The intracranial Windkessel implies arteriovenous pulsatile coupling increased by venous resistances

Baselli, Giuseppe; Laganà, Maria Marcella

doi:10.1016/j.bspc.2021.103092

Cited by 5 publications

(3 citation statements)

References 47 publications

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“…Indeed, if the observed AF BBV was fully driven by the systemic arterial pressure BBV ( Figure 5 , green arrow at the arterial input) and next transferred via the IC capacitive coupling to the VF and the CSF (similar to the main pulse waves) similar BBV PSD patterns and LF/HF ratios should be seen in the three flow signals. In addition, a transfer of LF waves via the IC arterio–venous capacitive coupling [ 14 ] would be rather more sensitive to the systolic AF modulation, since the major role of the capacitive coupling is compensating the systolic arterial water-hammer effect. Further evidence supporting this hypothesis is the poor presence of LF waves in the CSFF series [ 27 ].…”

Section: Discussionmentioning

confidence: 99%

“…In addition, despite being beyond the scope of this work, it is worth mentioning a potential impact on hemodynamic modelling studies, i.e., the modelling of AF, VF, and CSFF and their interactions in the IC circulation [ 14 , 15 , 16 , 17 , 18 , 19 ]. In this field, moving from the fitting of single gated wave shapes to several sampled ones is expected to highly increase the statistical power of model fitting to be exploited by parametric identification.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Phase-Contrast MRI to Monitor Cervical Blood and Cerebrospinal Fluid Flow Beat-by-Beat Variability

et al. 2022

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Beat-by-beat variability (BBV) rhythms are observed in both cardiovascular (CV) and intracranial (IC) compartments, yet interactions between the two are not fully understood. Real-Time Phase-Contrast (RT-PC) MRI sequence was acquired for 30 healthy volunteers at 1st cervical level on a 3T scanner. The arterial (AF), venous (VF), and cerebrospinal fluid (CSF) flow (CSFF) were computed as velocity integrals over the internal carotid artery, internal jugular vein, and CSF. AF, VF, and CSFF signals were segmented in inspiration and expiration beats, to assess the respiration influence. Systolic and diastolic BBV, and heart period series underwent autoregressive power spectral density analysis, to evaluate the low-frequency (LF, Mayer waves) and high frequency (HF, respiratory waves) components. The diastolic VF had the largest BBV. LF power was high in the diastolic AF series, poor in all CSFF series. The pulse wave analyses revealed higher mean amplitude during inspiration. Findings suggests a possible role of LF modulation of IC resistances and propagation of HF waves from VF to AF and CCSF. PC-RT-MRI could provide new insight into the interaction between CV and IC regulation and pave the way for a detailed analysis of the cerebrovascular effects of varied respiration patterns due to exercise and rehabilitation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-Time Phase-Contrast MRI to Monitor Cervical Blood and Cerebrospinal Fluid Flow Beat-by-Beat Variability

et al. 2022

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“…To date, many methods have been developed for pulse wave analysis for extracting cardiovascular information, including blood pressure assessed directly on the waveform with some initiate calibration. A variety of analysis methods are derived based on the basic concepts of wave reflection [5] or the arterial Windkessel model [6], which provide some parameters such as augmentation index (AIx) and vascular compliance to describe the cardiovascular system [7][8][9][10][11]. The computation involved is more dependent on the location accuracy of feature points.…”

Section: Introductionmentioning

confidence: 99%

Pulse Wave Analysis Method of Cardiovascular Parameters Extraction for Health Monitoring

Jin

Geng

Zhang

et al. 2023

IJERPH

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Objective: A pulse waveform is regarded as an information carrier of the cardiovascular system, which contains multiple interactive cardiovascular parameters reflecting physio-pathological states of bodies. Hence, multiple parameter analysis is increasingly meaningful to date but still cannot be easily achieved one by one due to the complex mapping between waveforms. This paper describes a new analysis method based on waveform recognition aimed for extracting multiple cardiovascular parameters to monitor public health. The objective of this new method is to deduce multiple cardiovascular parameters for a target pulse waveform based on waveform recognition to a most similar reference waveform in a given database or pattern library. Methods: The first part of the methodology includes building the sub-pattern libraries and training classifier. This provides a trained classifier and the sub-pattern library with reference pulse waveforms and known parameters. The second part is waveform analysis. The target waveform will be classified and output a state category being used to select the corresponding sub-pattern library with the same state. This will reduce subsequent recognition scope and computation costs. The mainstay of this new analysis method is improved dynamic time warping (DTW). This improved DTW and K-Nearest Neighbors (KNN) were applied to recognize the most similar waveform in the pattern library. Hence, cardiovascular parameters can be assigned accordingly from the most similar waveform in the pattern library. Results: Four hundred and thirty eight (438) randomly selected pulse waveforms were tested to verify the effectiveness of this method. The results show that the classification accuracy is 96.35%. Using statistical analysis to compare the target sample waveforms and the recognized reference ones from within the pattern library, most correlation coefficients are beyond 0.99. Each set of cardiovascular parameters was assessed using the Bland-Altman plot. The extracted cardiovascular parameters are in strong agreement with the original verifying the effectiveness of this new approach. Conclusion: This new method using waveform recognition shows promising results that can directly extract multiple cardiovascular parameters from waveforms with high accuracy. This new approach is efficient and effective and is very promising for future continuous monitoring of cardiovascular health.

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The pulse waveform quantification method basing on contour and derivative

Jin

Zhang

Geng

et al. 2022

Computer Methods and Programs in Biomedicine

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The intracranial Windkessel implies arteriovenous pulsatile coupling increased by venous resistances

Cited by 5 publications

References 47 publications

Real-Time Phase-Contrast MRI to Monitor Cervical Blood and Cerebrospinal Fluid Flow Beat-by-Beat Variability

Real-Time Phase-Contrast MRI to Monitor Cervical Blood and Cerebrospinal Fluid Flow Beat-by-Beat Variability

Pulse Wave Analysis Method of Cardiovascular Parameters Extraction for Health Monitoring

The pulse waveform quantification method basing on contour and derivative

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