تشخیص صرع در سیگنال‌های الکتروانسفالوگرافی (EEG) بر اساس ویژگی طیف کلی موجک (GWS) با استفاده ماشین بردار پشتیبان

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مهندسی پزشکی، دانشکده مهندسی برق و کامپیوتر، دانشگاه تبریز، تبریز، ایران

چکیده

در حدود یک درصد از مردم دنیا از صرع رنج می‌برند. اولین مرحله از درمان صرع، تشخیص به‌موقع و صحیح آن است. یکی از راه‌های تشخیص صرع، تجزیه و تحلیل دقیق سیگنال الکتروانسفالوگرافی (EEG) است. ویژگی‌های مختلفی جهت تشخیص این بیماری از روی سیگنال مانند دامنه سیگنال وجود دارد. در این مقاله، با بررسی اطلاعات زمان-فرکانسی سیگنال EEG در افراد مبتلا به سندرم صرع بدون تشنج و افراد سالم، روش جدیدی برای تشخیص صرع ارائه شده است. در ابتدا ویژگی طیف کلی موجک (GWS) برای سیگنال EEG افراد سالم و افراد مبتلا به سندرم صرع استخراج شده است. برای بررسی این طیف در باندهای فرکانسی، سیگنال EEG با استفاده از تبدیل موجک به 5 زیرباند تجزیه می‌گردد. سپس با اعمال این ویژگی به طبقه‌بند مبتنی‌بر ماشین بردار پشتیبان به تشخیص صرع پرداخته شده است. نتایج تجزیه و تحلیل، تفاوت قابل ملاحظه‌ای، جهت تفکیک کردن فرد بر اساس سیگنال EEG فراهم می‌کند. روش پیشنهادی در مقایسه با روش‌های قبلی، سیگنال‌های سالم و صرعی را با دقت 100% طبقه‌بندی کرده است. همچنین، مشاهده شد که مقادیر غالب GWS برای سیگنال‌های انتخاب‌شده از بیماران مبتلا به سندرم صرعی در باند فرکانسی دلتا و تتا یافت می‌شوند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Detection of Epilepsy in Electroencephalographic (EEG) Signals Based on Global Wavelet Spectrum (GWS) Using Support Vector Machine (SVM)

نویسندگان [English]

  • F. Hasanzadeh
  • S. Meshgini
Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
چکیده [English]

Approximately one percent of the world's population suffers from epilepsy. The first stage of epilepsy treatment is timely and correct diagnosis. One of the ways to diagnose epilepsy is to accurately analyze EEG signals. There are various features to diagnose the disease from a signal such as the signal amplitude. In this paper, a new method for the diagnosis of epilepsy is presented by examining the time-frequency information of the EEG signal in people with seizure-free seizure syndrome and healthy people. Initially, the Global Wavelet Spectrum (GWS) feature of the EEG signal was extracted. To interpret this Spectrum in frequency bands, EEG signals decompose to five levels by continuous wavelet transform. Then, by applying this feature, a Support vector machine-based classifier was used to diagnose epilepsy. The results of the analysis provided a significant difference in the separation of the individual based on the brain signal. The proposed method compared to the previous methods, can classify epilepsy and intact signals with 100% accuracy. It was also observed that the dominant (GWS) values for the signals selected from patients with epilepsy in the delta and theta frequency band are discussed.

کلیدواژه‌ها [English]

  • Epilepsy
  • electroencephalography
  • wavelet transform
  • global wavelet spectrum
  • support vector machine

مراجع

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