3D-ballistocardiography in microgravity: comparison with ground based recordings

Investigating Cardiorespiratory Interaction Using Ballistocardiography and Seismocardiography-A Narrative Review

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.

Towards precise tracking of electric-mechanical cardiac time intervals through joint ECG and BCG sensing and signal processing

Automatic tracking of intra-beat cardiac activities in ballistocardiogram (BCG) is a highly interesting yet technically challenging topic for cardiac monitoring, due to the signal's high susceptibility to various forms of distortions. In this paper, we aim to further investigate the BCG waveform detection from a signal processing and analysis viewpoint. We collect synchronized electrocardiography(ECG) and BCG recordings from four healthy human subjects using an in-house built multi-physiological monitoring device. Particularly, we study post-exercise ECG-BCG signals that embed considerable variation in the heart beat during the post-exercise recovery phase. Furthermore, we develop an efficient and interactive tool for detecting and marking ECG-BCG waveforms in each heart beat. Through analyzing the detected time interval signals, we explore new interesting patterns of dynamic associations between different time interval signals. At the same time, we call for development of improved detection algorithms to address robustness and accuracy issues.