0% Complete
English
صفحه اصلی
/
سی و دومین کنفرانس ملی و دهمین کنفرانس بین المللی مهندسی زیست پزشکی ایران
Robust Glucose Level Classification from NIR-Based PPG Using Morphological Features
نویسندگان :
Arian Mesforoosh Mashhad
1
Yeganeh Binafar
2
Mohammad Reza Akbarzadeh Totonchi
3
1- دانشگاه فردوسی مشهد
2- دانشگاه فردوسی مشهد
3- دانشگاه فردوسی مشهد
کلمات کلیدی :
Diabetes classification،Photoplethysmography (PPG)،Near-infrared spectroscopy (NIRS)،Biomedical signal processing،Morphological features،Machine learning
چکیده :
Diabetes is a primary global health concern, and noninvasive monitoring could be critical for its early detection and management. This study presents a noninvasive approach to blood glucose classification using photoplethysmography (PPG) signal and machine learning approaches. However, PPG signals are biological signals that, similar to their counterparts, suffer from considerable environmental noise and patient-to-patient variability. Here, we propose a morphology-based framework for robust PPG-based Glucose classification. For this purpose, a custom-designed optical finger sensor operating at 940 nm was used to record two independent 30 s signals from fasting participants, including both healthy and diabetic subjects. After excluding low-quality signals, the final dataset included 159 subjects. Signals also underwent multi-stage filtering, normalization, and cycle-based template-matching quality control before feature extraction. We then employed the proposed framework to identify consistent cycle-shape patterns within each acquisition and verify their stability across repeated recordings. Two feature sets were compared including the cycle-based morphological and global signal-based features. Correlation analysis showed that morphology-based features were more robust and reproducible, while global signal features were less reliable under short-duration acquisitions. Multiple classifiers were tested, with Gradient Boosting achieving the highest accuracy (93.75%) using morphological features, compared to 84.38% with non-morphological features. These findings suggest that morphology-based signal analysis provides robust and salient features from short PPG signals, enabling practical and accurate noninvasive diabetes screening.
لیست مقالات
لیست مقالات بایگانی شده
یک سامانه هوشمند پشتیبان تصمیم مبتنی بر چندعامل برای طبقهبندی انواع کسبوکار
حسن ضیافت
Effective Connectivity Alterations within the Cortico–Basal Ganglia Circuit Associated with Motor Skill Learning
Mohammad Rezaei - Alireza Talesh Jafadideh - Fariba Bahrami - Shahzad Tahmasebi Boroujeni
Physics-Informed Neural Networks for Cardiac Flow Estimation in 2D Simplified Human Right Ventricular Geometry
Mohammadmahdi Sekhavatpisheh - Nasser Fatouraee
چالشهای اخلاقی هوش مصنوعی در حسابداری مدیریت
محمدرضا پورعلی لاکلایه - مصطفی لطفی
A Comparative Analysis of Simulated and Experimental Acoustic and Thermal Behavior of HIFU
Maryam Fazeli - Remi Souchon - Cyril Lafon - Mehran Jahed
راهکارهای عملی برای اجرای موفق پروژههای هوش مصنوعی در ایران
ملینا عبدلی
Mechanical properties of cancer cells as potential predictive biomarkers
Sayed Reza Ramezani - Afsaneh Mojra
تاثیر استفاده از هوش مصنوعی بر فرآیند مدیریت مشتری(CRM) و رشد کسب و کار در صنعت بیمه
مسعود سبزچی دهخوارقانی - میترا زابلی پیله رود
Hierarchical Task-Structured GNN Meta-Learning for Few-Shot EEG Motor Imagery Decoding
Mohammad Armin Dehghan - Mohammad Mohammadianbisheh - Mohammad Bagher Shamsollahi
تحلیل رنگ بافت عضلانی و چربی گاو با روشهای مبتنی بر بینایی ماشین: یک بررسی جامع
فاطمه بناءهمزایی - مصطفی حشمتی
بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 42.5.2