شناسایی صرع بر اساس بهینه‌سازی ویژگی‌های ادغامی تبدیل هارتلی با مدل ترکیبی MLP و GA همراه با استراتژی یادگیری ممتیک

نویسندگان

1 فارغ التحصیل کارشناسی ارشد

2 عضو هیئت علمی دانشگاه آزاد اسلامی نجف آباد

چکیده

چکیده: یکی از مسائل مهم در پردازش سیگنال‌های EEG، تشخیص حمله صرع است. در این مقاله، یک الگوریتم تشخیص و طبقه‌بندی سیگنال‌های مغزی حاوی حمله صرع از سیگنال‌های بدون حمله بر اساس ادغام ویژگی‌های تبدیل هارتلی پیشنهاد شده است. در این الگوریتم، علاوه بر استخراج ویژگی‌های معمول زمانی و فرکانسی مانند آنتروپی طیفی و چگالی طیف توان، یک ویژگی جدید مبتنی بر ادغام ویژگی‌های مستخرج از تبدیل هارتلی تعریف می‌شود. برای تعریف این ویژگی جدید، ویژگی‌های مستخرج از تبدیل هارتلی بر اساس یک سناریوی ماتریس کرنلی ادغام می‌شوند. جهت بهینه کردن و کاهش ابعاد بردار ویژگی مستخرج از سیگنال‌های مغزی، از یک مدل ترکیبی الگوریتم ژنتیک تحت استراتژی آموزش ممتیک و شبکه عصبی چندلایه پس‌انتشار خطا استفاده می‌شود. طبقه‌بندی نهایی بر روی این ویژگی‌های بهینه‌شده توسط یک شبکه عصبی پرسپترون با یک لایه پنهان انجام می‌شود و به‌طور میانگین صحت 325/95% را در طبقه‌بندی سیگنال‌های صرعی فراهم می‌کند.
 

کلیدواژه‌ها


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