Heart Rate Variability
16 papers with code • 0 benchmarks • 3 datasets
Heart rate variability (HRV) is the physiological phenomenon of variation in the time interval between heartbeats. It is measured by the variation in the beat-to-beat interval.
Benchmarks
These leaderboards are used to track progress in Heart Rate Variability
UBFC-rPPG
MMSE-HR
VIPL-HR
Latest papers
Conversational Health Agents: A Personalized LLM-Powered Agent Framework
openCHA includes an orchestrator to plan and execute actions for gathering information from external sources, essential for formulating responses to user inquiries.
Accelerated Sample-Accurate R-Peak Detectors Based on Visibility Graphs
Further acceleration is obtained by adopting the computationally efficient horizontal visibility graph, which has not yet been used for R-peak detection.
CardiacGen: A Hierarchical Deep Generative Model for Cardiac Signals
SENSE-Lab-OSU/cardiac_gen_model
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We present CardiacGen, a Deep Learning framework for generating synthetic but physiologically plausible cardiac signals like ECG.
Pan-Tompkins++: A Robust Approach to Detect R-peaks in ECG Signals
However, the performance of the Pan-Tompkins algorithm in detecting the QRS complexes degrades in low-quality and noisy signals.
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.
Real-Time Monitoring of User Stress, Heart Rate and Heart Rate Variability on Mobile Devices
The user's pulse wave is then used to determine stress (according to the Baevsky Stress Index), heart rate, and heart rate variability.
SCAMPS: Synthetics for Camera Measurement of Physiological Signals
The use of cameras and computational algorithms for noninvasive, low-cost and scalable measurement of physiological (e. g., cardiac and pulmonary) vital signs is very attractive.
pyVHR: a Python framework for remote photoplethysmography
phuselab/pyVHR
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PeerJ Computer Science 2022
A number of effective methods relying on data-driven, model-based and statistical approaches have emerged in the past two decades.
PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regression
The proposed method learns an individual-specific predictive model from heterogeneous observations, and enables estimation of an optimal transport map that yields a push forward operation onto the demographic features for each task.
Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced Error
However, extracting IBIs from noisy signals is challenging since the morphology of the signal is distorted in the presence of the noise.
