ArticleScientific reports2019
Chest Movement and Respiratory Volume both Contribute to Thoracic Bioimpedance during Loaded Breathing.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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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.
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Who cites it
16 citing papers in PubMed, 42 citations in OpenAlex.
- Respiratory Monitoring in Motion: An Overview of Wearable Methods and Algorithmic Approaches for Reliable Assessment.Biosensors · 2026Review
- Applications of bioimpedance measurement in clinical and allied fields: a comprehensive review.Biomedical engineering online · 2026Review
- Radar-Based Monitoring: A Proof of Principle Study in a Piglet Model for a Novel Approach in Non-Contact Vital Sign Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Leveraging Central Sleep Apnea Events to Validate the Measurement of Lung Volume Changes Using Thoracic Bio-Impedance.Sensors (Basel, Switzerland) · 2025Article
- Thoracic Motion Analysis Using a TrueDepth Camera in Patients with Relapsing Polychondritis: A Pilot Study.Healthcare (Basel, Switzerland) · 2025Article
- Validation and user experience of a dry electrode based Health Patch for heart rate and respiration rate monitoring.Scientific reports · 2024Article
- High-speed bioimpedance-based gating system for radiotherapy: Prototype and proof of principle.Journal of applied clinical medical physics · 2024Article
- Impedance-Based Ventilation Detection and Signal Quality Control During Out-of-Hospital Cardiopulmonary Resuscitation.IEEE journal of biomedical and health informatics · 2023Article
- Cough sound-based estimation of vital capacity via cough peak flow using artificial neural network analysis.Scientific reports · 2023Article
- Development of a wearable belt with integrated sensors for measuring multiple physiological parameters related to heart failure.Scientific reports · 2022Article
- Wearable Bioimpedance Monitoring: Viewpoint for Application in Chronic Conditions.JMIR biomedical engineering · 2021Article
- Artefact Detection in Impedance Pneumography Signals: A Machine Learning Approach.Sensors (Basel, Switzerland) · 2021Article
- Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System.Sensors (Basel, Switzerland) · 2021Article
- Toward the Use of Temporary Tattoo Electrodes for Impedancemetric Respiration Monitoring and Other Electrophysiological Recordings on Skin.Sensors (Basel, Switzerland) · 2021Article
- Non-Invasive Cardiac and Respiratory Activity Assessment From Various Human Body Locations Using Bioimpedance.IEEE open journal of engineering in medicine and biology · 2021Article
- Simple Wireless Impedance Pneumography System for Unobtrusive Sensing of Respiration.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
Abstract
Bioimpedance has been widely studied as alternative to respiratory monitoring methods because of its linear relationship with respiratory volume during normal breathing. However, other body tissues and fluids contribute to the bioimpedance measurement. The objective of this study is to investigate the relevance of chest movement in thoracic bioimpedance contributions to evaluate the applicability of bioimpedance for respiratory monitoring. We measured airflow, bioimpedance at four electrode configurations and thoracic accelerometer data in 10 healthy subjects during inspiratory loading. This protocol permitted us to study the contributions during different levels of inspiratory muscle activity. We used chest movement and volume signals to characterize the bioimpedance signal using linear mixed-effect models and neural networks for each subject and level of muscle activity. The performance was evaluated using the Mean Average Percentage Errors for each respiratory cycle. The lowest errors corresponded to the combination of chest movement and volume for both linear models and neural networks. Particularly, neural networks presented lower errors (median below 4.29%). At high levels of muscle activity, the differences in model performance indicated an increased contribution of chest movement to the bioimpedance signal. Accordingly, chest movement contributed substantially to bioimpedance measurement and more notably at high muscle activity levels.
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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.