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روش طبقه‌بندی کدینگ تنک و نمایش تحلیلی سیگنال الکتروانسفالوگرام برای تشخیص اختلال وسواس اجباری

نوع مقاله : مقاله کامل پژوهشی

نویسندگان

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

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

10.22041/ijbme.2022.542095.1733

چکیده

اختلال وسواس اجباری چهارمین اختلال روانی شایع و دهمین علت ناتوانی در سراسر جهان است. این بیماری می‌تواند منجر به اختلال در عمل‌کردهای مختلف شناختی مانند توجه، حافظه، تفکر، پردازش شنیداری کلمات و شناخت بصری شود. مطالعات گذشته نشان دهنده‌ی تغییر در ارتباط بین فعالیت لوب‌های مختلف مغز بیماران مبتلا به وسواس اجباری است. از این رو کمی‌سازی تقارن و ارتباطات بین نواحی مختلف مغزی توجه زیادی را به خود جلب کرده است. در این مطالعه رویکردی جدید و کارآمد بر اساس نمایش تحلیلی سیگنال‌های الکتروانسفالوگرام و ویژگی‌های آماری ارائه شده است تا امکان کمی‌سازی تفاوت مولفه‌های ذاتی مربوط به فعالیت‌های مغزی بین لوب‌های مغز فراهم شود. بدین منظور پوش‌های فاز و دامنه‌ی سیگنال‌های تحلیلی الکتروانسفالوگرام استخراج و تجزیه و تحلیل شده است. هم‌چنین از روش طبقه‌بندی کم‌ترین مربعات غیرمنفی تنک برای تمایز بین گروه سالم و بیماران مبتلا به اختلال وسواس اجباری استفاده شده است. قابلیت تمایز روش پیشنهادی با داده‌های الکتروانسفالوگرام 19 فرد سالم و 11 بیمار حین انجام تکالیف ساده‌ی فلانکر مورد مطالعه قرار گرفته است. نتایج به دست آمده، موثر بودن ترکیب اطلاعات دامنه و فاز در تشخیص بیماری وسواس اجباری را با میانگین صحت 78/93 درصد نشان داده است. در مقایسه بین نواحی مختلف نیز ویژگی‌های مستخرج بین نیم‌کره‌‌های مغزی و آن‌هایی که از لوب پیشانی و شبکه‌ی پیشانی-آهیانه‌ای استخراج شده‌اند، کارایی بیش‌تری در تشخیص بیماری از خود نشان داده‌اند. هم‌چنین این مطالعه اهمیت بیش‌تر استفاده از اطلاعات دامنه در تشخیص اختلال وسواس اجباری را نشان داده است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Sparse Coding Classification and Analytic EEG Signal Representation for Obsessive Compulsive Disorder Detection

نویسندگان [English]

  • Farzaneh Manzari 1
  • Peyvand Ghaderyan 2

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

چکیده [English]

Obsessive-Compulsive Disorder (OCD) is the fourth most common mental disorder and the tenth cause of disability worldwide. This disorder can lead to cognitive impariments in attention, memory, thinking, auditory processing of words and visual cognition. Previous studies have demonstrated that OCD is associated with changes in connectivity between different lobes of the brain. Hence, the quantification of symmetry and connectivity between different brain regions has attracted great attention. This study has provided a new efficient approach based on analytic representation of EEG signals and statistical features to quantify the difference of intrinsic components of brain activity between brain lobes. For this purpose, phase spectra and amplitude envelopes of the analytic EEG signals have been extracted and analyzed. Furthermore, Non-Negative Least Square sparse classification method has been used for discriminating between healthy control group and OCD patients. The detection capability of the proposed method has been studied in 19 healthy subjects and 11 patients, performing simple flanker tasks. The obtained results have demonstrated the effectiveness of the combined amplitude and phase information in OCD detection with high average accuracy rate of 93.78 %. In comparison between different regions, the inter-hemispheric features and those extracted from the frontal lobe and frontal-parietal network have shown more efficiency in diagnosing the OCD. This study has also highlighted more importance of amplitude information in the OCD detection.

کلیدواژه‌ها [English]

  • Empirical Mode Decomposition (EMD)
  • Statistical Features
  • Non-Negative Least Square Sparse (NNLS)
  • Amplitude Envelope
  • Phase Spectra
  • Flanker Task
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نشریه‌ی علمی مهندسی پزشکی زیستی
دوره 15، شماره 4
اسفند 1400
صفحه 313-328
فایل ها
سابقه مقاله
  • تاریخ دریافت: 12 آبان 1400
  • تاریخ بازنگری: 07 فروردین 1401
  • تاریخ پذیرش: 14 فروردین 1401
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