0% Complete
صفحه اصلی
/
سی و دومین کنفرانس بین المللی مهندسی برق
Noninvasive Blood Pressure Classification Based on Photoplethysmography Using Machine Learning Techniques
نویسندگان :
Hanieh Mohammadi
1
Bahram Tarvirdizadeh
2
Khalil Alipour
3
Mohammad Ghamari
4
1- University of Tehran
2- University of Tehran
3- University of Tehran
4- Kettering University
کلمات کلیدی :
blood pressure،photoplethysmograph،feature extraction،feature selection،machine learning
چکیده :
Blood pressure (BP) is one of the four vital signs that offer crucial medical insights into cardiovascular activity. High BP is associated with an increased risk of diseases such as heart attacks and strokes. Traditional BP measurement methods, including invasive and cuff-based devices, have limitations in providing continuous monitoring and can be uncomfortable for individuals. In contrast, wearable devices offer a promising solution for ambulatory care and public health monitoring by enabling frequent BP measurements in non-clinical environments. To meet this requirement, we propose an approach for cuff-less and continuous BP classification using photoplethysmograph (PPG) signals and machine learning (ML) techniques. PPG is a light-based method used to detect variations in blood volume with each heartbeat, offering a noninvasive approach for evaluation. This technology is cost-effective, accessible, and allows for continuous usage. In this research, PPG signals collected from various individuals were subjected to preprocessing and feature extraction. To enhance the performance of ML algorithms and address concerns related to computational complexity and overfitting, feature selection techniques (three techniques) were implemented. These techniques aimed to strategically choose relevant features and subsequently train and evaluate the ML algorithms (eight algorithms) using these selected features. The output of the algorithms is in the form of four classes: normotension (NT), prehypertension (PHT), stage 1 hypertension (S1HT), and stage 2 hypertension (S2HT). The light gradient boosting machine (LightGBM) algorithm, combined with the SelectFromModel feature selection technique, achieved the highest performance, boasting an accuracy of 84.61% on the test data.
لیست مقالات
لیست مقالات بایگانی شده
Modulation Classification with Convolutional Neural Network based Deep Learning in Elastic Optical Network
Ehsan Varasteh - Seyed Sadra Kashef - Morteza Valizadeh - Mehdi Ranjbar Zefreh
Forged Channel: A Breakthrough Approach for Accurate Parkinson's Disease Classification using Leave-One-Subject-Out Cross-Validation
SeyedAmirReza Hamidi - Kamal Mohamed-Pour - Mohammad Yousefi
بررسی تاثیر کنترل کنندههای سیستم انتقال جریان مستقیم مبتنی بر مبدلهای منبع ولتاژ با اجزای شبکه قدرت با استفاده از روش تحلیل مدال خطی
علی ضیائی - رضا قاضی - روح الامین زینلی داورانی
کدینگ فیبوناچی جهش یافته: ارائه یک روش برای افزایش قابلیت اطمینان در شبکههای روی تراشه سهبعدی
مجتبی فرمانی - سروین ناظر جعفری - زهرا شیرمحمدی
A Compact Microstrip Combline Filter for Microwave S-band
Sina Rezaee - Mohammad Memarian
Broadband Two Layers 1-Bit Metal-Only Transmitarray with Polarization Conversion Technique
Majid Karimipour - Iman Aryanian
طراحی و شبیه سازی یک تقویت کننده کم نویز پهن باند در باند K (18 تا 27 گیگاهرتز)
نوید نصیری - حسین شمسی
Optimal Operation of Lithium-Ion Batteries Considering Degradation Cost in Vehicle-to-Grid Systems
Mahdi Esfandiari - Amin Rafrafi - Abolfazl Pirayesh
Entanglement Witness Derived By Using Kolmogorov-Arnold Networks
Fatemeh Lajevardi - Azam Mani - Ali Fahim
بهره برداری از ESS ها در بخش DC ترانسفوماتور حالت جامد به منظور بهبود کیفیت توان شبکه برق
یوسف عطائی - رضا قندهاری - مهدی بابائی - بهنام بهارلوئی
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 40.4.2