Grouping Similar Seismocardiographic Signals Using Respiratory Information

TL;DR

This study investigates how respiratory information influences the morphology of seismocardiographic signals, showing that lung volume provides better grouping of similar SCG events, which can improve cardiac signal analysis.

Contribution

It introduces a method to categorize SCG signals based on respiratory data, enhancing understanding and classification of cardiac signals influenced by respiration.

Findings

Respiratory flow rate can distinguish SCG events into inspiratory/expiratory groups.

Lung volume provides superior grouping of similar SCG events.

Variations in SCG morphology are linked to changes in lung volume and intrathoracic pressure.

Abstract

Seismocardiography (SCG) offers a potential non-invasive method for cardiac monitoring. Quantification of the effects of different physiological conditions on SCG can lead to enhanced understanding of SCG genesis, and may explain how some cardiac pathologies may affect SCG morphology. In this study, the effect of the respiration on the SCG signal morphology is investigated. SCG, ECG, and respiratory flow rate signals were measured simultaneously in 7 healthy subjects. Results showed that SCG events tended to have two slightly different morphologies. The respiratory flow rate and lung volume information were used to group the SCG events into inspiratory/expiratory groups or low/high lung-volume groups, respectively. Although respiratory flow information could separate similar SCG events into two different groups, the lung volume information provided better grouping of similar SCGs. This suggests that variations in SCG morphology may be due, at least in part, to changes in the intrathoracic pressure or heart location since those parameters correlates more with lung volume than respiratory flow. Categorizing SCG events into different groups containing similar events allows more accurate estimation of SCG features, and better signal characterization, and classification.