Evidence map›Paper›PMID 36502267›Full record

ReviewSensors (Basel, Switzerland)2022

Investigating Cardiorespiratory Interaction Using Ballistocardiography and Seismocardiography-A Narrative Review.

Paniz Balali, Jeremy Rabineau, Amin Hossein, Cyril Tordeur, Olivier Debeir, Philippe van de Borne

Open access · goldAbstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
2.9field-weighted citation impact, top 9% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 38 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Unobtrusive Bed Monitor State of the Art.Sensors (Basel, Switzerland) · 2025
    Review
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Paniz BalaliLaboratoray of Physics and Physiology, Université Libre de Bruxelles, 1050 Brussels, Belgium.
Jeremy RabineauLaboratoray of Physics and Physiology, Université Libre de Bruxelles, 1050 Brussels, Belgium.ORCID 0000-0002-7946-075X
Amin HosseinLaboratoray of Physics and Physiology, Université Libre de Bruxelles, 1050 Brussels, Belgium.
Cyril TordeurLaboratoray of Physics and Physiology, Université Libre de Bruxelles, 1050 Brussels, Belgium.
Olivier DebeirLaboratory of Image Synthesis and Analysis, Université Libre de Bruxelles, 1050 Brussels, Belgium.ORCID 0000-0002-6461-1551
Philippe van de BorneLaboratoray of Physics and Physiology, Université Libre de Bruxelles, 1050 Brussels, Belgium.ORCID 0000-0002-5693-1045
Université Libre de Bruxelles · BE

Funding

Belgian Federal Science Policy Office 4000110826Fund for Scientific Research 043709 & 29801
6 · The paper itself

Abstract

Ballistocardiography (BCG) and seismocardiography (SCG) are non-invasive techniques used to record the micromovements induced by cardiovascular activity at the body's center of mass and on the chest, respectively. Since their inception, their potential for evaluating cardiovascular health has been studied. However, both BCG and SCG are impacted by respiration, leading to a periodic modulation of these signals. As a result, data processing algorithms have been developed to exclude the respiratory signals, or recording protocols have been designed to limit the respiratory bias. Reviewing the present status of the literature reveals an increasing interest in applying these techniques to extract respiratory information, as well as cardiac information. The possibility of simultaneous monitoring of respiratory and cardiovascular signals via BCG or SCG enables the monitoring of vital signs during activities that require considerable mental concentration, in extreme environments, or during sleep, where data acquisition must occur without introducing recording bias due to irritating monitoring equipment. This work aims to provide a theoretical and practical overview of cardiopulmonary interaction based on BCG and SCG signals. It covers the recent improvements in extracting respiratory signals, computing markers of the cardiorespiratory interaction with practical applications, and investigating sleep breathing disorders, as well as a comparison of different sensors used for these applications. According to the results of this review, recent studies have mainly concentrated on a few domains, especially sleep studies and heart rate variability computation. Even in those instances, the study population is not always large or diversified. Furthermore, BCG and SCG are prone to movement artifacts and are relatively subject dependent. However, the growing tendency toward artificial intelligence may help achieve a more accurate and efficient diagnosis. These encouraging results bring hope that, in the near future, such compact, lightweight BCG and SCG devices will offer a good proxy for the gold standard methods for assessing cardiorespiratory function, with the added benefit of being able to perform measurements in real-world situations, outside of the clinic, and thus decrease costs and time.

Indexed as

Artificial IntelligenceBallistocardiographyElectrocardiographyHeart RateHumansRespiratory RateSignal Processing, Computer-Assistedballistocardiographycardiopulmonary interactioncardiorespiratory interactionheart rate variabilityrespiratory signalseismocardiographysleep apneasleep breathing disorderswearable cardiac monitoring

Identifiers

PMID36502267
PMCPMC9737480
OpenAlexW4311664146

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.