2018
DOI: 10.1371/journal.pone.0191825
|View full text |Cite
|
Sign up to set email alerts
|

Non-linear models for the detection of impaired cerebral blood flow autoregulation

Abstract: The ability to discriminate between normal and impaired dynamic cerebral autoregulation (CA), based on measurements of spontaneous fluctuations in arterial blood pressure (BP) and cerebral blood flow (CBF), has considerable clinical relevance. We studied 45 normal subjects at rest and under hypercapnia induced by breathing a mixture of carbon dioxide and air. Non-linear models with BP as input and CBF velocity (CBFV) as output, were implemented with support vector machines (SVM) using separate recordings for l… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

1
14
1

Year Published

2020
2020
2024
2024

Publication Types

Select...
5
1

Relationship

2
4

Authors

Journals

citations
Cited by 8 publications
(16 citation statements)
references
References 34 publications
1
14
1
Order By: Relevance
“…In this study, hypercapnia led to significant depression of dCA (figures 2(C) and (D)), as well as highly significant values of AUC, when compared to the null hypothesis of AUC = 0.5 (figure 4). Nevertheless, the values of AUC we obtained were lower than corresponding values in the literature, but those involved different physiological conditions (Katsogridakis et al 2013), or more complex mathematical models (Chacon et al 2018).…”
Section: Discussioncontrasting
confidence: 87%
See 1 more Smart Citation
“…In this study, hypercapnia led to significant depression of dCA (figures 2(C) and (D)), as well as highly significant values of AUC, when compared to the null hypothesis of AUC = 0.5 (figure 4). Nevertheless, the values of AUC we obtained were lower than corresponding values in the literature, but those involved different physiological conditions (Katsogridakis et al 2013), or more complex mathematical models (Chacon et al 2018).…”
Section: Discussioncontrasting
confidence: 87%
“…As mentioned above, this assumption is not acceptable for frequencies below approximately 0.15 Hz because an active CA implies that cerebrovascular resistance is changing over time, thus representing a departure from the premise of linearity (Bendat and Piersol 1986). Although non-linear models have been proposed to address this inherent limitation of TFA (Chacon et al 2018), the jury is still out to determine the benefits of using these models in clinical applications, and the key differences that would result in comparison with classical TFA.…”
Section: Limitations Of the Studymentioning
confidence: 99%
“…The ROC analysis in our study revealed that a cut-off value of 4.0 had the best sensitivity and specificity for predicting response to therapy and is in agreement with previous publications suggesting an ARI < 4 to define impaired CA 22,23 . This cut-off value is a new finding in a stroke population, and should be further investigated and replicated in larger studies to be implemented as a valid assessment tool for detection of CA impairment.…”
Section: Discussionsupporting
confidence: 91%
“…In addition to the statistical time-series analysis techniques, there are various machine learning algorithms used for cerebral physiologic signal modeling [ 10 , 11 ] as well as for the prediction task [ 12 ], including models such as linear regression, artificial neural networks (ANNs), convolutional neural networks (CNNs), extreme gradient boosting (XGBoost), and decision trees. These algorithms offer adaptability and data-driven capabilities that can uncover intricate patterns within the data, particularly in cases where complexities demand more flexible modeling approaches [ 13 ]. The juxtaposition of these two approaches, i.e., statistical time-series analysis techniques (leveraging frequency or time domain methods) and machine learning algorithms, presents a compelling landscape for the temporal analysis and forecasting of cerebral pressure–flow physiologic signals.…”
Section: Introductionmentioning
confidence: 99%