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Abstract Seismocardiographic (SCG) signals are chest wall vibrations induced by cardiac activity and are potentially useful for cardiac monitoring and diagnosis. SCG waveform is observed to vary with respiration, but the mechanism of these changes is poorly understood as alterations in autonomic tone, lung volume, heart location and intrathoracic pressure are all varying during the respiratory cycle. Understanding SCG variability and its sources may help reduce variability and increase SCG clinical utility. This study investigated SCG variability during breath holding (BH) at two different lung volumes (i.e., end inspiration and end expiration) and five airway pressures (i.e., 0, ± 2–4, and ± 15–20 cm H2O). Variability during normal breathing was also studied with and without grouping SCG beats into two clusters of similar waveform morphologies (performed using the K-medoid algorithm in an unsupervised machine learning fashion). The study included 15 healthy subjects (11 Females and 4 males, Age: 21 ± 2 y) where SCG, ECG, and spirometry were simultaneously acquired. SCG waveform variability was calculated at each experimental state (i.e., lung volume and airway pressure). Results showed that breath holding was more effective in reducing the intra-state variability of SCG than clustering normal breathing data. For the BH states, the intra-state variability increased as the airway pressure deviated from zero. The subaudible-to-audible energy ratio of the BH states increased as the airway pressure decreased below zero which may be related to the effect of the intrathoracic pressure on cardiac afterload and blood ejection. When combining the BH waveforms at end inspiration and end expiration states (at the same airway pressures) into one group, the intra-state variability increased, which suggests that the lung volume and associated change in heart location were a significant source of variability. The linear trend between airway pressure and waveform changes was found to be statistically significant for BH at end expiration. To confirm these findings, more studies are needed with a larger number of airway pressure levels and larger number of subjects.

Seismocardiographic (SCG) signals are chest wall vibrations induced by cardiac activity and are potentially useful for cardiac monitoring and diagnosis. SCG waveform is observed to vary with respiration, but the mechanism of these changes is poorly understood as alterations in autonomic tone, lung volume, heart location and intrathoracic pressure are all varying during the respiratory cycle. Understanding SCG variability and its sources may help reduce variability and increase SCG clinical utility. This study investigated SCG variability during breath holding (BH) at two different lung volumes (i.e., end inspiration and end expiration) and five airway pressures (i.e., 0, ± 2–4, and ± 15–20 cm H2O). Variability during normal breathing was also studied with and without grouping SCG beats into two clusters of similar waveform morphologies (performed using the K-medoid algorithm in an unsupervised machine learning fashion). The study included 15 healthy subjects (11 Females and 4 males, Age: 21 ± 2 y) where SCG, ECG, and spirometry were simultaneously acquired. SCG waveform variability was calculated at each experimental state (i.e., lung volume and airway pressure). Results showed that breath holding was more effective in reducing the intra-state variability of SCG than clustering normal breathing data. For the BH states, the intra-state variability increased as the airway pressure deviated from zero. The subaudible-to-audible energy ratio of the BH states increased as the airway pressure decreased below zero which may be related to the effect of the intrathoracic pressure on cardiac afterload and blood ejection. When combining the BH waveforms at end inspiration and end expiration states (at the same airway pressures) into one group, the intra-state variability increased, which suggests that the lung volume and associated change in heart location were a significant source of variability. The linear trend between airway pressure and waveform changes was found to be statistically significant for BH at end expiration. To confirm these findings, more studies are needed with a larger number of airway pressure levels and larger number of subjects. [ABSTRACT FROM AUTHOR]