شناسایی تشنج صرعی بر پایه‌ی آمارگان نقشه تبدیل موجک و روش‌ EMD برای آنالیز طیفی هیلبرت - هوانگ در باند فرکانسی گاما سیگنال‌هایEEG

محورهای موضوعی : پردازش سیگنالهای پزشکی

مرتضی به نام ¹ , حسین پورقاسم ²

1 - دانشگاه آزاد اسلامی واحد نجف آباد
2 - دانشگاه آزاد اسلامی واحد نجف آباد

تاریخ دریافت : 1394/03/30 تاریخ پذیرش : 1394/05/08 تاریخ انتشار : 1394/06/10

کلید واژه: تبدیل موجک, بیماری صرع, تبدیل هیلبرت – هوانگ, ریتم‌های مغزی, طبقه‌بند K- نزدیک‌ترین همسایه (KNN),

چکیده مقاله :

تشخیص بیماری تشنج با استفاده از آنالیز سیگنال‌های مغزی (EEG) از جمله روش‌های کلینیکی کارآمد در درمان دارویی و تصمیمات پیش از جراحی مغزی می‌باشد. در این مقاله، پس از آماده‌سازی سیگنال‌ها با استفاده از یک فیلترینگ مناسب، باند فرکانسی گاما استخراج شده است و سایر ریتم‌های مغزی، مقادیر نویز محیطی و سیگنال‌های حیاتی دیگر حذف می‌شوند. سپس، تبدیل موجک سیگنال‌های مغزی و نقشه موزائیکی تبدیل موجک در چند سطح محاسبه می‌شود. با تقسیم مناسب نقشه‌ی رنگی به بخش‌بندی‌های مختلف، هیستوگرام هر زیر- تصویر محاسبه شده و آمارگان آن بر پایه‌ی مقدار ممان‌های آماری و آنتروپی منفی محاسبه می‌شود. بردار ویژگی آماری با استفاده از تحلیل مولفه‌های اصلی (PCA) به یک بعد کاهش می‌یابد. با استفاده از الگوریتم EMD و پروسه غربالگری در تحلیل داده‌ها به وسیله‌ی توابع حالت ذاتی (IMF) و مقدار مانده‌ی سیگنال‌ها و با استفاده از طیف تبدیل هیلبرت و تشکیل طیف هیلبرت – هوانگ یک ویژگی مکانی بر پایه‌ی فاصله اقلیدسی برای طبقه‌بندی سیگنال‌های مغزی محاسبه می‌شود. بوسیله‌ی طبقه‌بند K- نزدیک‌ترین همسایه (KNN) و با در نظر گرفتن پارامتر همسایگی بهینه، سیگنال‌های مغزی به دو کلاس دارای تشنج و سیگنال‌های سالم با میزان صحت 54/76% و واریانس خطای 3685/0 در آزمایش‌های مختلف طبقه‌بندی می‌شوند.

چکیده انگلیسی:

Seizure detection using brain signal (EEG) analysis is the important clinical methods in drug therapy and the decisions before brain surgery. In this paper, after signal conditioning using suitable filtering, the Gamma frequency band has been extracted and the other brain rhythms, ambient noises and the other bio-signal are canceled. Then, the wavelet transform of brain signal and the map of wavelet transform in multi levels are computed. By dividing the color map to different epochs, the histogram of each sub-image is obtained and the statistics of it based on statistical momentums and Negentropy values are calculated. Statistical feature vector using Principle Component Analysis (PCA) is reduced to one dimension. By EMD algorithm and sifting procedure for analyzing the data by Intrinsic Mode Function (IMF) and computing the residues of brain signal using spectrum of Hilbert transform and Hilbert – Huang spectrum forming, one spatial feature based on the Euclidian distance for signal classification is obtained. By K-Nearest Neighbor (KNN) classifier and by considering the optimal neighbor parameter, EEG signals are classified in two classes, seizure and non-seizure signal, with the rate of accuracy 76.54% and with variance of error 0.3685 in the different tests.          

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