AIMC Topic: Photoplethysmography

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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 Apr 22, 2022
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...
Blood Pressure Determination Blood Pressure Deep Learning Photoplethysmography Humans Algorithms
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Deduction learning for precise noninvasive measurements of blood glucose with a dozen rounds of data for model training.

Scientific reports Apr 20, 2022
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, ...
Humans Deep Learning Blood Glucose Photoplethysmography Neural Networks, Computer Algorithms
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Deep convolutional neural network-based signal quality assessment for photoplethysmogram.

Computers in biology and medicine Mar 22, 2022
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...
Photoplethysmography Bayes Theorem Heart Rate Neural Networks, Computer Big Data
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Intelligent Bio-Impedance System for Personalized Continuous Blood Pressure Measurement.

Biosensors Feb 28, 2022
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...
Artificial Intelligence Photoplethysmography Blood Pressure Determination Humans Blood Pressure Pulse Wave Analysis Electric Impedance
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Normalization of photoplethysmography using deep neural networks for individual and group comparison.

Scientific reports Feb 24, 2022
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...
Male Photoplethysmography Signal Processing, Computer-Assisted Female Adult Motion Heart Rate Humans Neural Networks, Computer
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Application of photoplethysmography signals for healthcare systems: An in-depth review.

Computer methods and programs in biomedicine Feb 1, 2022
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, ...
Machine Learning Delivery of Health Care Photoplethysmography Heart Rate Blood Pressure Signal Processing, Computer-Assisted
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Contactless facial video recording with deep learning models for the detection of atrial fibrillation.

Scientific reports Jan 7, 2022
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 (...
Male Middle Aged Pulsatile Flow Deep Learning Time Factors Diagnosis, Computer-Assisted Reproducibility of Results Aged Humans Aged, 80 and over Regional Blood Flow Prospective Studies Video Recording Electrocardiography Atrial Fibrillation Heart Rate Image Processing, Computer-Assisted Female Face Predictive Value of Tests Photoplethysmography Skin
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90% Accuracy for Photoplethysmography-Based Non-Invasive Blood Glucose Prediction by Deep Learning with Cohort Arrangement and Quarterly Measured HbA1c.

Sensors (Basel, Switzerland) Nov 24, 2021
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...
Humans Deep Learning Photoplethysmography Blood Glucose Glycated Hemoglobin Cohort Studies
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Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda.

Sensors (Basel, Switzerland) Sep 20, 2021
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 ...
Monitoring, Physiologic Deep Learning Humans Heart Rate Signal Processing, Computer-Assisted Photoplethysmography Algorithms
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Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning.

Sensors (Basel, Switzerland) Sep 8, 2021
Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP) measurement is interesting for various reasons. First, PPG can easily be measured using fingerclip sensors. Second, camera based approaches allow to derive remote PPG ...
Photoplethysmography Neural Networks, Computer Blood Pressure Blood Pressure Determination Deep Learning
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