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
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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
Multimodal Emotion Recognition by Fusing Video Semantic in MOOC Learning Scenarios
The method proposed in this paper not only contributes to a deeper understanding of the impact of instructional videos on learners' emotional states but also provides a beneficial reference for future research on emotion recognition in MOOC learning scenarios.
Constraint Latent Space Matters: An Anti-anomalous Waveform Transformation Solution from Photoplethysmography to Arterial Blood Pressure
Arterial blood pressure (ABP) holds substantial promise for proactive cardiovascular health management.
Non-Contact Acquisition of PPG Signal using Chest Movement-Modulated Radio Signals
With this, we first utilize principal component analysis for dimensionality reduction of the radio data.
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For both classification problems, we feed the pre-processed and augmented PPG data to a number of machine learning, deep learning and transformer models which models provide a very high accuracy, i. e., in the range of 95% to 99%.
TNANet: A Temporal-Noise-Aware Neural Network for Suicidal Ideation Prediction with Noisy Physiological Data
Current methods predominantly focus on image and text data or address artificially introduced noise, neglecting the complexities of natural noise in time-series analysis.
Cuff-less Arterial Blood Pressure Waveform Synthesis from Single-site PPG using Transformer & Frequency-domain Learning
We propose two novel purpose-built deep learning (DL) models for synthesis of the arterial blood pressure (ABP) waveform in a cuff-less manner, using a single-site photoplethysmography (PPG) signal.
Variability of morphology in beat-to-beat photoplethysmographic waveform quantified with unsupervised wave-shape manifold learning for clinical assessment
We investigated the beat-to-beat fluctuation of the photoplethysmography (PPG) waveform.
A low-cost PPG sensor-based empirical study on healthy aging based on changes in PPG morphology
For biological age prediction, the shallow FFNN again performs the best with a mean absolute error (MAE) of 1. 64.
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.
