A Data-Driven Approach to Transfer Function Analysis for Superior Discriminative Power: Optimized Assessment of Dynamic Cerebral Autoregulation

Test-retest reliability of transfer function analysis metrics for assessing dynamic cerebral autoregulation to spontaneous blood pressure oscillations

Transfer function analysis (TFA) is a widely used method for assessing dynamic cerebral autoregulation in humans. In the present study, we assessed the test-retest reliability of established TFA metrics derived from spontaneous blood pressure oscillations and based on 5 min recordings. The TFA-based gain, phase and coherence in the low-frequency range (0.07-0.20 Hz) from 19 healthy volunteers, 37 patients with subarachnoid haemorrhage and 19 patients with sepsis were included. Reliability assessments included the smallest real difference (SRD) and the coefficient of variance for comparing consecutive 5 min recordings, temporally separated 5 min recordings and consecutive recordings with a minimal length of 10 min. In healthy volunteers, temporally separating the 5 min recordings led to a 0.38 (0.01-0.79) cm s-1 mmHg-1 higher SRD for gain (P = 0.032), and extending the duration of recordings did not affect the reliability. In subarachnoid haemorrhage, temporal separation led to a 0.85 (-0.13 to 1.93) cm s-1 mmHg-1 higher SRD (P = 0.047) and a 20 (-2 to 41)% higher coefficient of variance (P = 0.038) for gain, but neither metric was affected by extending the recording duration. In sepsis, temporal separation increased the SRD for phase by 94 (23-160)° (P = 0.006) but was unaffected by extending the recording. A recording duration of 8 min was required to achieve stable gain and normalized gain measures in healthy individuals, and even longer recordings were required in patients. In conclusion, a recording duration of 5 min appears insufficient for obtaining stable and reliable TFA metrics when based on spontaneous blood pressure oscillations, particularly in critically ill patients with subarachnoid haemorrhage and sepsis.

Point-Counterpoint: Transfer function analysis of dynamic cerebral autoregulation: To band or not to band?

Transfer function analysis (TFA) is the most frequently adopted method for assessing dynamic cerebral autoregulation (CA) with continuously recorded arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV). Conventionally, values of autoregulatory metrics (e.g., gain and phase) derived from TFA are averaged within three frequency bands separated by cut-off frequencies at 0.07 Hz and 0.20 Hz, respectively, to represent the efficiency of dynamic CA. However, this is of increasing concerns, as there remains no solid evidence for choosing these specific cut-off frequencies, and the rigid adoption of these bands can stifle further developments in TFA of dynamic CA. In this 'Point-Counterpoint' mini-review, we provide evidence against the fixed banding, indicate possible alternatives, and call for awareness of the risk of the 'one-size-fits-all' banding becoming dogmatic. We conclude that we need to remain open to the multiple possibilities offered by TFA to realize its full potential in studies of human dynamic CA.

Transfer function analysis of dynamic cerebral autoregulation: A CARNet white paper 2022 update

Cerebral autoregulation (CA) refers to the control of cerebral tissue blood flow (CBF) in response to changes in perfusion pressure. Due to the challenges of measuring intracranial pressure, CA is often described as the relationship between mean arterial pressure (MAP) and CBF. Dynamic CA (dCA) can be assessed using multiple techniques, with transfer function analysis (TFA) being the most common. A 2016 white paper by members of an international Cerebrovascular Research Network (CARNet) that is focused on CA strove to improve TFA standardization by way of introducing data acquisition, analysis, and reporting guidelines. Since then, additional evidence has allowed for the improvement and refinement of the original recommendations, as well as for the inclusion of new guidelines to reflect recent advances in the field. This second edition of the white paper contains more robust, evidence-based recommendations, which have been expanded to address current streams of inquiry, including optimizing MAP variability, acquiring CBF estimates from alternative methods, estimating alternative dCA metrics, and incorporating dCA quantification into clinical trials. Implementation of these new and revised recommendations is important to improve the reliability and reproducibility of dCA studies, and to facilitate inter-institutional collaboration and the comparison of results between studies.

An Attention-based Wavelet Convolution Neural Network for Epilepsy EEG Classification

As a kind of non-invasive, low-cost, and readily available brain examination, EEG has attached significance to the means of clinical diagnosis of epilepsy. However, the reading of long-term EEG records has brought a heavy burden to neurologists and experts. Therefore, automatic EEG classification for epileptic patients plays an essential role in epilepsy diagnosis and treatment. This paper proposes an Attention Mechanism-based Wavelet Convolution Neural Network for epilepsy EEG classification. Attention Mechanism-based Wavelet Convolution Neural Network firstly uses multi-scale wavelet analysis to decompose the input EEGs to obtain their components in different frequency bands. Then, these decomposed multi-scale EEGs are input into the Convolution Neural Network with an attention mechanism for further feature extraction and classification. The proposed algorithm achieves 98.89% triple classification accuracy on the Bonn EEG database and 99.70% binary classification accuracy on the Bern-Barcelona EEG database. Our experiments prove that the proposed algorithm achieves a state-of-the-art classification effect on epilepsy EEG.

