Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks | Paper Citation Record

Observation f33aaffa-ede1-45fc-8d87-26d763a45668 · outbound

This paper cites Photoplethysmogram analysis and applications: an integrative review.Frontiers in Physiology, 12:808451, 2022.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Photoplethysmogram analysis and applications: an integrative review.Frontiers in Physiology, 12:808451, 2022

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Observation 8b716a7d-e1cb-493f-a05f-f4ec155e3080 · outbound

This paper cites Wearable photoplethysmography for cardiovascular monitoring.Proceedings of the IEEE, 110(3):355–381, 2022.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Wearable photoplethysmography for cardiovascular monitoring.Proceedings of the IEEE, 110(3):355–381, 2022

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This paper cites Accuracy in wrist-worn, sensor-based measurements of heart rate and energy expenditure in a diverse cohort.Journal of Personalized Medicine, 7(2):3, 2017.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Accuracy in wrist-worn, sensor-based measurements of heart rate and energy expenditure in a diverse cohort.Journal of Personalized Medicine, 7(2):3, 2017

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This paper cites Smartwatch per- formance for the detection and quantification of Atrial Fibrillation.Circulation: Arrhythmia and Electrophysiology, 12(6):e006834, 2019.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Smartwatch per- formance for the detection and quantification of Atrial Fibrillation.Circulation: Arrhythmia and Electrophysiology, 12(6):e006834, 2019

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This paper cites Blood pressure out of the office: its time has finally come.American Journal of Hypertension, 29(3):289–295, 2016.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Blood pressure out of the office: its time has finally come.American Journal of Hypertension, 29(3):289–295, 2016

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This paper cites Ambulatory blood pressure measurement: the case for implementation in primary care.Hyperten- sion, 51(6):1435–1441, 2008.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Ambulatory blood pressure measurement: the case for implementation in primary care.Hyperten- sion, 51(6):1435–1441, 2008

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Unresolved cited work

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Unresolved cited work

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Observation ce91cdc9-3af5-4ad2-8416-a417655b0ec0 · outbound

This paper cites Newer technologies for detection of Atrial Fibrillation.BMJ, 363, 2018.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Newer technologies for detection of Atrial Fibrillation.BMJ, 363, 2018

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Observation e9e8b9a3-3d2c-44c4-86a4-6bb7798821b3 · outbound

This paper cites Emerging technologies for identifying Atrial Fibrillation.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Emerging technologies for identifying Atrial Fibrillation

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Observation 447bd7f0-2467-444c-ba56-9dd54d72ca7f · outbound

This paper cites Diagnostic features and potential applications of PPG signal in healthcare: A systematic review.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Diagnostic features and potential applications of PPG signal in healthcare: A systematic review

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This paper cites Arterial stiffness indices in healthy volunteers using non-invasive digital photoplethysmography.Blood Pressure, 17(2):116–123, 2008.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Arterial stiffness indices in healthy volunteers using non-invasive digital photoplethysmography.Blood Pressure, 17(2):116–123, 2008

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Unresolved cited work

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Observation dbcc150d-fdf3-487a-8b09-ac83453bd549 · outbound

This paper cites Calibration-free pulse oximetry based on two wavelengths in the infrared—A preliminary study.Sensors, 14(4):7420–7434, 2014.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Calibration-free pulse oximetry based on two wavelengths in the infrared—A preliminary study.Sensors, 14(4):7420–7434, 2014

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This paper cites A benchmark for machine-learning based non- invasive blood pressure estimation using photoplethysmogram.Scientific Data, 10(1):149, 2023.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks A benchmark for machine-learning based non- invasive blood pressure estimation using photoplethysmogram.Scientific Data, 10(1):149, 2023

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Unresolved cited work

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This paper cites A survey: From shallow to deep machine learning approaches for blood pressure estimation using biosensors.Expert Systems with Applications, 197:116788, 2022.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks A survey: From shallow to deep machine learning approaches for blood pressure estimation using biosensors.Expert Systems with Applications, 197:116788, 2022

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This paper cites A comparison of deep learning techniques for arterial blood pressure prediction.Cognitive Computation, 14(5):1689– 1710, 2022.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks A comparison of deep learning techniques for arterial blood pressure prediction.Cognitive Computation, 14(5):1689– 1710, 2022

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This paper cites A deep learning approach to monitoring and detecting Atrial Fibrillation using wearable technology.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks A deep learning approach to monitoring and detecting Atrial Fibrillation using wearable technology

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Passive detection of Atrial Fibrillation using a commercially available smartwatch.JAMA Cardiology, 3(5):409–416, 2018

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Ambulatory Atrial Fibrillation monitoring using wearable photoplethysmography with deep learning

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks End-to-end Deep Learning from Raw Sensor Data: Atrial Fibrillation Detection using Wearables

