ArticleIEEE transactions on bio-medical engineering2022
Estimation of Changes in Intracardiac Hemodynamics Using Wearable Seismocardiography and Machine Learning in Patients With Heart Failure: A Feasibility Study.
Article in IEEE transactions on bio-medical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 51 citations in OpenAlex.
- The Future of Cardiac Care: A Review on the Role for Non-Invasive, Home-Based Monitoring and Rehabilitation after Hospitalisation for Acute Decompensated Heart Failure.Current heart failure reports · 2026Review
- Advances in cardiac devices and bioelectronics augmented with artificial intelligence.The Journal of physiology · 2026Review
- Integrating multimodal intelligence in heart failure: AI-driven risk prediction, precision diagnosis, phenotyping, personalized treatment, and prognosis.Chinese medical journal · 2026Review
- Seismocardiography Pig Hypovolemia Dataset for Signal Quality Indexing and Validated Cardiac Timings.Scientific data · 2026Article
- Deep Learning Predicts Cardiac Output from Seismocardiographic Signals in Heart Failure.The American journal of cardiology · 2026Article
- Quantifying changes in triaxial seismocardiography variability due to sub-optimal volume status.Frontiers in physiology · 2026Article
- Non-Invasive Wearable Technology to Predict Heart Failure Decompensation.Journal of clinical medicine · 2025Review
- VOCurrent cardiology reports · 2025Review
- Emerging use of pulmonary artery and cardiac pressure sensing technology in the management of worsening heart failure events.Heart failure reviews · 2025Review
- A Forcecardiography dataset with simultaneous SCG, Heart Sounds, ECG, and Respiratory signals.Scientific data · 2025Article
- Noninvasive biometric monitoring technologies for patients with heart failure.Heart failure reviews · 2025Review
- Evaluation of seismocardiography in detecting pre-load changes and cardiovascular disease: a comparative study with transthoracic echocardiography.European heart journal. Digital health · 2025Article
- Application of Telemedicine in the Management of Cardiovascular Diseases: A Focus on Heart Failure.Reviews in cardiovascular medicine · 2025Review
- From video to vital signs: a new method for contactless multichannel seismocardiography.NPJ cardiovascular health · 2025Article
- Deep learning-based beat-to-beat delineation of heart sounds and fiducial points in seismocardiography.Frontiers in digital health · 2025Article
- Multifunctional Porous Soft Composites for Bimodal Wearable Cardiac Monitors.AIChE journal. American Institute of Chemical Engineers · 2024Article
- Non-invasive heart failure monitoring: leveraging smart scales and digital biomarkers to improve heart failure outcomes.Heart failure reviews · 2024Review
- Accurate Modeling of Ejection Fraction and Stroke Volume With Mobile Phone Auscultation: Prospective Case-Control Study.JMIR cardio · 2024Article
- An open-access simultaneous electrocardiogram and phonocardiogram database.Physiological measurement · 2024Article
- A Wavelet-Based Approach for Motion Artifact Reduction in Ambulatory Seismocardiography.IEEE journal of translational engineering in health and medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
objectiveTracking changes in hemodynamic congestion and the consequent proactive readjustment of treatment has shown efficacy in reducing hospitalizations for patients with heart failure (HF). However, the cost-prohibitive nature of these invasive sensing systems precludes their usage in the large patient population affected by HF. The objective of this research is to estimate the changes in pulmonary artery mean pressure (PAM) and pulmonary capillary wedge pressure (PCWP) following vasodilator infusion during right heart catheterization (RHC), using changes in simultaneously recorded wearable seismocardiogram (SCG) signals captured with a small wearable patch.
methodsA total of 20 patients with HF (20% women, median age 55 (interquartile range (IQR), 44-64) years, ejection fraction 24 (IQR, 16-43)) were fitted with a wearable sensing patch and underwent RHC with vasodilator challenge. We divided the dataset randomly into a training-testing set (n = 15) and a separate validation set (n = 5). We developed globalized (population) regression models to estimate changes in PAM and PCWP from the changes in simultaneously recorded SCG.
resultsThe regression model estimated both pressures with good accuracies: root-mean-square-error (RMSE) of 2.5 mmHg and R
conclusionChanges in wearable SCG signals may be used to track acute changes in intracardiac hemodynamics in patients with HF. SIGNIFICANCE: This method holds promise in tracking longitudinal changes in hemodynamic congestion in hemodynamically-guided remote home monitoring and treatment for patients with HF.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.