Does breathing pattern affect cerebrovascular reactivity?

2021 ◽  
Author(s):  
Ece Su Sayin ◽  
Anahis Davidian ◽  
Harrison Levine ◽  
Lashmi Venkatraghavan ◽  
David J. Mikulis ◽  
...  
Keyword(s):  
Breathing Pattern ◽  
Cerebrovascular Reactivity

Artifact removal from neurophysiological signals: impact on intracranial and arterial pressure monitoring in traumatic brain injury

2020 ◽  
Vol 132 (6) ◽  
pp. 1952-1960 ◽  
Author(s):  
Seung-Bo Lee ◽  
Hakseung Kim ◽  
Young-Tak Kim ◽  
Frederick A. Zeiler ◽  
Peter Smielewski ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Neurological Status ◽  
Cerebrovascular Reactivity ◽  
Cerebral Hypoperfusion ◽  
Clinical Parameters ◽  
Artifact Removal ◽  
Clinical Events ◽  
Arterial Blood ◽  
Before And After

OBJECTIVEMonitoring intracranial and arterial blood pressure (ICP and ABP, respectively) provides crucial information regarding the neurological status of patients with traumatic brain injury (TBI). However, these signals are often heavily affected by artifacts, which may significantly reduce the reliability of the clinical determinations derived from the signals. The goal of this work was to eliminate signal artifacts from continuous ICP and ABP monitoring via deep learning techniques and to assess the changes in the prognostic capacities of clinical parameters after artifact elimination.METHODSThe first 24 hours of monitoring ICP and ABP in a total of 309 patients with TBI was retrospectively analyzed. An artifact elimination model for ICP and ABP was constructed via a stacked convolutional autoencoder (SCAE) and convolutional neural network (CNN) with 10-fold cross-validation tests. The prevalence and prognostic capacity of ICP- and ABP-related clinical events were compared before and after artifact elimination.RESULTSThe proposed SCAE-CNN model exhibited reliable accuracy in eliminating ABP and ICP artifacts (net prediction rates of 97% and 94%, respectively). The prevalence of ICP- and ABP-related clinical events (i.e., systemic hypotension, intracranial hypertension, cerebral hypoperfusion, and poor cerebrovascular reactivity) all decreased significantly after artifact removal.CONCLUSIONSThe SCAE-CNN model can be reliably used to eliminate artifacts, which significantly improves the reliability and efficacy of ICP- and ABP-derived clinical parameters for prognostic determinations after TBI.

P211 Comorbidity between asthma, inducible laryngeal obstruction and breathing pattern disorder

2019 ◽  
Author(s):  
C Slinger ◽  
H Wilson ◽  
A Vyas ◽  
R Slinger
Keyword(s):  
Breathing Pattern ◽  
Laryngeal Obstruction

scholarly journals A novel method of quantifying hemodynamic delays to improve hemodynamic response, and CVR estimates in CO2 challenge fMRI

2021 ◽  
pp. 0271678X2097858
Author(s):  
Jinxia (Fiona) Yao ◽  
Ho-Ching (Shawn) Yang ◽  
James H Wang ◽  
Zhenhu Liang ◽  
Thomas M Talavage ◽  
...  
Keyword(s):  
Arrival Time ◽  
Stress Test ◽  
Low Frequency ◽  
Hemodynamic Response ◽  
Blood Oxygen Level Dependent ◽  
Cerebrovascular Reactivity ◽  
Bold Signal ◽  
Hemodynamic Response Function ◽  
Carbon Dioxide Co2 ◽  
Co2 Challenge

Elevated carbon dioxide (CO2) in breathing air is widely used as a vasoactive stimulus to assess cerebrovascular functions under hypercapnia (i.e., “stress test” for the brain). Blood-oxygen-level-dependent (BOLD) is a contrast mechanism used in functional magnetic resonance imaging (fMRI). BOLD is used to study CO2-induced cerebrovascular reactivity (CVR), which is defined as the voxel-wise percentage BOLD signal change per mmHg change in the arterial partial pressure of CO2 (PaCO2). Besides the CVR, two additional important parameters reflecting the cerebrovascular functions are the arrival time of arterial CO2 at each voxel, and the waveform of the local BOLD signal. In this study, we developed a novel analytical method to accurately calculate the arrival time of elevated CO2 at each voxel using the systemic low frequency oscillations (sLFO: 0.01-0.1 Hz) extracted from the CO2 challenge data. In addition, 26 candidate hemodynamic response functions (HRF) were used to quantitatively describe the temporal brain reactions to a CO2 stimulus. We demonstrated that our approach improved the traditional method by allowing us to accurately map three perfusion-related parameters: the relative arrival time of blood, the hemodynamic response function, and CVR during a CO2 challenge.

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