تولید سیگنال فوتوپلتیسموگرام (PPG) مصنوعی با استفاده از مدل سازنده مبتنی بر برنامه‌نویسی ژنتیک
محورهای موضوعی : هوش مصنوعی و رباتیکفاطمه قاسمی 1 , فردین ابدالی محمدی 2 *
1 - گروه مهندسی کامپیوتر و فناوری اطلاعات، دانشکده فنی و مهندسی، دانشگاه رازی، کرمانشاه، ایران
2 - هیات علمی
کلید واژه: فوتوپلتیسموگرام, مدل سازنده, برنامه‌نویسی ژنتیک, مقیاس‌پذیری, مدل ریاضی,
چکیده مقاله :
امروزه تحولات فناوری اطلاعات و ارتباطات در حوزه سلامت، به‌ویژه در نظارت بر فعالیت‌های قلبی، به افزایش استفاده از تکنولوژی فوتوپلتیسموگرام (PPG: Photoplethysmogram) در دستگاه‌های هوشمند و تلفن‌های همراه منجر شده است. توسعۀ مدل‌های سازنده به جهت تولید سیگنال‌های مصنوعی PPG نیازمند حل چالش‌هایی مانند کمبود تنوع و محدودیت داده‌ها در آموزش مدل‌های یادگیری عمیق است. این مقاله، یک رویکرد مبتنی بر برنامه‌نویسی ژنتیک (GP: Genetic Programming) را به کار می‌گیرد تا مدل سازنده‌ای را ارائه دهد که با کمک یک نمونۀ اولیه از سیگنال PPG، قادر به تولید داده‌هایی با تنوع بیشتر و دقت افزوده‌شده باشد. برخلاف رگرسیون معمول، در برنامه‌نویسی ژنتیک ساختار و ترکیبات مدل ریاضی به‌صورت خودکار تعیین میگردد. رویکرد پیشنهادی، با داشتن اندازه خطای میانگین (MSE: Mean Squared Error) برابر با 0.0001، ریشه میانگین مربعات خطا (RMSE: Root Mean Squared Error) به‌اندازه 0.01 و همبستگی 0.999 نشان می‌دهد که به دلیل بهینگی مناسب و دقت قابل‌قبول در تولید داده‌های PPG مصنوعی، نسبت به دیگر روش‌ها برتری دارد و ازنظر کارایی و قابلیت اجرا در محیط‌های با منابع محدود نیز مؤثر عمل می کند.
Today, advancements in Information and Communication Technology (ICT), particularly in healthcare and cardiac activity monitoring, have led to increased adoption of Photoplethysmogram (PPG) technology in smart devices and mobile phones. The development of generative models for producing artificial PPG signals faces challenges such as a lack of diversity and constraints in training data. This article employs a Genetic Programming (GP) based approach to introduce a generative model capable of producing PPG data with increased diversity and enhanced accuracy using an initial sample of PPG signals. In contrast to conventional regression, Genetic Programming automates the determination of the mathematical model's structure and compositions. The proposed approach, with a Mean Squared Error (MSE) of 0.0001, Root Mean Square Error (RMSE) of 0.01, and a correlation of 0.999, demonstrates superior performance due to appropriate optimization and acceptable accuracy in generating synthetic PPG data. It outperforms other methods in terms of efficiency and execution capability, especially in resource-constrained environments.
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