Paper
18 February 2017 Coherent hemodynamics spectroscopy: initial applications in the neurocritical care unit
Author Affiliations +
Abstract
We used coherent hemodynamics spectroscopy (CHS) and near-infrared spectroscopy (NIRS) to measure the absolute cerebral blood flow (CBF) and cerebral autoregulation efficiency of a patient with intraventricular hemorrhage in the neurocritical care unit. Mean arterial pressure oscillations were induced with cyclic thigh cuff inflations at a super-systolic pressure. The oscillations in oxyhemoglobin ([HbO2]) and deoxyhemoglobin ([Hb]) cerebral concentrations were used to compute CHS amplitude and phase spectra that were fit with the frequency-domain equations of our hemodynamic model. From the fitted parameters, we obtained measures of local autoregulation efficiency (cutoff frequency: 0.07 ± 0.02 Hz) and absolute regional CBF (33 ± 9 ml/100g/min). We introduce a new approach for computing CHS spectra using coherence criteria and time-varying transfer function analysis. We show that with this approach we can maximize the number of frequency points in the CHS spectra for more effective fitting with our hemodynamic model. Finally, we show how absolute measurements of the cerebral concentrations of [HbO2] and [Hb] at baseline can be used to further enhance the fitting procedure.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kristen T. Tgavalekos, Angelo Sassaroli, Xuemei Cai, Joshua Kornbluth, and Sergio Fantini "Coherent hemodynamics spectroscopy: initial applications in the neurocritical care unit", Proc. SPIE 10059, Optical Tomography and Spectroscopy of Tissue XII, 100591F (18 February 2017); https://doi.org/10.1117/12.2251021
Lens.org Logo
CITATIONS
Cited by 1 scholarly publication.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Hemodynamics

Near infrared spectroscopy

Blood pressure

Autoregressive models

Spectroscopy

Cerebral blood flow

Data modeling

Back to Top