@Article{zia.20,
  author = {Zia, J. and Kimball, J. and Rozell, C.J. and Inan, O.T.},
  title = {Harnessing the Manifold Structure of Cardiomechanical Signals for Physiological Monitoring during Hemorrhage},
  month = jun,
  journal = {IEEE Transactions on Biomedical Engineering},
  year={2021},
  volume={68},
  number={6},
  pages={1759-1767},
  doi={10.1109/TBME.2020.3014040},
  abstract = {
  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 moni- toring 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 (∆PEP). Results: ∆PEP 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.},
  url = {https://doi.org/10.1109/TBME.2020.3014040}
}