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

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده مهندسی برق و کامپیوتر، دانشگاه صنعتی ارومیه، ارومیه، ایران

2 دانشگاه آزاد اسلامی واحد مهدی شهر، مهدیشهر، ایران

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

موضوعات

عنوان مقاله [English]

Introducing a Non-Linear Feature Extraction Based on DT-CWT Coefficients of EEG Signals for Detecting Epileptic Seizures

نویسندگان [English]

  • Afshin Koleiji 1
  • Nematollah Ezzati 1
  • Mohsen Bahonar 2
  • Abdollah Jafari Chashmi 2
  • Ehsan Mostafapour 1
1 Faculty of Electrical And Computer Engineering, Urmia University of Technology, Urmia, Iran
2 Islamic 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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مدل سازی در مهندسی
دوره 24، شماره 84
در حال تکمیل شدن
فروردین 1405
صفحه 87-99

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  • تاریخ دریافت: 02 خرداد 1402
  • تاریخ بازنگری: 23 بهمن 1402
  • تاریخ پذیرش: 22 اسفند 1403

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