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مدل‌سازی آشوب‌گرای سیگنال فوتوپلتیسموگرام جهت تخمین فشارخون مبتنی بر شبکه‌های عصبی عمیق کانولوشنی

نویسندگان

دانشگاه صنعتی همدان، همدان، ایران

چکیده

تخمین فشار خون به صورت غیرتهاجمی از طریق پردازش سیگنال‌های محیطی شامل سیگنال‌های قلبی و فوتوپلتیسموگرام از مسائل مورد توجه در حوزه پردازش سیگنال‌های پزشکی است. عمده مطالعات بر استخراج ویژگی‌های متنوع از سیگنال‌های محیطی و تخمین فشار خون به وسیله مدل‌های مبتنی بر هوش محاسباتی متمرکز هستند. در این مقاله یک مدل شبکه عصبی عمیق کانولوشنی پیشنهاد شده که با به کارگیری شکل موج سیگنال فوتوپلتیسموگرام و ویژگی‌های آشوبگرای آن می‌تواند فشار خون را با دقت بالایی تخمین زند. ویژگی آشوب‌گرای سیگنال در فضای فاز با نام آنالیز کمی بازرخداد به همراه نمونه‌های سری زمانی فوتوپلتیسموگرام به عنوان ورودی مدل شبکه عصبی عمیق کانولوشنی در نظر گرفته شده‌اند. نتایج آزمون مدل پیشنهادی بر روی دادگان استخراجی از مجموعه داده MIMIC-II بر اساس استانداردهای انجمن فشار خون بریتانیا (BHS) و انجمن توسعه ابزار پزشکی (AAMI) نشان می‌دهد که این ترکیب در ورودی می‌تواند دقت بالایی در تخمین فشار خون فراهم کند. به طور خاص در تخمین فشار خون دیاستول و سیستول، معیار ضریب همبستگی پیرسون (R) برای روش پیشنهادی به ترتیب 93916/0 و 93357/0 به دست آمد. مطابق استاندارد BHS این روش حائز درجه کیفی A در تخمین فشارخون است و دارای میانگین و انحراف معیار خطای تخمین برای فشار خون دیاستول و سیستول به ترتیب برابر 5/3±45/0 و 69/6±48/0 مطابق نیازمندی‌های استاندارد AAMI می‌باشد.

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علوم رایانش و فناوری اطلاعات
دوره 19، شماره 1
بهار و تابستان
اردیبهشت 1400