شناسایی بیماران نقص توجه-بیش فعال با استفاده از ویژگی‌های برمبنای موجک سیگنال‌های EEG

محورهای موضوعی : انرژی های تجدیدپذیر

سحر کریمی شهرکی ¹ , مهدی خضری ²

1 - دانشکده مهندسی برق- واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران.
2 - مرکز تحقیقات پردازش دیجیتال و بینایی ماشین- واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف‌آباد، ایران

تاریخ دریافت : 1399/06/02 تاریخ پذیرش : 1399/09/28 تاریخ انتشار : 1400/09/01

کلید واژه: تبدیل موجک, ویژگی های زمانی, اختلال توجه-بیش فعالی, طبقه‌بندی ماشین بردار پشتیبان, طبقه‌بندی k نزدیکترین همسایه,

چکیده مقاله :

اختلال توجه-بیش فعالی (ADHD)، نوعی بیماری روانی توسعه عصبی است که باعث عدم توجه، اضطراب، بیش فعالی و رفتارهای تکانشگری فرد می‌شود. این بیماری بیشتر درکودکان دیده می‌شود و به‌طور مستقیم منجر به ناتوانی آن‌ها در یادگیری می‌شود. هدف این مطالعه، ارایه سیستمی به‌منظور شناسایی دقیق‌تر بیماران ADHD با استفاده از ویژگی‌های برمبنای موجک سیگنال‌های مغزی (EEG) است. سیگنال‌های EEG ثبت شده از 61 کودک ADHD (شناسایی شده بر مبنای معیار DSM-IV) و60 کودک سالم به عنوان گروه کنترل در محدوده سنی 7-12 سال برای طراحی سیستم مورد استفاده قرارگرفتند. در روش پیشنهادی، سیگنال‌های EEG با اعمال تبدیل موجک به زیرباندهایی تجزیه شدند؛ و برای نسخه زمانی سیگنال‌ها در هر زیرباند، ویژگی‌های زمانی و آماری محاسبه شدند. مجموعه ویژگی کاهش یافته با روش تحلیل مولفه اصلی (PCA) سپس برای آموزش واحد طبقه‌بندی به منظور شناسایی بیماران ADHD از افراد سالم به‌کار رفت. برای کسب نتایج مطلوب، انواع مختلف توابع موجک و سطوح تجزیه مورد بررسی قرارگرفتند. تابع موجک bior3.1با روش طبقه‌بندی ماشین بردار پشتیبان (SVM)و تابع موجک rbio1.1 با روش طبقه‌بندی k نزدیکترین همسایه (kNN) با کسب دقت‌های شناسایی به‌ترتیب 98.33 و 99.17 درصد، بهترین عملکرد را ارایه کردند. روش طبقه‌بندی SVM با تابع کرنل پایه شعاعی (RBF) و روش kNN با تعداد همسایگی k=3 بهترین نتایج را کسب کردند. نتایج به‌دست آمده در این مطالعه، در مقایسه با نتایج گزارش شده در مطالعات قبلی حداقل 2 درصد بهبود در دقت شناسایی بیماران ADHD را نشان دادند.

چکیده انگلیسی:

Attention Deficit Hyperactivity Disorder (ADHD) is a neurological and psychiatric disorder which causes to attention deficit, anxiety, hyperactivity and impulsive behaviors. ADHD is more common in children and directly leads to their learning disability. The aim of this study was to accurately identify ADHD patients by using wavelet-based features of brain signals (EEG). Recorded EEG signals from 61 children with ADHD (diagnosed according to the DSM-IV criteria) and 60 healthy controls in the age range of 7-12 years were used to design the system. In the proposed method by applying wavelet transform, EEG signals were decomposed into subbands; for the time version of the signals in each subband, the temporal and statistical features were calculated. The reduced feature set by principal component analysis (PCA) method was then used to train the classification unit to identify ADHD patients from healthy individuals. To obtain the desired results, different types of wavelet functions and decomposition levels were investigated. The bior3.1 wavelet function with the support vector machine (SVM) classifier and the rbio1.1 wavelet function with the k-nearest neighbor (kNN) classifier presented the best performance with the recognition accuracy of 98.33% and 99.17%, respectively. The SVM classification method with radial basis kernel function (RBF) and the kNN method with the number of nearest neighbors, k = 3 obtained the best results.The results obtained in this study compared to the results reported in previous studies showed at least a 2% improvement in the recognition accuracy of ADHD patients.

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