Heart Rate Modeling and Prediction Using Autoregressive Models and Deep Learning

Physiological time series are affected by many factors, making them highly nonlinear and nonstationary. As a consequence, heart rate time series are often considered difficult to predict and handle. However, heart rate behavior can indicate underlying cardiovascular and respiratory diseases as well...

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Detalles Bibliográficos
Autores principales: Staffini, Alessio, Svensson, Thomas, Chung, Ung-il, Svensson, Akiko Kishi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8747593/
https://www.ncbi.nlm.nih.gov/pubmed/35009581
http://dx.doi.org/10.3390/s22010034

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8747593/
https://www.ncbi.nlm.nih.gov/pubmed/35009581
http://dx.doi.org/10.3390/s22010034

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