استخراج اطلاعات تفاضلی سیگنال‌های الکتروانسفالوگرام جهت تشخیص اختلال وسواس اجباری
محورهای موضوعی : پردازش چند رسانه ای، سیستمهای ارتباطی، سیستمهای هوشمندفرزانه منظری 1 , پیوند قادریان 2
1 - دانشجوی کارشناسی ارشد، دانشکده‌ی مهندسی پزشکی، دانشگاه صنعتی سهند تبریز
2 - دانشکده مهندسی پزشکی، دانشگاه صنعتی سهند، تبریز، ایران
کلید واژه: ویژگی‌های آماری تفاضلی, اختلال وسواس اجباری, تجزیه به مؤلفه‌های مد ذاتی, ماشین بردار پشتیبان,
چکیده مقاله :
اختلال وسواس اجباری یک بیماری مزمن ذهنی و اجتماعی است که در بین حدود 2 تا 3 درصد جمعیت انسان‌ها شیوع دارد و سبب مشکلاتی در عملکردهای شناختی و افت کیفیت زندگی مبتلایان می‌گردد. به همین علت تشخیص صحیح و به هنگام آن می‌تواند به روان پزشکان در درمان و یا کنترل این بیماری کمک کند. تحقیقات پیشین در زمینه‌ی بیماری وسواس اجباری نشان‌دهنده‌ی اختلال در فعالیت الکتریکی بین نواحی مختلف مغزی بوده‌اند. بنابراین، در این مطالعه رویکردی جدید در زمینه‌ی تشخیص اختلال وسواس اجباری ارائه شده است که مبتنی بر تجزیه سیگنال به توابع پایه‌ای ذاتی و استخراج تغییرات لحظه‌ای دامنه و فاز سیگنال الکتروانسفالوگرام به صورت تفاضلی در حین انجام تکالیف فلانکر می‌باشد. ارزیابی الگوریتم پیشنهادی با داده‌های الکتروانسفالوگرام 19 فرد سالم و 11 بیمار دارای اختلال وسواس اجباری توسط طبقه‌بند ماشین بردار پشتیبان صورت گرفته است. نتایج به دست آمده، قابلیت روش پیشنهادی در تشخیص بیماری را با صحت بالای 89/93 درصد با استفاده از اطلاعات تفاضلی دامنه‌ی سیگنال الکتروانسفالوگرام نشان داده‌اند. در مقایسه بین نواحی مختلف ویژگی‌های آماری مستخرج از لوب پیشانی، شبکه‌ی پیشانی-آهیانه و نیم‌کره‌ی مغزی کارایی بیشتری در تشخیص بیماری ارائه داده‌اند.
Introduction: Obsessive-Compulsive Disorder (OCD) is a chronic mental and social disease that is prevalent in about 2 to 3% of the human population leading to cognitive impairments and affected quality of patient's life. Therefore, a reliable and timely diagnosis can help psychiatrists in better treating or controlling this disease.Method: Previous studies have demonstrated interdependence impairments between different brain regions in patients with OCD. Hence, this study has provided a new approach based on the decomposition of signals into intrinsic components and extraction of differential transient changes in amplitude envelope and phase spectra of the EEG signal recorded during Flanker tasks. The proposed algorithm has been evaluated using 19 healthy subjects and 11 patients by the Support Vector Machine (SVM) classifier.Result: The obtained results have confirmed the capability of the proposed method in diagnosing the disease with high accuracy of 93.89% using amplitude differential information of the electroencephalogram signal.Conclusion: In comparison between different regions, the statistical features extracted from the frontal lobe, the frontal-parietal network, and the inter-hemispheric features have offered better detection ability.
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