Advertisement

Journal of Medical and Biological Engineering

, Volume 39, Issue 4, pp 490–497 | Cite as

Complexity-Based Analysis of the Difference Between Normal Subjects and Subjects with Stuttering in Speech Evoked Auditory Brainstem Response

  • Marjan MozaffarileghaEmail author
  • Hamidreza Namazi
  • Ali Akbar Tahaei
  • Sajad Jafari
Original Article

Abstract

Deficits in auditory processing are an assumed underlying mechanism in stuttering. Previous studies have demonstrated that speech evoked auditory brainstem response (s-ABR) is a reliable method to evaluate brainstem timing in clinical populations with persistent developmental stuttering (PDS). The examination of s-ABR signals to quantify differential complexities between PDS and normal subjects using linear analysis is unreliable. This prompted us to evaluate non-linear methods, which are more effective for conveying complex dynamics. The aim of the current study is to apply fractal dimension and the Hurst exponent to s-ABR signals in order to identify complexity differences between PDS and normal subjects who were stimulated with the synthetic/da/stimulus. Analysis of scaling exponents showed a statistically significant difference between the two groups. The s-ABR signal in subjects with stuttering becomes more complex due to stimulation. These findings are discussed in terms of dysfunctional sub-cortical activation in PDS populations.

Keywords

Speech evoked auditory brainstem response (s-ABR) Persistent developmental stuttering (PDS) Fractal dimension Hurst exponent Complexity 

