 27.
    Marple, L. A new autoregressive spectrum analysis algorithm. IEEE Trans. Acoust. 28(4):441–454, 1980.CrossRefGoogle Scholar
  28. 28.
    Marple, S. L. Digital Spectral Analysis: With Applications. Englewood Cliffs, NJ: Prentice-Hall, Inc., 1987.Google Scholar
  29. 29.
    McConaghy, T., H. Leung, E. Bossé, and V. Varadan, “Classification of audio radar signals using radial basis function neural networks,” IEEE Trans. Instrum. Measure. 52(6):1771–1779, 2003.CrossRefGoogle Scholar
  30. 30.
    Merletti, R., A. Gulisashvili, and L. R. Lo Conte. Estimation of shape characteristics of surface muscle signal spectra from time domain data. IEEE Trans. Biomed. Eng. 42(8):769–776, 1995.CrossRefPubMedGoogle Scholar
  31. 31.
    O’Brien, I. A., P. O’hare, and R. J. Corrall, Heart rate variability in healthy subjects: effect of age and the derivation of normal ranges for tests of autonomic function. Brit. Heart J. 55(4):348–354, 1986.CrossRefPubMedGoogle Scholar
  32. 32.
    Paiss, O., and G. F. Inbar, Autoregressive modeling of surface EMG and its spectrum with application to fatigue. IEEE Trans. Biomed. Eng. 34(10):761–770, 1987.CrossRefGoogle Scholar
  33. 33.
    Papoulis, A. Probability, Random Variables, and Stochastic Processes, 3rd edn. New York: WCB/McGraw-Hill, 1991.Google Scholar
  34. 34.
    Porta, A., G. Baselli, D. Liberati, N. Montano, C. Cogliati, T. Gnecchi-Ruscone, A. Malliani, and S. Cerutti, Measuring regularity by means of a corrected conditional entropy in sympathetic outflow. Biol. Cybernet. 78(1):71–78, 1998.CrossRefGoogle Scholar
  35. 35.
    Porta, A., G. Baselli, F. Lombardi, N. Montano, A. Malliani, and S. Cerutti, Conditional entropy approach for the evaluation of the coupling strength. Biol. Cybernet. 81(2):119–129, 1999.CrossRefGoogle Scholar
  36. 36.
    Porta, A., S. Guzzetti, N. Montano, R. Furlan, M. Pagani, A. Malliani, and S. Cerutti, Entropy, entropy rate, and pattern classification as tools to typify complexity in short heart period variability series. IEEE Trans. Biomed. Eng. 48(11):1282–1291, 2001.CrossRefPubMedGoogle Scholar
  37. 37.
    Porta, A., S. Guzzetti, N. Montano, M. Pagani, V. Somers, A. Malliani, G. Baselli, and S. Cerutti, Information domain analysis of cardiovascular variability signals: Evaluation of regularity, synchronisation and co-ordination. Med. Biol. Eng. Comput. 38(2):180–188, 2000.CrossRefPubMedGoogle Scholar
  38. 38.
    Porta, A., E. Tobaldini, S. Guzzetti, R. Furlan, N. Montano, and T. Gnecchi-Ruscone. Assessment of cardiac autonomic modulation during graded head-up tilt by symbolic analysis of heart rate variability. Am. J. Physiol. Heart Circ. Physiol. 293(1):H702–H708, 2007.CrossRefPubMedGoogle Scholar
  39. 39.
    Ramsey, D. J. C., D. G. Smithard, and L. Kalra, Can pulse oximetry or a bedside swallowing assessment be used to detect aspiration after stroke? Stroke, 37(12): 2984–2988, 2006.CrossRefPubMedGoogle Scholar
  40. 40.
    Reddy, N. P., E. P. Canilang, J. Casterline, M. B. Rane, A. M. Joshi, R. Thomas, and R. Candadai, Noninvasive accelaration measurements to characterize the pharyngeal phase of swallowing. J. Biomed. Eng. 13:379–383, 1991.CrossRefPubMedGoogle Scholar
  41. 41.
    Reddy, N. P., B. R. Costarella, R. C. Grotz, and E. P. Canilang, Biomechanical measurements to characterize the oral phase of dysphagia. IEEE Trans. Biomed. Eng. 37(4):392–397, 1990.CrossRefPubMedGoogle Scholar
  42. 42.
    Reddy, N. P., A. Katakam, V. Gupta, R. Unnikrishnan, J. Narayanan, and E. P. Canilang, Measurements of acceleration during videofluorographic evaluation of dysphagic patients. Med. Eng. Phys. 22(6):405–412, 2000.CrossRefPubMedGoogle Scholar
  43. 43.
    Rissanen, J. Modeling by shortest data description. Automatica, 14(5):465–471, 1978.CrossRefGoogle Scholar
  44. 44.
    Schmidt-Lucke, C., P. Borgström, and J. A. Schmidt-Lucke. Low frequency flowmotion/(vasomotion) during patho-physiological conditions. Life Sci. 71(23): 2713–2728, 2002.CrossRefPubMedGoogle Scholar
  45. 45.
    Schwarz, G. Estimating the dimension of a model. Ann. Stat. 6(2):461–464, 1978.CrossRefGoogle Scholar
  46. 46.
    Sejdić, E., C. M. Steele, and T. Chau, Segmentation of dual-axis swallowing accelerometry signals in healthy subjects with analysis of anthropometric effects on duration of swallowing activities. IEEE Trans. Biomed. Eng. 56(4):1090–1097, 2009.CrossRefPubMedGoogle Scholar
  47. 47.
    Steele, C., C. Allen, J. Barker, P. Buen, R. French, A. Fedorak, S. Day, J. Lapointe, L. Lewis, C. MacKnight, S. McNeil, J. Valentine, and L. Walsh, Dysphagia service delivery by speech-language pathologists in Canada: results of a national survey. Can. J. Speech-Language Pathol. Audiol. 31(4):166–177, 2007.Google Scholar
  48. 48.
    Stoica, P., and Y. Selén, Model-order selection: a review of information criterion rules. IEEE Signal Process. Mag. 21(4):36–47, 2004.CrossRefGoogle Scholar
  49. 49.
    Tracy, J. F., J. A. Logemann, P. J. Kahrilas, P. Jacob, M. Kobara, and C. Krugler. Preliminary observations on the effects of age on oropharyngeal deglutition. Dysphagia 4(2):90–94, 1989.CrossRefPubMedGoogle Scholar
  50. 50.
    Wang, P., Y. Kim, L. H. Ling, and C. B. Soh, First heart sound detection for phonocardiogram segmentation. In: Proc. of 27th Annual International Conference of the Engineering in Medicine and Biology Society (IEEE-EMBS 2005), Shanghai, China, Sept. 1–5, 2005, pp. 5519–5522.Google Scholar
  51. 51.
    Yang, Y. Can the strengths of AIC and BIC be shared? A conflict between model indentification and regression estimation. Biometrika 92(4):937–950, 2005.CrossRefGoogle Scholar

Copyright information

© Biomedical Engineering Society 2010

Authors and Affiliations

  • Ervin Sejdić
    • 1
  • Vicki Komisar
    • 2
  • Catriona M. Steele
    • 3
  • Tom Chau
    • 1
  1. 1.Bloorview Research Institute, Bloorview Kids Rehab and the Institute of Biomaterials and Biomedical EngineeringUniversity of TorontoTorontoCanada
  2. 2.Department of Engineering ScienceUniversity of TorontoTorontoCanada
  3. 3.Toronto Rehabilitation Institute and the Department of Speech-Language PathologyUniversity of TorontoTorontoCanada

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