ArticleIEEE transactions on bio-medical engineering2020
Classification of Decompensated Heart Failure From Clinical and Home Ballistocardiography.
Article in IEEE transactions on bio-medical engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predicting disease outcomes from remote monitoring using machine learning: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Smartwatch-based detection of loss of pulse.PloS one · 2026Article
- Headpulse Biometric Measures Following Concussion in Young Adult Athletes.JAMA network open · 2023Article
- Wearables in Cardiovascular Disease.Journal of cardiovascular translational research · 2023Review
- Wearable Devices in Cardiovascular Medicine.Circulation research · 2023Review
- Trends in Passive IoT Biomarker Monitoring and Machine Learning for Cardiovascular Disease Management in the U.S. Elderly Population.Advances in geriatric medicine and research · 2023Article
- Non-invasive technologies for heart failure, systolic and diastolic dysfunction modeling: a scoping review.Frontiers in bioengineering and biotechnology · 2023Article
- Contactless remote monitoring of sleep: evaluating the feasibility of an under-mattress sensor mat in a real-life deployment.Health systems (Basingstoke, England) · 2023Article
- Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation.Frontiers in digital health · 2023Article
- Mechanocardiography-Based Measurement System Indicating Changes in Heart Failure Patients during Hospital Admission and Discharge.Sensors (Basel, Switzerland) · 2022Article
- Passively Captured Interpersonal Social Interactions and Motion From Smartphones for Predicting Decompensation in Heart Failure: Observational Cohort Study.JMIR formative research · 2022Article
- The Novel Data Collection and Analytics Tools for Remote Patient Monitoring in Heart Failure (Nov-RPM-HF) Trial: Protocol for a Single-Center Prospective Trial.JMIR research protocols · 2022Article
- Statistical Analysis of the Consistency of HRV Analysis Using BCG or Pulse Wave Signals.Sensors (Basel, Switzerland) · 2022Article
- Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signals.Frontiers in physiology · 2022Article
- Article
- The kinocardiograph for assessment of changes in haemodynamic load in patients with chronic heart failure with reduced ejection fraction.ESC heart failure · 2021Article
- A Noncontact Ballistocardiography-Based IoMT System for Cardiopulmonary Health Monitoring of Discharged COVID-19 Patients.IEEE internet of things journal · 2021Article
- Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set.Sensors (Basel, Switzerland) · 2021Article
- Case Report: Ambient Sensor Signals as Digital Biomarkers for Early Signs of Heart Failure Decompensation.Frontiers in cardiovascular medicine · 2021Article
- Closed-Loop Multiscale Computational Model of Human Blood Circulation. Applications to Ballistocardiography.Frontiers in physiology · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
objectiveTo improve home monitoring of heart failure patients so as to reduce emergency room visits and hospital readmissions. We aim to do this by analyzing the ballistocardiogram (BCG) to evaluate the clinical state of the patient.
methods1) High quality BCG signals were collected at home from HF patients after discharge. 2) The BCG recordings were preprocessed to exclude outliers and artifacts. 3) Parameters of the BCG that contain information about the cardiovascular system were extracted. These features were used for the task of classification of the BCG recording based on the status of HF.
resultsThe best AUC score for the task of classification obtained was 0.78 using slight variant of the leave one subject out validation method.
conclusionThis work demonstrates that high quality BCG signals can be collected in a home environment and used to detect the clinical state of HF patients. SIGNIFICANCE: In future work, a clinician/caregiver can be introduced into the system so that appropriate interventions can be performed based on the clinical state monitored at home.
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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.