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks A Neural Network-based method for continuous blood pressure estimation from a PPG signal

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This paper cites Brief overview of methods for measurement uncertainty analysis: GUM uncertainty framework, Monte Carlo method, characteristic function approach.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Brief overview of methods for measurement uncertainty analysis: GUM uncertainty framework, Monte Carlo method, characteristic function approach

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Dropout as a Bayesian approximation: Representing model uncertainty in deep learning

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Aleatoric and epistemic uncertainty in machine learning: An introduc- tion to concepts and methods.Machine learning, 110(3):457–506, 2021

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Sources of Uncertainty in Supervised Machine Learning -- A Statisticians' View

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks What uncertainties do we need in Bayesian deep learning for computer vision? Advances in neural information processing systems, 30, 2017

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Simple and scalable predictive uncertainty estimation using deep ensembles.Advances in neural information processing systems, 30, 2017

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Hands-on Bayesian neural networks—A tutorial for deep learning users.IEEE Computational Intelligence Magazine, 17(2):29–48, 2022

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks A practical Bayesian framework for backpropagation networks.Neural Computation, 4(3):448–472, 1992

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Scalable Bayesian Learning with posteriors

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Practical deep learning with Bayesian principles.Advances in neural information processing systems, 32, 2019

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Adam: A Method for Stochastic Optimization

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Variational Learning is Effective for Large Deep Networks

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Deep Ensembles: A Loss Landscape Perspective

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Concrete dropout.Advances in Neural Information Processing Systems, 30, 2017

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Deep evidential regression.Advances in neural information processing systems, 33:14927–14937, 2020

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks A Comprehensive Survey on Evidential Deep Learning and Its Applications

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Bayesian deep learning and a probabilistic perspective of generalization

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks A deeper look into aleatoric and epistemic uncertainty disentanglement

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Uncertainty quantification for deep learning-based remote photoplethysmography.IEEE Transactions on Instrumentation and Measurement, 2023

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks End-to-end prediction of emotion from heartbeat data collected by a consumer fitness tracker

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Quantifying Uncertainty in Blood Oxygen Estimation Models from Real-World Data

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation db885841-e7fd-4573-b9f2-60d733cde132 · outbound

This paper cites VideoCAD: an uncertainty-driven neural network for coronary artery disease screening from facial videos.IEEE Transactions on Instrumentation and Measurement, 72:1–12, 2022.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks VideoCAD: an uncertainty-driven neural network for coronary artery disease screening from facial videos.IEEE Transactions on Instrumentation and Measurement, 72:1–12, 2022

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:00:37.173688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5dcaa985-93e0-44c6-88d9-1b053ab87485 · outbound

This paper cites Improving PPG Signal Classification with Machine Learning: The Power of a Second Opinion.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Improving PPG Signal Classification with Machine Learning: The Power of a Second Opinion

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:00:37.161144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a2498184-fee7-4ac7-a3e8-0d44b168ede4 · outbound

This paper cites Non-contact blood pressure estimation using BP-related cardiovascular knowledge: an uncalibrated method based on consumer-level camera.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Non-contact blood pressure estimation using BP-related cardiovascular knowledge: an uncalibrated method based on consumer-level camera

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:00:37.148365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3ab3db51-1715-4304-9ecc-c9563084a263 · outbound

This paper cites Uncertainty estimation for deep learning-based automated analysis of 12-lead electrocardiograms.European Heart Journal-Digital Health, 2(3):401–415, 2021.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Uncertainty estimation for deep learning-based automated analysis of 12-lead electrocardiograms.European Heart Journal-Digital Health, 2(3):401–415, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:00:37.135940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2eb23101-8ee5-4694-a544-163b8ab84844 · outbound

This paper cites Quantifying deep neural network uncertainty for Atrial Fibrillation detection with limited labels.Scientific Reports, 12(1):20140, 2022.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Quantifying deep neural network uncertainty for Atrial Fibrillation detection with limited labels.Scientific Reports, 12(1):20140, 2022

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:00:37.123435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ffa1621f-727c-44f4-bab1-41d2e6fec408 · outbound

This paper cites BayesBeat: Reliable Atrial Fibrillation Detection from Noisy Photoplethysmography Data.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks BayesBeat: Reliable Atrial Fibrillation Detection from Noisy Photoplethysmography Data

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Resolution: verified exact; local_arxiv, observed 2026-08-15T21:00:36.695938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0674491a-49c1-4439-897f-bb0a4e0d24c1 · outbound

This paper cites Validation of uncertainty quantification metrics: a primer based on the consistency and adaptivity concepts.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Validation of uncertainty quantification metrics: a primer based on the consistency and adaptivity concepts

