نوع مقاله: مقاله کامل پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، دانشکده‌ی مهندسی برق، دانشگاه صنعتی شریف، تهران، ایران

2 استادیار، دانشکده‌ی مهندسی برق، دانشگاه صنعتی شریف، تهران، ایران

3 استادیار، دانشکده‌ی مهندسی برق، دانشگاه صنعتی شریف، تهران، ایران / دانشیار، دانشکده‌ی مخابرات و پردازش اطلاعات، دانشگاه گنت، گنت، بلژیک

چکیده

قرار­ گرفتن در وضعیت­های مختلف ادراکی، شناختی و احساسی با نوعی انتشار اطلاعات از طریق نوسانات نورون­های مغزی همراه است. بررسی این نوسانات و به طور مشخص­­­ ارتباطات و تعاملات میان بخش­های مختلف مغز، می­تواند اطلاعات مفیدی درباره­ی نحوه‌ی واکنش مغز در­ وضعیت­های مختلف حاصل نماید. ارتباطات بین نواحی مختلف مغز به سه دسته­ی ساختاری، موثر و کارکردی تقسیم‌بندی می­شوند. دسته­ی اول به ارتباط بین نورون­های نواحی مجاور می­پردازد، در حالی که دسته­ی دوم و سوم بر هم‌سانی زمانی بین نوسانات بخش­های نه لزوما مجاور تمرکز دارند. اگر چه سیگنال­های EEG به دلیل دقت مکانی نسبتا پایین، مناسب­ترین معیار برای سنجش ارتباطات کارکردی و موثر بین بخش­های مختلف مغز نیستند، اما بررسی آماری این سیگنال­ها می­تواند در تشخیص هم‌زمانی بین نوسانات نواحی مختلف مغز کمک قابل توجهی نماید. در این مقاله، چارچوبی نوین برای پیش­بینی وقوع تشنج با استفاده از سیگنال­های EEG ارائه شده که از معیار علیت گرنجر در حوزه­ی فرکانس برای اندازه­گیری میزان هم­زمانی نوسانات سیگنال­های EEG در زمان‌های Inter-ictal و Pre-ictal استفاده می­کند. در ادامه، با به کارگیری یک طبقه‌بند Logistic Regression با عبارت تنظیم‌کننده‌ی درجه‌ی اول به تفکیک نمونه‌های استخراج شده از این دو بازه‌ی زمانی از یک‌دیگر پرداخته شده است. در گام آخر، با در نظر گرفتن بازه‌های زمانی متوالی، در صورتی که به تعداد مشخصی بازه‌ی مربوط به Pre-ictal شناخته شود، وقوع تشنج اعلام می‌گردد. شبیه‌سازی‌های انجام شده روی مجموعه­ی داده­ی CHB-MIT به ازای افق پیش‌بینی 10 دقیقه به نرخ حساسیت 03/95% و نرخ پیش‌بینی نادرست 14/0 بر ساعت منتج شده است که نشان دهنده‌ی عمل‌کرد قابل قبول روش پیشنهادی در مقایسه با بهترین نتایج گزارش شده در سایر مقالات می‌باشد.

کلیدواژه‌ها

عنوان مقاله [English]

Analysis of Brain Connectivity for Epileptic Seizure Prediction using EEG Signals

نویسندگان [English]

  • Saeed Ghodsi 1
  • Hoda Mohammadzade 2
  • Hamid Aghajan 3

1 M.Sc. Student, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran

2 Assistant Professor, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran

3 Assistant Professor, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran / Associate Professor, Telecommunications and Information Processing Department, Ghent University, Ghent, Belium

چکیده [English]

Different perceptual, cognitive and emotional situations results in a kind of information flow in the brain by means of coordinated neuronal oscillations. Analysing these oscillations, especially synchronizations of different brain regions, can illustrate the brains response in the aforementioned situations. In the literature, connectivity between brain regions is divided into the three groups of structural, effective and functional, s.t. the first one refers to the connectivity between nearby regions, while the second and third ones focus on the synchronization of oscillations of arbitrary located regions. Although EEG is not the best choice for analyzing functional and effective connectivity between brain regions due to its relatively poor spatial resolution, extracting its statistical features may be helpful in the analysis of synchronization of brain oscillations. In this paper, a novel framework for the prediction of seizure occurrence using EEG signals is proposed which utilizes the Granger causality approach in frequency domain to measure synchronization of EEG signals in the Inter-ictal and Pre-ictal time periods. Afterwards, a Logistic Regression classifier with Lasso regularization is used to discriminate the samples extracted from these two periods. At last, if a predefined number of consecutive samples are labled as Pre-ictals, a seizure occurrence alarm is issued. Experimental simulations on the CHB-MIT dataset resulted in 95.03% sensitivity and 0.14/hour false prediction rate, for 10min prediction horizon, which demonstrates effectiveness of our proposed method compared to the state-of-the-arts.

کلیدواژه‌ها [English]

  • Computational Neuroscience
  • Seizure Prediction
  • Functional and Effective Connectivity
  • Machine Learning

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