Notes

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Approval

All procedures in studies involving human participants were performed in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Awad, S.S. (1997). The application of digital speech processing to stuttering therapy. In: Instrumentation and Measurement Technology Conference, 1997. IMTC/97. Proceedings. Sensing, Processing, Networking (pp. 1361–1367). IEEE.Google Scholar
  2. 2.
    Ai, O.C., Yunus, J. (2006). Overview of a computer-based stuttering therapy. In: Regional Postgraduate Conference on Engineering an Science (RPCES 2006) (pp. 207–211).Google Scholar
  3. 3.
    Chee, L.S., Ai, O.C., Hariharan, M., Yaacob, S. (2009). Automatic detection of prolongations and repetitions using LPCC. In: 2009 International Conference for Technical Postgraduates (TECHPOS) (pp. 1–4). IEEE.Google Scholar
  4. 4.
    Tan, T.-S., Ariff, A., Ting, C.-M., Salleh, S.-H. (2007). Application of malay speech technology in malay speech therapy assistance tools. In: International Conference on Intelligent and Advanced Systems, 2007. ICIAS 2007 (pp. 330–334). IEEE.Google Scholar
  5. 5.
    Yairi, E., & Ambrose, N. (2013). Epidemiology of stuttering: 21st century advances. Journal of Fluency Disorders, 38(2), 66–87.Google Scholar
  6. 6.
    Van Borsel, J., Achten, E., Santens, P., Lahorte, P., & Voet, T. (2003). fMRI of developmental stuttering: A pilot study. Brain and Language, 85(3), 369–376.Google Scholar
  7. 7.
    Månsson, H. (2000). Childhood stuttering: incidence and development. Journal of Fluency Disorders, 25(1), 47–57.Google Scholar
  8. 8.
    Foundas, A. L., Bollich, A. M., Corey, D. M., Hurley, M., & Heilman, K. M. (2001). Anomalous anatomy of speech–language areas in adults with persistent developmental stuttering. Neurology, 57(2), 207–215.Google Scholar
  9. 9.
    Ingham, R. J. (2001). Brain imaging studies of developmental stuttering. Journal of Communication Disorders, 34(6), 493–516.Google Scholar
  10. 10.
    Braun, A., Varga, M., Stager, S., Schulz, G., Selbie, S., Maisog, J., et al. (1997). Altered patterns of cerebral activity during speech and language production in developmental stuttering. An H2 (15) O positron emission tomography study. Brain, 120(5), 761–784.Google Scholar
  11. 11.
    Fox, P. T., Ingham, R. J., Ingham, J. C., & Hirsch, T. B. (1996). A PET study of the neural systems of stuttering. Nature, 382(6587), 158.Google Scholar
  12. 12.
    Chang, S.-E., Kenney, M. K., Loucks, T. M., & Ludlow, C. L. (2009). Brain activation abnormalities during speech and non-speech in stuttering speakers. Neuroimage, 46(1), 201–212.Google Scholar
  13. 13.
    Foundas, A. L., Bollich, A. M., Feldman, J., Corey, D. M., Hurley, M., Lemen, L. C., et al. (2004). Aberrant auditory processing and atypical planum temporale in developmental stuttering. Neurology, 63(9), 1640–1646.Google Scholar
  14. 14.
    Sommer, M., Koch, M. A., Paulus, W., Weiller, C., & Büchel, C. (2002). Disconnection of speech-relevant brain areas in persistent developmental stuttering. The Lancet, 360(9330), 380–383.Google Scholar
  15. 15.
    Boberg, E., Yeudall, L. T., Schopflocher, D., & Bo-Lassen, P. (1983). The effect of an intensive behavioral program on the distribution of EEG alpha power in stutterers during the processing of verbal and visuospatial information. Journal of Fluency Disorders, 8(3), 245–263.Google Scholar
  16. 16.
    Moore, W., Craven, D. C., & Faber, M. M. (1982). Hemispheric alpha asymmetries of words with positive, negative, and neutral arousal values preceding tasks of recall and recognition: Electrophysiological and behavioral results from stuttering males and nonstuttering males and females. Brain and Language, 17(2), 211–224.Google Scholar
  17. 17.
    Kikuchi, Y., Ogata, K., Umesaki, T., Yoshiura, T., Kenjo, M., Hirano, Y., et al. (2011). Spatiotemporal signatures of an abnormal auditory system in stuttering. Neuroimage, 55(3), 891–899.Google Scholar
  18. 18.
    Blood, I. M., & Blood, G. W. (1984). Relationship between stuttering severity and brainstem-evoked response testing. Perceptual and Motor Skills, 59(3), 935–938.Google Scholar
  19. 19.
