Decomposition of near-infrared spectroscopy signals using oblique subspace projections: applications in brain hemodynamic monitoring
Clinical data is comprised by a large number of synchronously collected biomedical signals that are measured at different locations. Deciphering the interrelationships of these signals can yield important information about their dependence providing some useful clinical diagnostic data. For instance...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2016
- Institución:
- Universidad del Rosario
- Repositorio:
- Repositorio EdocUR - U. Rosario
- Idioma:
- eng
- OAI Identifier:
- oai:repository.urosario.edu.co:10336/27476
- Acceso en línea:
- https://doi.org/10.3389/fphys.2016.00515
https://repository.urosario.edu.co/handle/10336/27476
- Palabra clave:
- Tikhonov regularization
Biomedical signal processing
Brain hemodynamics
Oblique subspace projections
- Rights
- License
- Abierto (Texto Completo)
| Summary: | Clinical data is comprised by a large number of synchronously collected biomedical signals that are measured at different locations. Deciphering the interrelationships of these signals can yield important information about their dependence providing some useful clinical diagnostic data. For instance, by computing the coupling between Near-Infrared Spectroscopy signals (NIRS) and systemic variables the status of the hemodynamic regulation mechanisms can be assessed. In this paper we introduce an algorithm for the decomposition of NIRS signals into additive components. |
|---|