تشخیص بیماری نقص‌توجه/بیش‌فعالی به کمک تحلیل ارتباطات نواحی مختلف مغزی و روش تاب-خوردگی زمانی پویا

نوع مقاله : علمی-پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، دانشکده مهندسی پزشکی، دانشگاه صنعتی سهند، تبریز، ایران

2 دانشیار، آزمایشگاه علوم اعصاب محاسباتی، دانشکده مهندسی پزشکی، دانشگاه صنعتی سهند، تبریز، ایران

3 استاد، دانشکده مهندسی پزشکی، دانشگاه صنعتی سهند، تبریز، ایران

چکیده

تشخیص بیماری‌های عصب تحولی مانند اختلال نقص‌توجه-بیش‌فعالی به دلیل تأثیر آن بر کیفیت زندگی انسان، مورد توجه زیادی در مطالعات بالینی قرار گرفته است. این اختلال در اثر عوامل ژنتیکی، ناهنجاری های آناتومیکی و کارکردی مغز ایجاد می‌شود که می‌تواند منجر به نقص در ادراک زمان، اختلال در حافظه‌کاری و بی-توجهی شود. ازآنجایی که بررسی تقارن بین فعالیت‌های نواحی مختلف مغز ممکن است نقش مهمی در تشخیص زودهنگام این اختلال ایفا کند، کمی‌سازی شباهت بین سیگنال‌های مغزی یکی از چالش‌های موجود در زمینه تشخیص اختلال نقص‌توجه-بیش‌فعالی است. هدف از این مطالعه محاسبه تقارن مابین جفت کانال‌های موجود در نواحی قشری بین نیم‌کره‌ای یا درون نیم‌کره‌ای مغز است. بدین منظور، الگوریتم جدیدی مبتنی بر تاب‌خوردگی هیلبرت پویا به صورت ویژگی دو متغیره در مرحله استخراج ویژگی ارائه شده و به‌جهت بررسی توانایی و قدرت تفکیک‌پذیری این ویژگی‌ها در نواحی مختلف مغزی، طبقه‌بند ماشین بردار پشتیبان پیشنهاد گردیده است. توانایی روش پیشنهادی در ایجاد تمایز مابین مناطق مختلف مغزی نیز بررسی شده است. الگوریتم پیشنهادی به کمک مجموعه داده‌های سیگنال الکتروانسفالوگرام، شامل 14 کودک بیمار مبتلا به اختلال نقص توجه-بیش فعالی از نوع ترکیبی و 19 کودک سالم که تکالیف بازتولید زمانی را انجام می‌دادند، ارزیابی شد. این روش در تفکیک افراد بیمار از گروه سالم به میانگین صحت بالای007/0±38/94 درصد دست یافت. نتایج تجربی همچنین عملکرد بهتر روش پیشنهادی را در مقایسه با روش‌های قبلی تشخیص بیماری نقص توجه-بیش فعالی با استفاده از سیگنال‌های EEG نشان دادند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Diagnosis of Attention Deficit/Hyperactivity Disorder using the analysis of different brain regions connectivity and Dynamic Time Warping method

نویسندگان [English]

  • F. Moghaddam 1
  • P. Ghaderyan 2
  • M. Shamsi 3
1 M.Sc. Student, Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran
2 Associate Professor, Computational Neuroscience Laboratory, Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran
3 Professor, Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran
چکیده [English]

The diagnosis of neurodevelopmental diseases, such as attention deficit/hyperactivity disorder (ADHD), has gained great attention in clinical studies due to its effect on the quality of human life. This disorder is caused by genetic factors, and anatomical and functional brain abnormalities, which can lead to timing deficits, working memory impairments, and inattention. Since the investigation of symmetry between activities of different brain regions may play an important role in the early diagnosis of this disorder, similarity quantification between brain signals is one of the existing challenges in the field of ADHD detection. The goal of this study is to compute symmetry between certain cortical areas from inter-hemispheric or intra-hemispheric channel pairs. For this purpose, a new algorithm based on dynamic time warping as a bivariate feature extraction step and support vector machine (SVM) classifier has been proposed. The proposed method's ability in distinct brain regions has also been explored.
The proposed methods have been evaluated on electroencephalogram (EEG) recordings of 14 ADHD children and 19 age-matched healthy controls performing a time-reproduction task. It has achieved high average accuracy rates of 94.38±0.007 in discriminating between healthy controls and patients with ADHD. The experimental results have also demonstrated the superior performance of the proposed method in comparison with previous ADHD detection methods using EEG signals.

کلیدواژه‌ها [English]

  • ADHD
  • EEG Signals
  • support vector machine
  • Bivariate Features
  • Dynamic time warpping
  • Timing Deficits

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مجله مهندسی برق دانشگاه تبریز
دوره 53، شماره 3
آبان 1402
صفحه 223-233

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