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Photoplethysmography

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XGBoost Regression of the Most Significant Photoplethysmogram Features for Assessing Vascular Aging.

IEEE journal of biomedical and health informatics
The purpose of this study was to confirm the potential of XGBoost as a vascular aging assessment model based on the photoplethysmogram (PPG) features suggested in previous studies, and to explore the key PPG features for vascular aging assessment thr...

A new deep learning framework based on blood pressure range constraint for continuous cuffless BP estimation.

Neural networks : the official journal of the International Neural Network Society
Blood pressure (BP) is known as an indicator of human health status, and regular measurement is helpful for early detection of cardiovascular diseases. Traditional techniques for measuring BP are either invasive or cuff-based and thus are not suitabl...

Deduction learning for precise noninvasive measurements of blood glucose with a dozen rounds of data for model training.

Scientific reports
Personalized modeling has long been anticipated to approach precise noninvasive blood glucose measurements, but challenged by limited data for training personal model and its unavoidable outlier predictions. To overcome these long-standing problems, ...

Deep convolutional neural network-based signal quality assessment for photoplethysmogram.

Computers in biology and medicine
Quality assessment of bio-signals is important to prevent clinical misdiagnosis. With the introduction of mobile and wearable health care, it is becoming increasingly important to distinguish available signals from noise. The goal of this study was t...

Intelligent Bio-Impedance System for Personalized Continuous Blood Pressure Measurement.

Biosensors
Continuous blood pressure (BP) measurement is crucial for long-term cardiovascular monitoring, especially for prompt hypertension detection. However, most of the continuous BP measurements rely on the pulse transit time (PTT) from multiple-channel ph...

Normalization of photoplethysmography using deep neural networks for individual and group comparison.

Scientific reports
Photoplethysmography (PPG) is easy to measure and provides important parameters related to heart rate and arrhythmia. However, automated PPG methods have not been developed because of their susceptibility to motion artifacts and differences in wavefo...

Application of photoplethysmography signals for healthcare systems: An in-depth review.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Photoplethysmography (PPG) is a device that measures the amount of light absorbed by the blood vessel, blood, and tissues, which can, in turn, translate into various measurements such as the variation in blood flow volume, ...

Contactless facial video recording with deep learning models for the detection of atrial fibrillation.

Scientific reports
Atrial fibrillation (AF) is often asymptomatic and paroxysmal. Screening and monitoring are needed especially for people at high risk. This study sought to use camera-based remote photoplethysmography (rPPG) with a deep convolutional neural network (...

90% Accuracy for Photoplethysmography-Based Non-Invasive Blood Glucose Prediction by Deep Learning with Cohort Arrangement and Quarterly Measured HbA1c.

Sensors (Basel, Switzerland)
Previously published photoplethysmography-(PPG) based non-invasive blood glucose (NIBG) measurements have not yet been validated over 500 subjects. As illustrated in this work, we increased the number subjects recruited to 2538 and found that the pre...

Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda.

Sensors (Basel, Switzerland)
Heart rate (HR) is one of the essential vital signs used to indicate the physiological health of the human body. While traditional HR monitors usually require contact with skin, remote photoplethysmography (rPPG) enables contactless HR monitoring by ...