Multifractal Analysis of Auditory Brainstem Responses to Spoken Syllable /da/

Mozaffarilegha, Marjan; Movahed, Seyed Mohammad Sadegh

doi:10.22041/ijbme.2018.78126.1309

Document Type : Full Research Paper

Authors

¹ Ph.D in Biomedical Engineering, Ibn-Sina Labratory, Department of Physics, Shahid Beheshti University, Tehran, Iran

² Associate Professor, Department of Physics, Shahid Beheshti University, Tehran, Iran

https://doi.org/10.22041/ijbme.2018.78126.1309

Abstract

The complexities and the effects of inter-subject variations on the encoding of sounds are features of the brainstem processing. Examining such data based on linear analysis is not reliable, encouraging to take into account non-linear methods which are effective ways of explaining such non-stationary signals. The purpose of this study is to explore the behavior of the brainstem in response to complex auditory stimuli /da/ using Multifractal Detrended Fluctuation Analysis modified by Singular Value Decomposition (SVD), Adaptive Detrending (AD) and Empirical Mode Decomposition (EMD). Auditory brainstem responses to synthetic /da/ stimuli were recorded for 40 normal subjects with a mean age of 22.7 years. MFDFA is carried out on the s-ABR time series data to evaluate the variation of their complexity and multiscaling. To utilize optimal Detrending of s-ABR time series, AD, SVD and EMD algorithms are applied on time series. By computing the fluctuation function and evaluating scaling behavior, scaling exponents such as generalized Hurst exponent and multifractal spectrum are determined. Given results in this method indicate that underlying signal has non-stationary nature in small scales, but property of system is controlled by trend in large scales. There is a crossover at msec on the behavior of fluctuation function corresponding to dominant sinusoidal trend in all samples. The average of Hurst exponent is at 68% confidence interval in small scales msec. The -dependency of demonstrate that underlying data sets have multifractality nature and are almost due to long-range correlations. The width of singularity spectrum which is a measure of the signal complexity of underlying data in average equates to at confidence interval.

Keywords

Main Subjects

Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing

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Iranian Journal of Biomedical Engineering

Multifractal Analysis of Auditory Brainstem Responses to Spoken Syllable /da/

References

References

Volume 11, Issue 3
September 2017
Pages 255-264

Multifractal Analysis of Auditory Brainstem Responses to Spoken Syllable /da/

References

References

Volume 11, Issue 3September 2017Pages 255-264

Volume 11, Issue 3
September 2017
Pages 255-264