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
Improving the Performance of Unified Power Quality Conditioner Using Interval Type 2 Fuzzy Control
Farzad Rastegar - Zohreh Paydar
A Communication-Aware Scheduler for Containers in a Kubernetes Environment Using Girvan-Newman Clustering
Marzie Norouzi Dehnashi - Mahmoud Momtazpour - Seyyed Ahmad Javadi
Atrial Fibrillation (AF) Detection Using Deep Learning with GAN-based Data Augmentation
Amirhossein Akhoondkazemi - Arash Vashagh - Sayed Jalal Zahabi - Davood Shafie
تشخیص حضور انسان در خانه های هوشمند با استفاده از شبکه ی بی سیم محلی
امیرمحمد بصیرت - نغمه سادات مویدیان
حسگر ضریب شکست مبتنی بر فانو رزونانس در موجبرهای فلز- عایق- فلز، با رزوناتور صفحهای تزویج شده از جانب
تورج هاشمی - نسرین عبدالهی برازجان - عباس علی قنبری
Noninvasive Diagnosis of the Type of Breast Tumor through Artificial Neural Networks
Pooya Tahmasebi - Maryam Mehdizadeh Dastjerdi - Ali Fallah - Saeid Rashidi
A Hybrid Approach for Multimodal Biometric Recognition based on Feature Level Fusion in Reproducing Kernel Hilbert Space
Mohammad Hassan Safavipour - Mohammad Ali Doostari - Hamed Sadjedi
Design of a Three-Stage OTA with Wide Capacitive Load Range Using Dual-Path and Q-Factor Compensation
Mohammadreza Abedi Orang
Model Predictive Control for Optimal Drug Administration of Cancer Chemotherapy
Zahra Hosseinpour - Amirhossein Nikoofard - Erfan Nejabat
بررسی عملکرد تقویت کننده فیبری پالسی نانوثانیه اربیوم ایتربیوم با نرخ تکرار پایین
احسان حمیدنژاد - اصغر غلامی - محمدجواد حکمت
بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 41.7.4