Skip to main content

Advertisement

Log in

Baseline Characteristics of Dual-Axis Cervical Accelerometry Signals

  • Published:
Annals of Biomedical Engineering Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

FIGURE 1
FIGURE 2
FIGURE 3
FIGURE 4

Similar content being viewed by others

References

  1. Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Contr. 19(6):716–723, 1974.

    Article  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  4. Bendat, J. S., and A. G. Piersol. Random Data: Analysis and Measurement Procedures, 2nd edn. New York, NY: Wiley, 1986.

    Google Scholar 

  5. Brockwell, P. J., and R. A. Davis., Time Series: Theory and Methods, 2nd ed. New York, NY: Springer-Verlag, 1991.

    Book  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    PubMed  Google Scholar 

  9. Clancy, E. A., and N. Hogan. Single site electromyograph amplitude estimation. IEEE Trans. Biomed. Eng. 41(2):159–167, 1994.

    Article  CAS  PubMed  Google Scholar 

  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. Cover, T. M., and J. A. Thomas. Elements of Information Theory, Wiley Series in Telecommunications. New York, NY: Wiley, 1991.

  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.

    Article  CAS  Google Scholar 

  13. Donoho, D. L. De-noising by soft-thresholding. IEEE Trans. Inf. Theory, 41(3):613–627, 1995.

    Article  Google Scholar 

  14. Donoho, D. L., and I. M. Johnstone. Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3):425–455, 1994.

    Article  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  16. Kay, S. M. Modern Spectral Estimation: Theory and Application. Englewood Cliffs, NJ: Prentice Hall, 1988.

    Google Scholar 

  17. Kay, S. M., and S. L. Marple, Spectrum analysis—a modern perspective. Proc. IEEE 69(11): 1380–1419, 1981.

    Article  Google Scholar 

  18. Kim, Y., and G. H. McCullough, Maximum hyoid displacement in normal swallowing. Dysphagia 23(3):274–279, 2008.

    Article  PubMed  Google Scholar 

  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. 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.

    Article  CAS  Google Scholar 

  21. Lees, R. S. Phonoangiography: qualitative and quantitative. Ann. Biomed. Eng. 12(1):55–62, 1984.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  24. Ljung, L. System Identification: Theory for the User, 2nd edn. Upper Saddle River, NJ: Prentice-Hall, 1999.

    Google Scholar 

  25. Logemann, J. A. Evaluation and Treatment of Swallowing Disorders, 2nd ed. Austin, TX: PRO-ED, 1998.

    Google Scholar 

  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.

    Article  Google Scholar 

  27. Marple, L. A new autoregressive spectrum analysis algorithm. IEEE Trans. Acoust. 28(4):441–454, 1980.

    Article  Google Scholar 

  28. Marple, S. L. Digital Spectral Analysis: With Applications. Englewood Cliffs, NJ: Prentice-Hall, Inc., 1987.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  33. Papoulis, A. Probability, Random Variables, and Stochastic Processes, 3rd edn. New York: WCB/McGraw-Hill, 1991.

    Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  43. Rissanen, J. Modeling by shortest data description. Automatica, 14(5):465–471, 1978.

    Article  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  45. Schwarz, G. Estimating the dimension of a model. Ann. Stat. 6(2):461–464, 1978.

    Article  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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. Stoica, P., and Y. Selén, Model-order selection: a review of information criterion rules. IEEE Signal Process. Mag. 21(4):36–47, 2004.

    Article  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  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.

  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.

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ervin Sejdić.

Additional information

Associate Editor Sean S. Kohles oversaw the review of this article.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sejdić, E., Komisar, V., Steele, C.M. et al. Baseline Characteristics of Dual-Axis Cervical Accelerometry Signals. Ann Biomed Eng 38, 1048–1059 (2010). https://doi.org/10.1007/s10439-009-9874-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10439-009-9874-z

Keywords

Navigation