Photoplethysmographic (PPG) signals were recorded from the fingers of 16 healthy volunteers with periods of timed and forced respiration. The aim of this pilot study was to compare estimations of arterial oxygen saturation (SpO2) recorded using a dedicated pulse oximetry system while subjects were breathing regularly with and without a mouthpiece containing a flow resistor. The experiments were designed to mimic the effects of mechanical ventilation in anesthetized patients. The effect of estimated airway pressures of ± 15 cmH2O caused observable modulation in the recorded red and PPG signals. SpO2 values were calculated from the pre-recorded PPG signals. Mean SpO2 values were 95.4% with the flow resistor compared with 97.3% with no artificial resistance, with statistical significance demonstrated using a Student's t-test (P = 0.006).
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http://dx.doi.org/10.1109/EMBC.2013.6610406 | DOI Listing |
Sci Rep
January 2025
Department of Electrical Engineering, California Polytechnic State University, San Luis Obispo, California, USA.
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-invasive alternative for estimating blood glucose levels. In this study, we propose an innovative 1-second signal segmentation method and evaluate the performance of three advanced deep learning models using a novel dataset to estimate blood glucose levels from PPG signals.
View Article and Find Full Text PDFJ Med Eng Technol
December 2024
Department of Computer Engineering and Information Technology, Razi University, Kermanshah, Iran.
Nowadays, photoplethysmograph (PPG) technology is being used more often in smart devices and mobile phones due to advancements in information and communication technology in the health field, particularly in monitoring cardiac activities. Developing generative models to generate synthetic PPG signals requires overcoming challenges like data diversity and limited data available for training deep learning models. This paper proposes a generative model by adopting a genetic programming (GP) approach to generate increasingly diversified and accurate data using an initial PPG signal sample.
View Article and Find Full Text PDFJ Vasc Access
December 2024
Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan.
Introduction: Vascular access (VA) is essential for patients with hemodialysis, and its dysfunction is a major complication that can reduce quality of life or even threaten life. VA patency is not only difficult to predict on an individual basis, but also challenging to predict in real-time. To overcome this challenge, this study aimed to develop a machine learning approach to predict 6-month primary patency (PP) using photoplethysmography (PPG) signals acquired from the tips of both index fingers.
View Article and Find Full Text PDFIn the early stages of atrial fibrillation (AF), most cases are paroxysmal (pAF), making identification only possible with continuous and prolonged monitoring. With the advent of wearables, smartwatches equipped with photoplethysmographic (PPG) sensors are an ideal approach for continuous monitoring of pAF. There have been numerous studies demonstrating successful capture of pAF events, especially using deep learning.
View Article and Find Full Text PDFPhysiol Meas
December 2024
University of Glasgow James Watt School of Engineering, James Watt School of Engineering, Glasgow, Glasgow, G12 8QQ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND.
Objective: We study the changes in morphology of the photoplethysmography (PPG) signals-acquired from a select group of South Asian origin-through a low-cost PPG sensor, and correlate it with healthy aging which allows us to reliably estimate the vascular age and chronological age of a healthy person as well as the age group he/she belongs to.
Methods: Raw infrared PPG data is collected from the finger-tip of 173 appar- ently healthy subjects, aged 3-61 years, via a non-invasive low- cost MAX30102 PPG sensor. In addition, the following metadata is recorded for each subject: age, gender, height, weight, family history of cardiac disease, smoking history, vitals (heart rate and SpO2).
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