Analysis of non-linear respiratory influences on sleep apnea classification

In this paper we propose the use of Kernel Principal Component Regression (KPCR) in order to model the nonlinear interaction between heart rate (HR) and respiration. We used wavelets in order to decompose the respiratory signal in 2 different frequency bands; namely, the low frequency band (LF) 0-0....

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Autores:

Tipo de recurso:

Fecha de publicación:

2014

Institución:

Universidad del Rosario

Repositorio:

Repositorio EdocUR - U. Rosario

Idioma:

eng

OAI Identifier:

oai:repository.urosario.edu.co:10336/28432

Acceso en línea:

https://repository.urosario.edu.co/handle/10336/28432

Palabra clave:

Computational modeling
Heart rate
Matrix decomposition
Abstracts
Monitoring
Biomedical monitoring
Couplings

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