Details

Autor(en) / Beteiligte

• Zia, Jonathan
• Kimball, Jacob
• Rozell, Christopher
• Inan, Omer T.

Titel

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

Ist Teil von

• IEEE transactions on biomedical engineering, 2021-06, Vol.68 (6), p.1759-1767

Ort / Verlag

United States: IEEE

Erscheinungsjahr

2021

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• 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 (<inline-formula><tex-math notation="LaTeX">\Delta</tex-math></inline-formula>PEP). Results: <inline-formula><tex-math notation="LaTeX">\Delta</tex-math></inline-formula>PEP estimation during hemorrhage was achieved with a median <inline-formula><tex-math notation="LaTeX">R^2</tex-math></inline-formula> of 92.5% using a rapid manifold approximation method, comparable to an ISOMAP reference standard, which achieved an <inline-formula><tex-math notation="LaTeX">R^2</tex-math></inline-formula> 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.