AIMC Topic: Photoplethysmography
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Detecting anomalies in smart wearables for hypertension: a deep learning mechanism.
Frontiers in public health
Jan 15, 2025
INTRODUCTION: The growing demand for real-time, affordable, and accessible healthcare has underscored the need for advanced technologies that can provide timely health monitoring. One such area is predicting arterial blood pressure (BP) using non-inv...
Spiking-PhysFormer: Camera-based remote photoplethysmography with parallel spike-driven transformer.
Neural networks : the official journal of the International Neural Network Society
Jan 10, 2025
Artificial neural networks (ANNs) can help camera-based remote photoplethysmography (rPPG) in measuring cardiac activity and physiological signals from facial videos, such as pulse wave, heart rate and respiration rate with better accuracy. However, ...
rU-Net, Multi-Scale Feature Fusion and Transfer Learning: Unlocking the Potential of Cuffless Blood Pressure Monitoring With PPG and ECG.
IEEE journal of biomedical and health informatics
Jan 7, 2025
This study introduces an innovative deep-learning model for cuffless blood pressure estimation using PPG and ECG signals, demonstrating state-of-the-art performance on the largest clean dataset, PulseDB. The rU-Net architecture, a fusion of U-Net and...
Predicting Blood Pressures for Pregnant Women by PPG and Personalized Deep Learning.
IEEE journal of biomedical and health informatics
Jan 7, 2025
Blood pressure (BP) is predicted by this effort based on photoplethysmography (PPG) data to provide effective pre-warning of possible preeclampsia of pregnant women. Towards frequent BP measurement, a PPG sensor device is utilized in this study as a ...
Non-invasive blood glucose monitoring using PPG signals with various deep learning models and implementation using TinyML.
Scientific reports
Jan 2, 2025
Accurate and continuous blood glucose monitoring is essential for effective diabetes management, yet traditional finger pricking methods are often inconvenient and painful. To address this issue, photoplethysmography (PPG) presents a promising non-in...
Real-Time PPG-Based Biometric Identification: Advancing Security with 2D Gram Matrices and Deep Learning Models.
Sensors (Basel, Switzerland)
Dec 25, 2024
The integration of liveness detection into biometric systems is crucial for countering spoofing attacks and enhancing security. This study investigates the efficacy of photoplethysmography (PPG) signals, which offer distinct advantages over tradition...
Blood Pressure Estimation Using Explainable Deep-Learning Models Based on Photoplethysmography.
Anesthesia and analgesia
Dec 16, 2024
BACKGROUND: Due to their invasiveness, arterial lines are not typically used in routine monitoring, despite their superior responsiveness in hemodynamic monitoring and detecting intraoperative hypotension. To address this issue, noninvasive, continuo...
Enhancing artificial intelligence-driven sleep apnea diagnosis: The critical importance of input signal proficiency with a focus on mandibular jaw movements.
Journal of prosthodontics : official journal of the American College of Prosthodontists
Dec 15, 2024
PURPOSE: This review aims to highlight the pivotal role of the mandibular jaw movement (MJM) signal in advancing artificial intelligence (AI)-powered technologies for diagnosing obstructive sleep apnea (OSA).
Photoplethysmography as a noninvasive surrogate for microneurography in measuring stress-induced sympathetic nervous activation - A machine learning approach.
Computers in biology and medicine
Dec 12, 2024
The sympathetic nervous system (SNS) is essential for the body's immediate response to stress, initiating physiological changes that can be measured through sympathetic nerve activity (SNA). While microneurography (MNG) is the gold standard for direc...
Robust modelling of arterial blood pressure reconstruction from photoplethysmography.
Scientific reports
Dec 5, 2024
Blood pressure is a crucial indicator of cardiovascular disease, and arterial blood pressure (ABP) waveforms contain information that reflects the cardiovascular status. We propose a novel deep-learning method that converts photoplethysmogram (PPG) s...
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