Cargando…
Physics-Informed Neural Networks for Modeling Physiological Time Series: A Case Study with Continuous Blood Pressure
The bold vision of AI-driven pervasive physiological monitoring, through the proliferation of off-the-shelf wearables that began a decade ago, has created immense opportunities to extract actionable information for precision medicine. These AI algorithms model the input-output relationships of a sys...
Autores principales: | Sel, Kaan, Mohammadi, Amirmohammad, Pettigrew, Roderic I., Jafari, Roozbeh |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Journal Experts
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9882661/ https://www.ncbi.nlm.nih.gov/pubmed/36711741 http://dx.doi.org/10.21203/rs.3.rs-2423200/v1 |
Ejemplares similares
-
Physics-informed neural networks for modeling physiological time series for cuffless blood pressure estimation
por: Sel, Kaan, et al.
Publicado: (2023) -
Continuous cuffless blood pressure monitoring with a wearable ring bioimpedance device
por: Sel, Kaan, et al.
Publicado: (2023) -
Magnetic field mapping of inaccessible regions using physics-informed neural networks
por: Coskun, Umit H., et al.
Publicado: (2022) -
An Accurate Bioimpedance Measurement System for Blood Pressure Monitoring
por: Huynh, Toan Huu, et al.
Publicado: (2018) -
Cuffless blood pressure monitoring from a wristband with calibration-free algorithms for sensing location based on bio-impedance sensor array and autoencoder
por: Ibrahim, Bassem, et al.
Publicado: (2022)