Abstract
The speech auditory brainstem response (sABR) is an objective clinical tool to diagnose particular impairments along the auditory brainstem pathways. We explore the scaling behavior of the brainstem in response to synthetic /da/ stimuli using a proposed pipeline including Multifractal Detrended Moving Average Analysis (MFDMA) modified by Singular Value Decomposition. The scaling exponent confirms that all normal sABR are classified into the non-stationary process. The average Hurst exponent is H = 0:77 ± 0:12 at 68% confidence interval indicating long-range correlation which shows the first universality behavior of sABR. Our findings exhibit that fluctuations in the sABR series are dictated by a mechanism associated with long-term memory of the dynamic of the auditory system in the brainstem level. The q-dependency of h(q) demonstrates that underlying data sets have multifractal nature revealing the second universality behavior of the normal sABR samples. Comparing Hurst exponent of original sABR with the results of the corresponding shuffled and surrogate series, we conclude that its multifractality is almost due to the long-range temporal correlations which are devoted to the third universality. Finally, the presence of long-range correlation which is related to the slow timescales in the subcortical level and integration of information in the brainstem network is confirmed.
Highlights
The ABR to click sounds cannot anticipate encoding of complex sounds because of the nonlinear dynamic behavior of the auditory system
In the presence of a sinusoidal trend superimposed on the data, the fluctuation functions derived by Multifractal Detrended Fluctuations Analysis (MFDFA) or Multifractal Detrended Moving Average Analysis (MFDMA) contain at least one cross-over
We evaluate the pattern of speech auditory brainstem response (sABR) signals and measure long-range temporal correlations in sABR series which has not been obtained as yet using other methods
Summary
The ABR to click sounds cannot anticipate encoding of complex sounds because of the nonlinear dynamic behavior of the auditory system. The generalized form of DFA which is known as Multifractal Detrended Fluctuations Analysis (MFDFA) is one of the best-known methods to capture multifractality in series[24,49], and used in various fields, ranging from cosmic microwave background radiations[50], sunspot fluctuations[51,52], plasma fluctuations[53], astronomy[54], economic time series[55,56,57], music[58,59], traffic jamming[60] to image processing[61,62] and biological time series[21,29,30,31,63] This approach is not suitable to completely remove sinusoidal and power-law trends[64,65,66]. The SVD method can remove trends corresponding to the sinusoidal trends in the results[75,78]
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