    Kramer, M. B., Green, D., & Guitar, B. (1987). A comparison of stutterers and nonstutterers on masking level differences and synthetic sentence identification tasks. Journal of Communication Disorders, 20(5), 379–390.Google Scholar
  20. 20.
    Khedr, E., El-Nasser, W. A., Abdel Haleem, E. K., Bakr, M. S., & Trakhan, M. N. (2000). Evoked potentials and electroencephalography in stuttering. Folia phoniatrica et logopaedica, 52(4), 178–186.Google Scholar
  21. 21.
    Craig, A. (2000). The developmental nature and effective treatment of stuttering in children and adolescents. Journal of Developmental and Physical Disabilities, 12(3), 173–186.Google Scholar
  22. 22.
    King, C., Warrier, C. M., Hayes, E., & Kraus, N. (2002). Deficits in auditory brainstem pathway encoding of speech sounds in children with learning problems. Neuroscience Letters, 319(2), 111–115.Google Scholar
  23. 23.
    Ibraheem, O. A., & Quriba, A. S. (2014). Auditory neural encoding of speech in adults with persistent developmental stuttering. The Egyptian Journal of Otolaryngology, 30(2), 157.Google Scholar
  24. 24.
    Tahaei, A. A., Ashayeri, H., Pourbakht, A., & Kamali, M. (2014). Speech evoked auditory brainstem response in stuttering. Scientifica.  https://doi.org/10.1155/2014/328646.Google Scholar
  25. 25.
    Corbera, S., Corral, M.-J., Escera, C., & Idiazábal, M. A. (2005). Abnormal speech sound representation in persistent developmental stuttering. Neurology, 65(8), 1246–1252.Google Scholar
  26. 26.
    Morgan, M. D., Cranford, J. L., & Burk, K. (1997). P300 event-related potentials in stutterers and nonstutterers. Journal of Speech, Language, and Hearing Research, 40(6), 1334–1340.Google Scholar
  27. 27.
    Johnson, K. L., Nicol, T., Zecker, S. G., Bradlow, A. R., Skoe, E., & Kraus, N. (2008). Brainstem encoding of voiced consonant–vowel stop syllables. Clinical Neurophysiology, 119(11), 2623–2635.Google Scholar
  28. 28.
    Kraus, N., & Nicol, T. (2005). Brainstem origins for cortical ‘what’and ‘where’pathways in the auditory system. Trends in Neurosciences, 28(4), 176–181.Google Scholar
  29. 29.
    Skoe, E., & Kraus, N. (2010). Auditory brainstem response to complex sounds: A tutorial. Ear and Hearing, 31(3), 302.Google Scholar
  30. 30.
    Russo, N., Nicol, T., Musacchia, G., & Kraus, N. (2004). Brainstem responses to speech syllables. Clinical Neurophysiology, 115(9), 2021–2030.Google Scholar
  31. 31.
    Brand, A., Behrend, O., Marquardt, T., McAlpine, D., & Grothe, B. (2002). Precise inhibition is essential for microsecond interaural time difference coding. Nature, 417(6888), 543–547.Google Scholar
  32. 32.
    Dobie, R. A., & Berlin, C. I. (1979). Binaural interaction in brainstem-evoked responses. Archives of Otolaryngology, 105(7), 391–398.Google Scholar
  33. 33.
    Skoe, E., Nicol, T., & Kraus, N. (2011). Cross-phaseogram: Objective neural index of speech sound differentiation. Journal of Neuroscience Methods, 196(2), 308–317.Google Scholar
  34. 34.
    Parish, L., Worrell, G. A., Cranstoun, S., Stead, S. M., Pennell, P., & Litt, B. (2004). Long-range temporal correlations in epileptogenic and non-epileptogenic human hippocampus. Neuroscience, 125(4), 1069–1076.Google Scholar
  35. 35.
    Marmarelis, V. Z., Shin, D. C., Song, D., Hampson, R. E., Deadwyler, S. A., & Berger, T. W. (2013). Nonlinear modeling of dynamic interactions within neuronal ensembles using principal dynamic modes. Journal of Computational Neuroscience, 34(1), 73–87.MathSciNetGoogle Scholar
  36. 36.
    Keefe, D.H., Burns, E.M., Ling, R., Laden, B. (1990). Chaotic dynamics of otoacoustic emissions. In: The mechanics and biophysics of hearing (pp. 194–201). Springer.Google Scholar
  37. 37.
    Mozaffarilegha, M., Esteki, A., Ahadi, M., & Nazeri, A. (2016). Identification of dynamic patterns of speech evoked auditory brainstem response based on ensemble empirical mode decomposition and nonlinear time series analysis methods. International Journal of Bifurcation and Chaos.  https://doi.org/10.1142/S0218127416502023.Google Scholar
  38. 38.
    Mozaffarilegha, M., Esteki, A., Ahadi, M., & Nazeri, A. (2016). Identification of dynamic patterns of speech-evoked auditory brainstem response based on ensemble empirical mode decomposition and nonlinear time series analysis methods. International Journal of Bifurcation and Chaos.  https://doi.org/10.1142/S0218127416502023.Google Scholar
