Photoplethysmography (PPG) is a technique widely used to monitor volumetric blood changes induced by cardiac pulsations. Pulse oximetry uses the technique of PPG to estimate arterial oxygen saturation values(S(p)O(2)). In poorly perfused tissues, S(p)O(2) readings may be compromised due to the poor quality of the PPG signals. We have developed a new multimode PPG measurement system which utilizes a reflectance PPG probe that operates in reflectance, transmittance and transreflectance mode simultaneously aiming to improve the quality of the PPG signals in cases of poor peripheral perfusion. In order to evaluate the performance of the probe, experiments were performed in healthy volunteers. A blood pressure cuff was used to induce systematic and controlled artificial hypoperfusion while PPG signals were recorded using all three modes. It was found that the amplitude of the transreflectance signal was significantly greater than the other two conventional PPG sensors at all occlusion pressures, suggesting the potential for improved signal acquisition in patients with peripheral hypoperfusion.

Download full-text PDF

Source
http://dx.doi.org/10.1109/IEMBS.2010.5627730DOI Listing

Publication Analysis

Top Keywords

ppg signals
12
ppg
8
quality ppg
8
evaluation multimode
4
multimode photoplethysmographic
4
photoplethysmographic sensor
4
sensor cuff-induced
4
cuff-induced hypoperfusion
4
hypoperfusion photoplethysmography
4
photoplethysmography ppg
4

Similar Publications

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 PDF

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 PDF

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 PDF

In 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 PDF

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).

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!