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:00:37.110004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d4f89d3b-a18f-49b6-8105-09506a7c1415 · outbound

This paper cites Fast and scalable Bayesian deep learning by weight-perturbation in adam.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Fast and scalable Bayesian deep learning by weight-perturbation in adam

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:00:37.095780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 54719b32-a126-4011-997e-74f0079b7bb9 · outbound

This paper cites Accurate uncertainties for deep learning using calibrated regression.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Accurate uncertainties for deep learning using calibrated regression

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Resolution: unresolved; no resolver link, observed 2026-08-15T21:00:36.446419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97ef4a16-950f-4cfb-9195-2bafa949423f · outbound

This paper cites Evaluating and calibrating uncertainty prediction in regression tasks.Sensors, 22(15):5540, 2022.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Evaluating and calibrating uncertainty prediction in regression tasks.Sensors, 22(15):5540, 2022

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Resolution: unresolved; no resolver link, observed 2026-08-15T21:00:36.450544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08cb62ca-5030-4510-8f71-27848d2993f4 · outbound

This paper cites On calibration of modern neural networks.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks On calibration of modern neural networks

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Resolution: unresolved; no resolver link, observed 2026-08-15T21:00:36.454833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 879499fa-d1da-41ec-a87a-06ba7090e0db · outbound

This paper cites Calibration of Model Uncertainty for Dropout Variational Inference.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Calibration of Model Uncertainty for Dropout Variational Inference

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Resolution: unresolved; no resolver link, observed 2026-08-15T21:00:36.458494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e468aae0-c383-41d7-bc4d-29da6073d0b1 · outbound

This paper cites Machine-learning for photoplethysmog- raphy analysis: Benchmarking feature, image, and signal-based approaches.arXiv preprint arXiv:2502.19949, 2025.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Machine-learning for photoplethysmog- raphy analysis: Benchmarking feature, image, and signal-based approaches.arXiv preprint arXiv:2502.19949, 2025

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Resolution: unresolved; no resolver link, observed 2026-08-15T21:00:36.462013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abd57042-df01-42ce-a9ba-2e77f79515e0 · outbound

This paper cites PulseDB: A large, cleaned dataset based on MIMIC-III and VitalDB for benchmarking cuff-less blood pressure estimation methods.Frontiers in Digital Health, 4:1090854, 2023.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks PulseDB: A large, cleaned dataset based on MIMIC-III and VitalDB for benchmarking cuff-less blood pressure estimation methods.Frontiers in Digital Health, 4:1090854, 2023

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:00:37.061190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dcb6465c-cb05-413d-9574-68b3d9ee2205 · outbound

This paper cites Photoplethysmography based Atrial Fibrillation detection: a review.NPJ digital medicine, 3(1):1–12, 2020.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Photoplethysmography based Atrial Fibrillation detection: a review.NPJ digital medicine, 3(1):1–12, 2020

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 60f058a9-c2de-40eb-a723-d5c6ec8fd038 · outbound

This paper cites Motion and noise artifact-resilient Atrial Fibrillation detection using a smartphone.IEEE journal on emerging and selected topics in circuits and systems, 8(2):230–239, 2018.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Motion and noise artifact-resilient Atrial Fibrillation detection using a smartphone.IEEE journal on emerging and selected topics in circuits and systems, 8(2):230–239, 2018

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:00:37.037689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 81eb753a-a9a9-45ff-bae4-1fa46ba8ae8e · outbound

This paper cites Detection of Atrial Fibrillation episodes using a wristband device.Physiological measurement, 38(5):787, 2017.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Detection of Atrial Fibrillation episodes using a wristband device.Physiological measurement, 38(5):787, 2017

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 595f9657-e147-40a2-a3bc-9feaa36394e4 · outbound

This paper cites Identification of Atrial Fibrillation by quantitative analyses of fingertip photoplethysmogram.Scientific reports, 7(1):1–7, 2017.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Identification of Atrial Fibrillation by quantitative analyses of fingertip photoplethysmogram.Scientific reports, 7(1):1–7, 2017

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:00:37.013863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e8559b78-18ff-4bb4-877d-86671f7ffcf4 · outbound

This paper cites On Batch Normalisation for Approximate Bayesian Inference.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks On Batch Normalisation for Approximate Bayesian Inference

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 29d010be-d5d5-4190-ba9c-03c648add93f · outbound

This paper cites Continuous PPG-based blood pressure monitoring using multi-linear regression.IEEE journal of biomedical and health informatics, 26(5):2096–2105, 2021.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Continuous PPG-based blood pressure monitoring using multi-linear regression.IEEE journal of biomedical and health informatics, 26(5):2096–2105, 2021

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ab59771e-f9fe-483a-93a4-f7dd0bfa3d8b · outbound

