AIS-RCA: روشی مؤثر برای بهبود فضای ویژگی‌ها در افزایش دقت تشخیص حملات صرع

نوع مقاله: مقاله کامل پژوهشی

نویسندگان

1 مربی، دانشگاه آزاد اسلامی، واحد شیراز، گروه مهندسی کامپیوتر

2 دانشیار، گروه مهندسی پزشکی، دانشکده مهندسی، دانشگاه شیراز

3 دانشیار، گروه علوم و مهندسی کامپیوتر، دانشکده مهندسی، دانشگاه شیراز

10.22041/ijbme.2010.13296

چکیده

پیش‌بینی زمان وقوع حملات صرع در بیماران از جمله موضوعاتیست که مورد توجه محققان است. حملات صرع به طور نامنظم و غیر قابل پیش‌بینی شده‌ای اتفاق می‌افتند. بنابراین تشخیص حملات صرع از روی سیگنال‌های EEGکه در بازة زمانی طولانی گرفته می‌شوند؛ بسیار حائز اهمیت است. این امر تشخیصی به دو مرحله مجزای استخراج ویژگی‌ها از قطعات سیگنال EEGو اعمال الگوریتم طبقه‌بندی بر روی بردارهای ویژگی تقسیم می‌شود. به همین منظور در مرحله اول با استفاده از تحلیل زمان- فرکانس بر روی قطعات سیگنال EEGو به‌دست آوردن صفحه زمان- فرکانس هر قطعه، استخراج ویژگی‌ها از سیگنال‌ها انجام می‌شود. در مرحله دوم با استفاده از الگوریتم نزدیک‌ترین همسایه کار تشخیص حملات صورت می‌گیرد. اما قبل از اعمال الگوریتم طبقه‌بندی، برای اصلاح فضای ویژگی‌ها و یادگیری معیار فاصله، از الگوریتم AIS-RCAاستفاده شده است. این الگوریتم برای به‌دست آوردن ماتریس تبدیل W، داده‌ها را به صورت مجموعه‌ای از دسته‌ها در نظر می‌گیرد و با ارائه الگوریتم جدید AD-AIRSو با الهام گرفتن از سیستم ایمنی بدن دسته‌ها را می‌یابد. آزمایش‌های انجام شده نشان دهندة دقت 100% و بهبود نتایج در مقایسه با برخی روش‌های انتقال موجک، آنتروپی، معیار بی‌نظمی و تبدیل انتقال فوریه سریع را نشان می‌دهد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

AIS-RCA: An Efficient Feature Reduction Method to Improve the Seizure Detection Rate

نویسندگان [English]

  • Amin Zare 1
  • Reza Boostani 2
  • Mansour Zolghadr Jahromi 3
1 Instructor, Department of Computer Engineering, Shiraz Branch, Islamic Azad University
2 Associate Professor, Biomedical Engineering Group, Department of Computer Sciences and Engineering, Shiraz University
3 Associate Professor, Department of Computer Sciences and Engineering, Shiraz University
چکیده [English]

There is a growing interest to improve seizure prediction by online analyzing of electroencephalogram (EEG) signals in epileptic patients. Seizure attack is occurred infrequently and unpredictably; hence, automatic detection of seizure during long-term is highly recommended. In this paper a novel Feature Reduction method namely AIS-RCA which adopted from the immunity system is proposed to improve the seizure detection rate. The automatic seizure detection can be performed in two successive stages: 1) The feature extraction/selection stage from EEG signals and 2) classifying the feature vectors by an efficient classifier. In this study, first, pseudo-Wigner-Ville distribution was applied to each window of the EEG signals and then the extracted features were transformed by AIS-RCA transform to represent the features in a more separable space. The AIS-RCA transformation matrix is estimated by using chunklets (a chunklet is defined as a subset of points that are known to be same). AIS-RCA using the proposed Artificial Immune System algorithm named Adaptive Distance-AIRS to discover the chunklets in the data space. Finally KNN classifier was applied to the transformed features to classify the seizure and non-seizure windows. The experimental results show that the proposed method yields epileptic detection accuracy rate up to 99.9% which is better than the results achieved by other types of features such as FFT, Wavelet transform, entropy and chaotic measures.

کلیدواژه‌ها [English]

  • EEG signal
  • Time-Frequency Analysis
  • Spectrum
  • Epileptic seizures
  • AIS-RCA
  • AD-AIRS
  • RCA
  • Artificial Immune System (AIS)
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