  39. 39.
    Bunde, A., Havlin, S., Kantelhardt, J. W., Penzel, T., Peter, J.-H., & Voigt, K. (2000). Correlated and uncorrelated regions in heart-rate fluctuations during sleep. Physical Review Letters, 85(17), 3736.Google Scholar
  40. 40.
    Ashkenazy, Y., Ivanov, P. C., Havlin, S., Peng, C.-K., Goldberger, A. L., & Stanley, H. E. (2001). Magnitude and sign correlations in heartbeat fluctuations. Physical Review Letters, 86(9), 1900.Google Scholar
  41. 41.
    Namazi, H. R. (2017). Fractal-based analysis of the influence of music on human respiration. Fractals, 25(06), 1750059.Google Scholar
  42. 42.
    Namazi, H., & Kiminezhadmalaie, M. (2015). Diagnosis of lung cancer by fractal analysis of damaged DNA. Computational and Mathematical Methods in Medicine.  https://doi.org/10.1155/2015/242695.MathSciNetGoogle Scholar
  43. 43.
    Namazi, H., & Kulish, V. V. (2015). Fractional diffusion based modelling and prediction of human brain response to external stimuli. Computational and Mathematical Methods in Medicine.  https://doi.org/10.1155/2015/148534.MathSciNetzbMATHGoogle Scholar
  44. 44.
    Namazi, H., Kulish, V. V., Delaviz, F., & Delaviz, A. (2015). Diagnosis of skin cancer by correlation and complexity analyses of damaged DNA. Oncotarget, 6(40), 42623.Google Scholar
  45. 45.
    Namazi, H., Kulish, V. V., Hussaini, J., Hussaini, J., Delaviz, A., Delaviz, F., et al. (2016). A signal processing based analysis and prediction of seizure onset in patients with epilepsy. Oncotarget, 7(1), 342.Google Scholar
  46. 46.
    Annadhason, A. (2012). Methods of fractal dimension computation. IRACST—International Journal of Computer Science and Information Technology and Security (IJCSITS).Google Scholar
  47. 47.
    Rényi, A. (1955). On a new axiomatic theory of probability. Acta Mathematica Hungarica, 6(3–4), 285–335.MathSciNetzbMATHGoogle Scholar
  48. 48.
    Schroeder, M. (1991). Chaos, fractals, power laws: Minutes from an infinite paradise. New York: Freeman.zbMATHGoogle Scholar
  49. 49.
    Namazi, H., Khosrowabadi, R., Hussaini, J., Habibi, S., Farid, A. A., & Kulish, V. V. (2016). Analysis of the influence of memory content of auditory stimuli on the memory content of EEG signal. Oncotarget, 7(35), 56120.Google Scholar
  50. 50.
    Cuadrado, E. M., & Weber-Fox, C. M. (2003). Atypical syntactic processing in individuals who stutter: Evidence from event-related brain potentials and behavioral measures. Journal of Speech, Language, and Hearing Research, 46(4), 960–976.Google Scholar
  51. 51.
    Banai, K., Nicol, T., Zecker, S. G., & Kraus, N. (2005). Brainstem timing: implications for cortical processing and literacy. Journal of Neuroscience, 25(43), 9850–9857.Google Scholar
  52. 52.
    Wible, B., Nicol, T., & Kraus, N. (2005). Correlation between brainstem and cortical auditory processes in normal and language-impaired children. Brain, 128(2), 417–423.Google Scholar
  53. 53.
    Gopal, K. V., & Pierel, K. (1999). Binaural interaction component in children at risk for central auditory processing disorders. Scandinavian Audiology, 28(2), 77–84.Google Scholar
  54. 54.
    Delb, W., Strauss, D. J., Hohenberg, G., Plinkert, P. K., & Delb, W. (2003). The binaural interaction component (BIC) in children with central auditory processing disorders (CAPD): El componente de interactión binaural (BIC) en niños con desórdenes del procesamiento central auditivo (CAPD). International Journal of Audiology, 42(7), 401–412.Google Scholar
  55. 55.
    Delb, W., Strauss, D. J., & Plinkert, P. K. (2004). A time-frequency feature extraction scheme for the automated detection of binaural interaction in auditory brainstem responses. International Journal of Audiology, 43(2), 69–78.Google Scholar

Copyright information

© Taiwanese Society of Biomedical Engineering 2018

Authors and Affiliations

  1. 1.Department of Biomedical Engineering and Physics, School of MedicineShahid Beheshti University of Medical SciencesTehranIran
  2. 2.Biomedical Engineering DepartmentAmirkabir University of TechnologyTehranIran
  3. 3.Department of Audiology, School of Rehabilitation SciencesIran University of Medical SciencesTehranIran

Personalised recommendations