AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Photoplethysmography

Showing 91 to 100 of 175 articles

Clear Filters

Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning.

Sensors (Basel, Switzerland)
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 ...

Combined deep CNN-LSTM network-based multitasking learning architecture for noninvasive continuous blood pressure estimation using difference in ECG-PPG features.

Scientific reports
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...

Deep learning-based photoplethysmography classification for peripheral arterial disease detection: a proof-of-concept study.

Physiological measurement
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...

Classification of Mental Stress Using CNN-LSTM Algorithms with Electrocardiogram Signals.

Journal of healthcare engineering
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...

A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods.

Sensors (Basel, Switzerland)
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...

Using CNN and HHT to Predict Blood Pressure Level Based on Photoplethysmography and Its Derivatives.

Biosensors
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 ...

Determining respiratory rate from photoplethysmogram and electrocardiogram signals using respiratory quality indices and neural networks.

PloS one
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...

Blood Pressure Morphology Assessment from Photoplethysmogram and Demographic Information Using Deep Learning with Attention Mechanism.

Sensors (Basel, Switzerland)
Arterial blood pressure (ABP) is an important vital sign from which it can be extracted valuable information about the subject's health. After studying its morphology it is possible to diagnose cardiovascular diseases such as hypertension, so ABP rou...

Novel Analgesic Index for Postoperative Pain Assessment Based on a Photoplethysmographic Spectrogram and Convolutional Neural Network: Observational Study.

Journal of medical Internet research
BACKGROUND: Although commercially available analgesic indices based on biosignal processing have been used to quantify nociception during general anesthesia, their performance is low in conscious patients. Therefore, there is a need to develop a new ...

On-Device Reliability Assessment and Prediction of Missing Photoplethysmographic Data Using Deep Neural Networks.

IEEE transactions on biomedical circuits and systems
Photoplethysmographic (PPG) measurements from ambulatory subjects may suffer from unreliability due to body movements and missing data segments due to loosening of sensor. This paper describes an on-device reliability assessment from PPG measurements...