AIMC Topic: Photoplethysmography

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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 (...
Humans Prospective Studies Predictive Value of Tests Skin Regional Blood Flow Photoplethysmography Electrocardiography Middle Aged Video Recording Diagnosis, Computer-Assisted Face Pulsatile Flow Time Factors Aged Female Aged, 80 and over Image Processing, Computer-Assisted Reproducibility of Results Deep Learning Male Atrial Fibrillation Heart Rate
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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 Blood Glucose Glycated Hemoglobin Cohort Studies Photoplethysmography
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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 ...
Signal Processing, Computer-Assisted Heart Rate Deep Learning Photoplethysmography Monitoring, Physiologic Humans 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 ...
Blood Pressure Determination Blood Pressure Deep Learning Photoplethysmography Neural Networks, Computer
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Combined deep CNN-LSTM network-based multitasking learning architecture for noninvasive continuous blood pressure estimation using difference in ECG-PPG features.

Scientific reports Jun 29, 2021
The pulse arrival time (PAT), the difference between the R-peak time of electrocardiogram (ECG) signal and the systolic peak of photoplethysmography (PPG) signal, is an indicator that enables noninvasive and continuous blood pressure estimation. Howe...
Humans Databases, Factual Photoplethysmography Machine Learning Heart Rate Models, Cardiovascular Blood Pressure Determination Blood Pressure
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Deep learning-based photoplethysmography classification for peripheral arterial disease detection: a proof-of-concept study.

Physiological measurement Jun 17, 2021
A proof-of-concept study to assess the potential of a deep learning (DL) based photoplethysmography PPG ('DLPPG') classification method to detect peripheral arterial disease (PAD) using toe PPG signals.PPG spectrogram images derived from our previous...
Deep Learning Photoplethysmography Heart Rate Leg Peripheral Arterial Disease Humans
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Classification of Mental Stress Using CNN-LSTM Algorithms with Electrocardiogram Signals.

Journal of healthcare engineering Jun 4, 2021
The mental stress faced by many people in modern society is a factor that causes various chronic diseases, such as depression, cancer, and cardiovascular disease, according to stress accumulation. Therefore, it is very important to regularly manage a...
Electrocardiography Electromyography Algorithms Photoplethysmography Humans Neural Networks, Computer
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A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods.

Sensors (Basel, Switzerland) May 27, 2021
The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning metho...
Heart Rate Photoplethysmography Algorithms Signal Processing, Computer-Assisted Deep Learning Heart
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Using CNN and HHT to Predict Blood Pressure Level Based on Photoplethysmography and Its Derivatives.

Biosensors Apr 13, 2021
According to the WTO, there were 1.13 billion hypertension patients worldwide in 2015. The WTO encouraged people to check the blood pressure regularly because a large amount of patients do not have any symptoms. However, traditional cuff measurement ...
Hypertension Photoplethysmography Humans Neural Networks, Computer Algorithms Blood Pressure Atherosclerosis Vital Signs
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Determining respiratory rate from photoplethysmogram and electrocardiogram signals using respiratory quality indices and neural networks.

PloS one Apr 8, 2021
Continuous and non-invasive respiratory rate (RR) monitoring would significantly improve patient outcomes. Currently, RR is under-recorded in clinical environments and is often measured by manually counting breaths. In this work, we investigate the u...
Electrocardiography Neural Networks, Computer Respiratory Rate Photoplethysmography Humans
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