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Investigating Cardiorespiratory Interaction Using Ballistocardiography and Seismocardiography-A Narrative Review
Paniz Balali1,2, Jeremy Rabineau1, Amin Hossein1
1Laboratoray of Physics and Physiology, Université Libre de Bruxelles, 1050 Brussels, Belgium.
Insights
Ballistocardiography (BCG) and seismocardiography (SCG) offer non-invasive vital sign monitoring. These techniques can simultaneously assess cardiorespiratory interactions, aiding in sleep studies and potentially improving diagnostics with AI.
Area of Science:
- Biomedical Engineering
- Cardiovascular Physiology
- Respiratory Medicine
Background:
- Ballistocardiography (BCG) and seismocardiography (SCG) are non-invasive methods measuring cardiovascular activity.
- Respiration significantly impacts BCG and SCG signals, necessitating signal processing or modified protocols.
- Recent literature shows growing interest in extracting respiratory information alongside cardiac data.
Purpose of the Study:
- To provide a comprehensive overview of cardiopulmonary interaction using BCG and SCG signals.
- To review advancements in extracting respiratory signals and cardiorespiratory interaction markers.
- To explore applications in sleep breathing disorders and compare sensor technologies.
Main Methods:
- Literature review focusing on cardiorespiratory interactions derived from BCG and SCG.
- Analysis of signal processing techniques for respiratory signal extraction.
- Examination of studies on sleep breathing disorders and heart rate variability.
- Comparison of various sensors used for BCG and SCG measurements.
Main Results:
- Simultaneous monitoring of respiratory and cardiovascular signals is feasible with BCG/SCG.
- Recent research predominantly focuses on sleep studies and heart rate variability.
- BCG and SCG are susceptible to motion artifacts and subject variability.
- Artificial intelligence shows promise for enhancing diagnostic accuracy.
Conclusions:
- BCG and SCG hold potential for non-invasive, real-world cardiorespiratory monitoring.
- Further research with larger, diverse populations is needed.
- AI integration may overcome current limitations and improve diagnostic capabilities.
- Compact BCG/SCG devices could become cost-effective alternatives to traditional methods.
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.
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