نوع مقاله: مقاله کامل پژوهشی

نویسندگان

1 دکتری مهندسی پزشکی، آزمایشگاه بین‌رشته‌ای ابن‌سینا، دانشکده‌ی فیزیک، دانشگاه شهید بهشتی، تهران

2 دانشیار، دانشکده‌ی فیزیک، دانشگاه شهید بهشتی، تهران

10.22041/ijbme.2018.78126.1309

چکیده

از ویژگی­های پردازش­های ساقه‌ی مغز، حضور پیچیدگی و تاثیرگذاری عوامل فردی در رمزگذاری اصوات می‌باشد. تشریح این پردازش‌ها بر مبنای تحلیل‌های خطی دشوار بوده و این خود انگیزه‌ای است مبنی بر استفاده از روش­های غیرخطی که قادر به تحلیل مناسب­تر سیگنال­های غیرمانا هستند. هدف این تحقیق، بررسی رفتار ساقه‌ی مغز در پاسخ به تحریک شنوایی هجای گفتاری /دا/ (s-ABR)، با استفاده از تحلیل چندفراکتالی MFDFA)) به همراه روش­های تحلیل روندزدایی، شامل تجزیه به مقادیر تکین (SVD)، روش تطبیقی (AD) و روش تجزیه به مدهای تجربی (EMD) می‌باشد. در این تحلیل، پس از ثبت پاسخ­های شنوایی ساقه‌ی مغز برانگیخته شده با هجای ساختگی /دا/ در 40 فرد بزرگ‌سال هنجار با میانگین سنی 22 سال، تحلیل MFDFA روی سیگنال، جهت ارزیابی تغییرات پیچیدگی و چندمقیاسی آن‌ها انجام می‌شود. هم‌چنین، به‌منظور روندزدایی بهینه از سیگنال، ابتدا روش­های SVD، AD و EMD روی داده‌های ورودی اعمال می‌شود. با محاسبه‌ی تابع افت‌وخیز و ارزیابی رفتار مقیاسی، نماهای مقیاسی مانند نمای هارست تعمیم‌یافته و طیف تکینگی تعیین می‌شود. نتایج نشان می‌دهد که در مقیاس­های کوچک، سیگنال دارای خاصیت نامانایی است. اما در مقیاس­های بزرگ، ویژگی سیستم توسط روند کنترل می‌شود. تمام نمونه­های مورد بررسی در مقیاس  میلی‌ثانیه، دارای تغییر رفتاری در تابع افت‌وخیز هستند که معادل با روند تناوبی غالب است. متوسط نمای هارست تعمیم‌یافته‌ی محاسبه‌شده توسط این روش در مقیاس‌های کوچک، یعنی  میلی‌ثانیه، برابر با  در 68 درصد تراز تطابق است. وابستگی  به ، نشان می­دهد که سیگنال s-ABR خاصیت چندفراکتالی دارد، که غالبا به دلیل همبستگی­ها است. پهنای طیف تکینگی، که معیاری از پیچیدگی سیگنال است، برای داده‌های مورد استفاده به طور میانگین برابر با  در تراز اطمینان  است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

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

نویسندگان [English]

  • Marjan Mozaffarilegha 1
  • Seyed Mohammad Sadegh Movahed 2

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

چکیده [English]

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.

کلیدواژه‌ها [English]

  • Speech-Auditory Brainstem Responses
  • MFDFA
  • Hurst Exponent
  • Complexity

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