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کلیجی, افشین, عزتی, نعمت اله, باهنر, محسن, جعفری چاشمی, عبداله, مصطفی پور, احسان. (1405). معرفی مشخصه استخراج غیرخطی مبتنی بر ضرایب DT-CWT سیگنال های EEG مغز برای تشخیص تشنج‌های صرعی. هنرهای کاربردی, 24(84), 87-99. doi: 10.22075/jme.2025.30724.2461
افشین کلیجی; نعمت اله عزتی; محسن باهنر; عبداله جعفری چاشمی; احسان مصطفی پور. "معرفی مشخصه استخراج غیرخطی مبتنی بر ضرایب DT-CWT سیگنال های EEG مغز برای تشخیص تشنج‌های صرعی". هنرهای کاربردی, 24, 84, 1405, 87-99. doi: 10.22075/jme.2025.30724.2461
کلیجی, افشین, عزتی, نعمت اله, باهنر, محسن, جعفری چاشمی, عبداله, مصطفی پور, احسان. (1405). 'معرفی مشخصه استخراج غیرخطی مبتنی بر ضرایب DT-CWT سیگنال های EEG مغز برای تشخیص تشنج‌های صرعی', هنرهای کاربردی, 24(84), pp. 87-99. doi: 10.22075/jme.2025.30724.2461
کلیجی, افشین, عزتی, نعمت اله, باهنر, محسن, جعفری چاشمی, عبداله, مصطفی پور, احسان. معرفی مشخصه استخراج غیرخطی مبتنی بر ضرایب DT-CWT سیگنال های EEG مغز برای تشخیص تشنج‌های صرعی. هنرهای کاربردی, 1405; 24(84): 87-99. doi: 10.22075/jme.2025.30724.2461

معرفی مشخصه استخراج غیرخطی مبتنی بر ضرایب DT-CWT سیگنال های EEG مغز برای تشخیص تشنج‌های صرعی

مدل سازی در مهندسی
دوره 24، شماره 84، فروردین 1405، صفحه 87-99    اصل مقاله (692.83 K)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.22075/jme.2025.30724.2461
نویسندگان
افشین کلیجی1؛ نعمت اله عزتی1؛ محسن باهنر2؛ عبداله جعفری چاشمی2؛ احسان مصطفی پور* 1
1دانشکده مهندسی برق و کامپیوتر، دانشگاه صنعتی ارومیه، ارومیه، ایران
2دانشگاه آزاد اسلامی واحد مهدی شهر، مهدیشهر، ایران
تاریخ دریافت: 02 خرداد 1402،  تاریخ بازنگری: 23 بهمن 1402،  تاریخ پذیرش: 22 اسفند 1403 
چکیده
صرع نوعی بیماری مغزی است که با مشاهده سیگنال های EEG قابل تشخیص است. ابن بیماری غالبا در کودکان رخ می دهد. با این وجود، برخی از موارد در بزرگسالان نیز مشاهده می شود. تشخیص این بیماری در مراحل اولیه برای پزشکان یک وظیفه چالش برانگیز است. نویسندگان در این کار، سیگنال EEG صرعی و طبیعی را با اتخاذ رویکرد یادگیری عمیق کلاس‌بندی کرده‌اند. برای دست یابی به ویژگی های کارآمد، موجک مختلط درختی دوگانه (DTCWT) در نظر گرفته شده است. سپس، ضرایب موجک تجزیه شده به استخراج ویژگی غیرخطی اعمال می شود. این ویژگی ها به عنوان ورودی کلاس بند تابع پایه هیبریدی شعاعی (RBF) استفاده می گردند. با استفاده از روش پیشنهادی، حدود 99 درصد دقت کلاس بندی مشاهده می شود. این امر، نیازمند بهبود قابل توجه الگوریتم پیشنهادی در مقایسه با سایر الگوریتم های ارائه شده قبلی است. اولین بار است که جهت تشخیص صرع از استخراج ویژگی غیر خطی بر روی ضرایب DT-CWT یک سیگنال EEG استفاده می شود.
کلیدواژه‌ها
صرع؛ الگوریتم k-means؛ مشخصه های غیر خطی؛ شبکه های تابع پایه شعاعی؛ کلاس بندی EEG مغز؛ کاهش ویژگی
عنوان مقاله [English]
Introducing a Non-Linear Feature Extraction Based on DT-CWT Coefficients of EEG Signals for Detecting Epileptic Seizures
نویسندگان [English]
Afshin Koleiji1؛ Nematollah Ezzati1؛ Mohsen Bahonar2؛ Abdollah Jafari Chashmi2؛ Ehsan Mostafapour1
1Faculty of Electrical And Computer Engineering, Urmia University of Technology, Urmia, Iran
2Islamic Azad University, Mehdi Shahr Branch, Mehdi Shahr, Iran
چکیده [English]
Epilepsy is a type of brain disease that can be diagnosed by observing EEG signals. This disease often occurs in children, but some cases are also observed in adults. Early diagnosis of this disease is a challenging task for doctors. In this study, the authors have classified epileptic and normal EEG signals by adopting a deep learning approach. To obtain efficient features, the Dual-Tree Complex Wavelet Transform (DTCWT) is considered. Then, the wavelet coefficients are decomposed to extract nonlinear features. These features are used as input to the Radial Basis Function (RBF) hybrid base classifier. Using the proposed method, approximately 99% classification accuracy is achieved, which requires a significant improvement compared to previous proposed algorithms. This is the first time that nonlinear feature extraction from DT-CWT coefficients of an EEG signal is used for epilepsy diagnosis.
کلیدواژه‌ها [English]
Epilepsy, K-Means algorithm, Non-linear features, Radial basis function networks, Brain EEG classification, Feature reduction
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آمار
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