Improvement of exhausted cerebral autoregulation in patients with idiopathic intracranial hypertension benefit of venous sinus stenting

Objective.To evaluate the cerebral autoregulation (CA) in idiopathic intracranial hypertension (IIH) patients with transfer function analysis, and to explore its improvement after venous sinus stenting.Approach. In total, 15 consecutive IIH patients with venous sinus stenosis and 15 controls were recruited. All the patients underwent digital subtraction angiography and venous manometry. Venous sinus stenting was performed for IIH patients with a trans-stenosis pressure gradient ≥8 mmHg. CA was assessed before and after the operation with transfer function analysis, by using the spontaneous oscillations of the cerebral blood flow velocity in the bilateral middle cerebral artery and blood pressure.Main results. Compared with controls, the autoregulatory parameters, phase shift and rate of recovery, were both significantly lower in IIH patients [(57.94° ± 23.22° versus 34.59° ± 24.15°,p < 0.001; (39.87 ± 21.95) %/s versus (20.56 ± 46.66) %/s,p= 0.045, respectively). In total, six patients with bilateral transverse or sigmoid sinus stenosis received venous sinus stenting, in whom, the phase shift significantly improved after venous sinus stenting (39.62° ± 20.26° versus 22.79° ± 19.96°,p = 0.04).Significance. The study revealed that dynamic CA was impaired in IIH patients and was improved after venous sinus stenting. CA assessment has the potential to be used for investigating the hemodynamics in IIH patients.

Separation of normal and impaired dynamic cerebral autoregulation using deep embedded clustering: a proof-of-concept study

Objective. A previous study has shown that a data-driven approach can significantly improve the discriminative power of transfer function analysis (TFA) used to differentiate between normal and impaired cerebral autoregulation (CA) in two groups of data. The data was collected from both healthy subjects (assumed to have normal CA) and symptomatic patients with severe stenosis (assumed to have impaired CA). However, the sample size of the labeled data was relatively small, owing to the difficulty in data collection. Therefore, in this proof-of-concept study, we investigate the feasibility of using an unsupervised learning model to differentiate between normal and impaired CA on TFA variables without requiring labeled data for learning.Approach. Continuous arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV), which were recorded simultaneously for approximately 10 min, were included from 148 subjects (41 healthy subjects, 31 with mild stenosis, 13 with moderate stenosis, 22 asymptomatic patients with severe stenosis, and 41 symptomatic patients with severe stenosis). Tiecks' model was used to generate surrogate data with normal and impaired CA. A recently proposed unsupervised learning model was optimized and applied to separate the normal and impaired CA for both the surrogate data and real data.Main results. It achieved 98.9% and 74.1% accuracy for the surrogate and real data, respectively.Significance. To our knowledge, this is the first attempt to employ an unsupervised data-driven approach to assess CA using TFA. This method enables the development of a classifier to determine the status of CA, which is currently lacking.

Superior fitting of arterial resistance and compliance parameters with genetic algorithms in models of dynamic cerebral autoregulation

OBJECTIVE

The capacity of discriminating between normal and impaired dynamic cerebral autoregulation (dCA), based on spontaneous fluctuations in arterial blood pressure (ABP) and cerebral blood flow (CBF), has considerable clinical relevance. This study aimed to quantify the separate contributions of vascular resistance and compliance as parameters that could reflect myogenic and metabolic mechanisms to dCA.

METHODS

Forty-five subjects were studied under normo and hypercapnic conditions induced by breathing a mixture of 5% carbon dioxide in air. Dynamic cerebrovascular resistance and compliance models with ABP as input and CBFV as output were fitted using Genetic Algorithms to identify parameter values for each subject, and respiratory condition.

RESULTS

The efficiency of dCA was assessed from the models generated CBFV response to an ABP step change, corresponding to an autoregulation index of 5.561.57 in normocapnia and 2.381.73 in hypercapnia, with an area under the ROC curve (AUC) of 0.9 between both conditions. Vascular compliance increased from 0.750.7 ml/mmHg in normocapnia to 5.8212.0 ml/mmHg during hypercapnia, with an AUC of 0.88.

CONCLUSION

we demonstrated that Genetic Algorithms are a powerful tool to provide accurate identification of model parameters expressing the performance of human CA Significance: Further work is needed to validate this approach in clinical applications where individualised model parameters could provide relevant diagnostic and prognostic information about dCA impairment Index Terms arterial compliance, autoregulation impairment, cerebral blood flow, Genetic Algorithms, hypercapnia.