Photoplethysmography (PPG)
23 papers with code • 0 benchmarks • 4 datasets
Photoplethysmography (PPG) is a non-invasive light-based method that has been used since the 1930s for monitoring cardiovascular activity.
Benchmarks
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Libraries
Use these libraries to find Photoplethysmography (PPG) models and implementations
UBFC-rPPG
MMSE-HR
MTHS
MIMIC PERform Testing Dataset
Subtasks
Latest papers with no code
Large-scale Training of Foundation Models for Wearable Biosignals
Tracking biosignals is crucial for monitoring wellness and preempting the development of severe medical conditions.
Non-Contact NIR PPG Sensing through Large Sequence Signal Regression
Non-Contact sensing is an emerging technology with applications across many industries from driver monitoring in vehicles to patient monitoring in healthcare.
A Novel 1D Generative Adversarial Network-based Framework for Atrial Fibrillation Detection using Restored Wrist Photoplethysmography Signals
Self-AFNet managed to achieve an accuracy of 98. 07% and 98. 97%, respectively using two ECG splits which is comparable to the performance of AF detection utilizing reconstructed PPG segments.
Video-based sympathetic arousal assessment via peripheral blood flow estimation
We obtain median correlations of 0. 57 to 0. 63 between our inferred signals and the ground truth EDA using only videos of the participants' palms or foreheads or PPG signals from the foreheads or fingers.
Remote Heart Rate Monitoring in Smart Environments from Videos with Self-supervised Pre-training
Recent advances in deep learning have made it increasingly feasible to estimate heart rate remotely in smart environments by analyzing videos.
Photoplethysmography based atrial fibrillation detection: an updated review from July 2019
This paper offers a comprehensive review of the latest advancements in PPG-based AF detection, utilizing digital health and artificial intelligence (AI) solutions, within the timeframe spanning from July 2019 to December 2022.
PPG-to-ECG Signal Translation for Continuous Atrial Fibrillation Detection via Attention-based Deep State-Space Modeling
Photoplethysmography (PPG) is a cost-effective and non-invasive technique that utilizes optical methods to measure cardiac physiology.
Personalised and Adjustable Interval Type-2 Fuzzy-Based PPG Quality Assessment for the Edge
The proposed system employs a personalised approach to adapt the IT2FLS parameters to the unique characteristics of each individual's PPG signals. Additionally, the system provides adjustable levels of personalisation, allowing healthcare providers to adjust the system to meet specific requirements for different applications.
PhysioZoo: The Open Digital Physiological Biomarkers Resource
PhysioZoo is a collaborative platform designed for the analysis of continuous physiological time series.
A machine-learning sleep-wake classification model using a reduced number of features derived from photoplethysmography and activity signals
The classification of sleep stages is a mandatory step to assess sleep quality, providing the metrics to estimate the quality of sleep and how well our body is functioning during this essential period of rest.