This paper cites Parametric estimation of pulse arrival time: a robust approach to pulse wave velocity.Physiological measurement, 30(7):603, 2009.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Parametric estimation of pulse arrival time: a robust approach to pulse wave velocity.Physiological measurement, 30(7):603, 2009

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 403b110f-022e-46c6-8822-fb73c3ffcd3c · outbound

This paper cites Cuffless blood pressure estimation based on data-oriented continuous health monitoring system.Computational and mathematical methods in medicine, 2017, 2017.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Cuffless blood pressure estimation based on data-oriented continuous health monitoring system.Computational and mathematical methods in medicine, 2017, 2017

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0a61f3dd-aac0-4d1b-8f35-7f71d9fe4f08 · outbound

This paper cites "Can't Take the Pressure?": Examining the Challenges of Blood Pressure Estimation via Pulse Wave Analysis.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks "Can't Take the Pressure?": Examining the Challenges of Blood Pressure Estimation via Pulse Wave Analysis

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c9332b04-d690-447e-a25a-27aebdabab42 · outbound

This paper cites Cuffless and non-invasive systolic blood pressure estimation for aged class by using a photoplethysmograph.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Cuffless and non-invasive systolic blood pressure estimation for aged class by using a photoplethysmograph

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 75bd0927-b32e-4e5a-90bf-d73786572295 · outbound

This paper cites Central blood pressure estimation from distal PPG measurement using semiclassical signal analysis features.IEEE Access, 9:44963–44973, 2021.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Central blood pressure estimation from distal PPG measurement using semiclassical signal analysis features.IEEE Access, 9:44963–44973, 2021

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 11872e83-c189-40f3-89b2-82d0ae3b5409 · outbound

This paper cites Cuffless blood pressure estimation using cardiovascular dynamics.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Cuffless blood pressure estimation using cardiovascular dynamics

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 320ba121-4b59-43b9-b121-bc8d4bfc04ac · outbound

This paper cites A PPG-based calibration-free cuffless blood pressure estimation method using cardiovascular dynamics.Sensors, 23(8):4145, 2023.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks A PPG-based calibration-free cuffless blood pressure estimation method using cardiovascular dynamics.Sensors, 23(8):4145, 2023

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b5d1e637-860b-4289-aebf-ab4b5877747d · outbound

This paper cites Continuous cuffless blood pressure estimation using pulse transit time and photoplethysmogram intensity ratio.IEEE Transactions on Biomedical Engineering, 63(5):964–972, 2015.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Continuous cuffless blood pressure estimation using pulse transit time and photoplethysmogram intensity ratio.IEEE Transactions on Biomedical Engineering, 63(5):964–972, 2015

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:00:36.916892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 494052a1-252b-4a3b-bf08-10c8832e6b97 · outbound

This paper cites Blood pressure estimation using photoplethysmography only: comparison between different machine learning approaches.Journal of healthcare engineering, 2018, 2018.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Blood pressure estimation using photoplethysmography only: comparison between different machine learning approaches.Journal of healthcare engineering, 2018, 2018

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3bc17706-63f9-42cb-b4a7-763928a01d85 · outbound

This paper cites Continuous blood pressure measurement by using the pulse transit time: comparison to a cuff-based method.European journal of applied physiology, 112(1):309–315, 2012.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Continuous blood pressure measurement by using the pulse transit time: comparison to a cuff-based method.European journal of applied physiology, 112(1):309–315, 2012

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3c2f6294-653d-4764-967b-eb4db5076a66 · outbound

This paper cites Feasibility of cuff-free measurement of systolic and diastolic arterial blood pressure.Journal of electrocardiology, 44(2):201–207, 2011.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Feasibility of cuff-free measurement of systolic and diastolic arterial blood pressure.Journal of electrocardiology, 44(2):201–207, 2011

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 47e93bdb-34a2-44b6-91ff-e5bf39a17102 · outbound

This paper cites an unresolved cited work.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Unresolved cited work

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Resolution: unresolved; raw_fallback, observed 2026-08-15T21:00:36.861804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 146db60b-4c07-4e5c-b171-2ada761a5a20 · outbound

This paper cites Calibration in Machine Learning Uncertainty Quantification: beyond consistency to target adaptivity.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Calibration in Machine Learning Uncertainty Quantification: beyond consistency to target adaptivity

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7a051cd8-06bc-4b31-a427-fb55b4d87af7 · outbound

This paper cites Beyond deep ensembles: A Large- Scale Evaluation of Bayesian Deep Learning Under Distribution Shift.Advances in Neural Information Processing Systems, 36, 2024.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Beyond deep ensembles: A Large- Scale Evaluation of Bayesian Deep Learning Under Distribution Shift.Advances in Neural Information Processing Systems, 36, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:00:36.835246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Reference resolution

Outbound references

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