Photoplethysmography (PPG)
22 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
These leaderboards are used to track progress in Photoplethysmography (PPG)
Libraries
Use these libraries to find Photoplethysmography (PPG) models and implementations
UBFC-rPPG
MMSE-HR
MTHS
MIMIC PERform Testing Dataset
Subtasks
Most implemented papers
Facial Video-based Remote Physiological Measurement via Self-supervised Learning
yuezijie/video-based-remote-physiological-measurement-via-self-supervised-learning
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Facial video-based remote physiological measurement aims to estimate remote photoplethysmography (rPPG) signals from human face videos and then measure multiple vital signs (e. g. heart rate, respiration frequency) from rPPG signals.
Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection using Eight Million Samples Labeled with Imprecise Arrhythmia Alarms
To address this challenge, in this study, we propose to leverage AF alarms from bedside patient monitors to label concurrent PPG signals, resulting in the largest PPG-AF dataset so far (8. 5M 30-second records from 24100 patients) and demonstrating a practical approach to build large labeled PPG datasets.
Motion Matters: Neural Motion Transfer for Better Camera Physiological Measurement
Roni-Lab/MA-rPPG-Video-Toolbox
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Our findings illustrate the usefulness of motion transfer as a data augmentation technique for improving the generalization of models for camera-based physiological sensing.
ApSense: Data-driven Algorithm in PPG-based Sleep Apnea Sensing
This paper contributes to developing fingertip PPG-based obstructive sleep apnea (OSA) event onset recognition.
Learned Kernels for Sparse, Interpretable, and Efficient Medical Time Series Processing
sullychen/smolk
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Results: Our interpretable method achieves greater than 99% of the performance of the state-of-the-art methods on the PPG artifact detection task, and even outperforms the state-of-the-art on a challenging out-of-distribution test set, while using dramatically fewer parameters (2% of the parameters of Segade, and about half of the parameters of Tiny-PPG).
Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal
subangkar/simsig
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In this paper, we propose a novel contrastive learning based deep learning framework for patient similarity search using physiological signals.
Hypertension Detection From High-Dimensional Representation of Photoplethysmogram Signals
Although some studies suggest a relationship between blood pressure and certain vital signals, such as Photoplethysmogram (PPG), reliable generalization of the proposed blood pressure estimation methods is not yet guaranteed.
Region-Disentangled Diffusion Model for High-Fidelity PPG-to-ECG Translation
debadityaqu/rddm
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In this work, we introduce Region-Disentangled Diffusion Model (RDDM), a novel diffusion model designed to capture the complex temporal dynamics of ECG.
SiamAF: Learning Shared Information from ECG and PPG Signals for Robust Atrial Fibrillation Detection
Previous deep learning models learn from a single modality, either electrocardiogram (ECG) or photoplethysmography (PPG) signals.
ALPHA: AnomaLous Physiological Health Assessment Using Large Language Models
Our findings reveal that LLMs exhibit exceptional performance in determining medical indicators, including a Mean Absolute Error (MAE) of less than 1 beat per minute for heart rate and less than 1% for oxygen saturation (SpO2).
