AIMC Topic: Photoplethysmography

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Integrating Remote Photoplethysmography and Machine Learning on Multimodal Dataset for Noninvasive Heart Rate Monitoring.

Sensors (Basel, Switzerland) Nov 26, 2024
Non-contact heart monitoring is crucial in advancing telemedicine, fitness tracking, and mass screening. Remote photoplethysmography (rPPG) is a non-contact technique to obtain information about heart pulse by analyzing the changes in the light inten...
Photoplethysmography Body Mass Index Support Vector Machine Adult Male Middle Aged Monitoring, Physiologic Telemedicine Heart Rate Humans Machine Learning Algorithms Female Young Adult
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A non-invasive heart rate prediction method using a convolutional approach.

Medical & biological engineering & computing Nov 15, 2024
The research focuses on leveraging convolutional neural networks (CNNs) to enhance the analysis of physiological signals, specifically photoplethysmogram (PPG) data which is a valuable tool for non-invasive heart rate prediction. Recognizing the glob...
Signal Processing, Computer-Assisted Heart Rate Algorithms Male Photoplethysmography Humans Neural Networks, Computer
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MIMIC-BP: A curated dataset for blood pressure estimation.

Scientific data Nov 15, 2024
Blood pressure (BP) is one of the most prominent indicators of potential cardiovascular disorders. Traditionally, BP measurement relies on inflatable cuffs, which is inconvenient and limit the acquisition of such important health-related information ...
Photoplethysmography Electrocardiography Blood Pressure Determination Blood Pressure Humans Male Deep Learning
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Machine Learning Applied to Reference Signal-Less Detection of Motion Artifacts in Photoplethysmographic Signals: A Review.

Sensors (Basel, Switzerland) Nov 9, 2024
Machine learning algorithms have brought remarkable advancements in detecting motion artifacts (MAs) from the photoplethysmogram (PPG) with no measured or synthetic reference data. However, no study has provided a synthesis of these methods, let alon...
Humans Machine Learning Algorithms Photoplethysmography Signal Processing, Computer-Assisted Motion Artifacts
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GloGen: PPG prompts for few-shot transfer learning in blood pressure estimation.

Computers in biology and medicine Oct 8, 2024
With the rapid advancements in machine learning, its applications in the medical field have garnered increasing interest, particularly in non-invasive health monitoring methods. Blood pressure (BP) estimation using Photoplethysmogram (PPG) signals pr...
Humans Photoplethysmography Blood Pressure Determination Blood Pressure Signal Processing, Computer-Assisted Machine Learning Female Male
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Highly Sensitive Perovskite Photoplethysmography Sensor for Blood Glucose Sensing Using Machine Learning Techniques.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Sep 20, 2024
Accurate non-invasive monitoring of blood glucose (BG) is a challenging issue in the therapy of diabetes. Here near-infrared (NIR) photoplethysmography (PPG) sensor based on a vapor-deposited mixed tin-lead hybrid perovskite photodetector is develope...
Humans Machine Learning Blood Glucose Photoplethysmography Diabetes Mellitus Calcium Compounds Oxides Titanium Equipment Design
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Inferring ECG Waveforms from PPG Signals with a Modified U-Net Neural Network.

Sensors (Basel, Switzerland) Sep 19, 2024
There are two widely used methods to measure the cardiac cycle and obtain heart rate measurements: the electrocardiogram (ECG) and the photoplethysmogram (PPG). The sensors used in these methods have gained great popularity in wearable devices, which...
Heart Rate Photoplethysmography Electrocardiography Signal Processing, Computer-Assisted Neural Networks, Computer Algorithms Humans Wearable Electronic Devices
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PPG2RespNet: a deep learning model for respirational signal synthesis and monitoring from photoplethysmography (PPG) signal.

Physical and engineering sciences in medicine Sep 17, 2024
Breathing conditions affect a wide range of people, including those with respiratory issues like asthma and sleep apnea. Smartwatches with photoplethysmogram (PPG) sensors can monitor breathing. However, current methods have limitations due to manual...
Photoplethysmography Monitoring, Physiologic Humans Signal Processing, Computer-Assisted Deep Learning Respiration
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Variability of morphology in photoplethysmographic waveform quantified with unsupervised wave-shape manifold learning for clinical assessment.

Physiological measurement Sep 16, 2024
We investigated fluctuations of the photoplethysmography (PPG) waveform in patients undergoing surgery. There is an association between the morphologic variation extracted from arterial blood pressure (ABP) signals and short-term surgical outcomes. T...
Artifacts Photoplethysmography Unsupervised Machine Learning Signal Processing, Computer-Assisted Adult Male Middle Aged Female Aged Humans
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Prediction of intraoperative hypotension using deep learning models based on non-invasive monitoring devices.

Journal of clinical monitoring and computing Aug 19, 2024
PURPOSE: Intraoperative hypotension is associated with adverse outcomes. Predicting and proactively managing hypotension can reduce its incidence. Previously, hypotension prediction algorithms using artificial intelligence were developed for invasive...
Blood Pressure Determination Blood Pressure Area Under Curve Aged ROC Curve Databases, Factual Adult Humans Middle Aged Photoplethysmography Blood Pressure Monitors Hypotension Female Male Artificial Intelligence Electrocardiography Intraoperative Complications Capnography Reproducibility of Results Deep Learning Algorithms Consciousness Monitors Monitoring, Intraoperative
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