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Using near-infrared spectroscopy and a random forest regressor to estimate intracranial pressure.
Relander, Filip A J; Ruesch, Alexander; Yang, Jason; Acharya, Deepshikha; Scammon, Bradley; Schmitt, Samantha; Crane, Emily C; Smith, Matthew A; Kainerstorfer, Jana M.
Afiliación
  • Relander FAJ; Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States.
  • Ruesch A; Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States.
  • Yang J; Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States.
  • Acharya D; Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States.
  • Scammon B; Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States.
  • Schmitt S; Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States.
  • Crane EC; Carnegie Mellon University, Neuroscience Institute, Pittsburgh, Pennsylvania, United States.
  • Smith MA; Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States.
  • Kainerstorfer JM; Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States.
Neurophotonics ; 9(4): 045001, 2022 Oct.
Article en En | MEDLINE | ID: mdl-36247716
Significance: Intracranial pressure (ICP) measurements are important for patient treatment but are invasive and prone to complications. Noninvasive ICP monitoring methods exist, but they suffer from poor accuracy, lack of generalizability, or high cost. Aim: We previously showed that cerebral blood flow (CBF) cardiac waveforms measured with diffuse correlation spectroscopy can be used for noninvasive ICP monitoring. Here we extend the approach to cardiac waveforms measured with near-infrared spectroscopy (NIRS). Approach: Changes in hemoglobin concentrations were measured in eight nonhuman primates, in addition to invasive ICP, arterial blood pressure, and CBF changes. Features of average cardiac waveforms in hemoglobin and CBF signals were used to train a random forest (RF) regressor. Results: The RF regressor achieves a cross-validated ICP estimation of 0.937 r 2 , 2.703 - mm Hg 2 mean squared error (MSE), and 95% confidence interval (CI) of [ - 3.064 3.160 ] mmHg on oxyhemoglobin concentration changes; 0.946 r 2 , 2.301 - mmHg 2 MSE, and 95% CI of [ - 2.841 2.866 ] mmHg on total hemoglobin concentration changes; and 0.963 r 2 , 1.688 mmHg 2 MSE, and 95% CI of [ - 2.450 2.397 ] mmHg on CBF changes. Conclusions: This study provides a proof of concept for the use of NIRS in noninvasive ICP estimation.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Clinical_trials Idioma: En Revista: Neurophotonics Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
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Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Clinical_trials Idioma: En Revista: Neurophotonics Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos