Harnessing the Manifold Structure of Cardiomechanical Signals for Physiological Monitoring During Hemorrhage

Objective: Local oscillation of the chest wall in response to events during the cardiac cycle may be captured using a sensing modality called seismocardiography (SCG), which is commonly used to infer cardiac time intervals (CTIs) such as the pre-ejection period (PEP). An important factor impeding the ubiquitous application of SCG for cardiac monitoring is that morphological variability of the signals makes consistent inference of CTIs a difficult task in the time-domain. The goal of this work is therefore to enable SCG-based physiological monitoring during trauma-induced hemorrhage using signal dynamics rather than morphological features. Methods: We introduce and explore the observation that SCG signals follow a consistent low-dimensional manifold structure during periods of changing PEP induced in a porcine model of trauma injury. Furthermore, we show that the distance traveled along this manifold correlates strongly to changes in PEP (ΔPEP). Results: ΔPEP estimation during hemorrhage was achieved with a median R
²
of 92.5% using a rapid manifold approximation method, comparable to an ISOMAP reference standard, which achieved an R
²
of 95.3%. Conclusion: Rapidly approximating the manifold structure of SCG signals allows for physiological inference abstracted from the time-domain, laying the groundwork for robust, morphology-independent processing methods. Significance: Ultimately, this work represents an important advancement in SCG processing, enabling future clinical tools for trauma injury management.