Using EEG total energy as a noninvasively tracking of intracranial (and cerebral perfussion) pressure in an animal model: A pilot study.

PURPOSE: This study aims to describe the total EEG energy during episodes of intracranial hypertension (IH) and evaluate its potential as a classification feature for IH. NEW METHODS: We computed the sample correlation coefficient between intracranial pressure (ICP) and the total EEG energy. Additionally, a generalized additive model was employed to assess the relationship between arterial blood pressure (ABP), total EEG energy, and the odds of IH. RESULTS: The median sample cross-correlation between total EEG energy and ICP was 0.7, and for cerebral perfusion pressure (CPP) was 0.55. Moreover, the proposed model exhibited an accuracy of 0.70, sensitivity of 0.53, specificity of 0.79, precision of 0.54, F1-score of 0.54, and an AUC of 0.7. COMPARISON WITH EXISTING METHODS: The only existing comparable methods, up to our knowledge, use 13 variables as predictor of IH, our model uses only 3, our model, as it is an extension of the generalized model is interpretable and it achieves the same performance. CONCLUSION: These findings hold promise for the advancement of multimodal monitoring systems in neurocritical care and the development of a non-invasive ICP monitoring tool, particularly in resource-constrained environments.

Novel EEG Metric Correlates with Intracranial Pressure in an Animal Model.

BACKGROUND: Intracranial pressure (ICP) can be continuously and reliably measured using invasive monitoring through an external ventricular catheter or an intraparenchymal probe. We explore electroencephalography (EEG) to identify a reliable real-time noninvasive ICP correlate. METHODS: Using a previously described porcine model of intracranial hypertension, we examined the cross correlation between ICP time series and the slope of the EEG power spectral density as described by ϕ. We calculated ϕ as tan-1 (slope of power spectral density) and normalized it by π, where slope is that of the power-law fit (log frequency vs. log power) to the power spectral density of the EEG signal. Additionally, we explored the relationship between the ϕ time series and cerebral perfusion pressure. A total of 11 intracranial hypertension episodes across three different animals were studied. RESULTS: The mean correlation between ϕ angle and ICP was - 0.85 (0.15); the mean correlation with cerebral perfusion pressure was 0.92 (0.02). Significant correlation occurred at zero lag. In the absence of intracranial hypertension, the absolute value of the ϕ angle was greater than 0.9 (mean 0.936 radians). However, during extreme intracranial hypertension causing cerebral circulatory arrest, the ϕ angle is on average below 0.9 radians (mean 0.855 radians). CONCLUSIONS: EEG ϕ angle is a promising real-time noninvasive measure of ICP/cerebral perfusion using surface electroencephalography.

Permutation Entropy Analysis to Intracranial Hypertension from a Porcine Model.

Intracranial pressure (ICP) monitoring is commonly used in the follow-up of patients in intensive care units, but only a small part of the information available in the ICP time series is exploited. One of the most important features to guide patient follow-up and treatment is intracranial compliance. We propose using permutation entropy (PE) as a method to extract non-obvious information from the ICP curve. We analyzed the results of a pig experiment with sliding windows of 3600 samples and 1000 displacement samples, and estimated their respective PEs, their associated probability distributions, and the number of missing patterns (NMP). We observed that the behavior of PE is inverse to that of ICP, in addition to the fact that NMP appears as a surrogate for intracranial compliance. In lesion-free periods, PE is usually greater than 0.3, and normalized NMP is less than 90% and p(s1)>p(s720). Any deviation from these values could be a possible warning of altered neurophysiology. In the terminal phases of the lesion, the normalized NMP is higher than 95%, and PE is not sensitive to changes in ICP and p(s720)>p(s1). The results show that it could be used for real-time patient monitoring or as input for a machine learning tool.

Reversible focal intracranial hypertension swine model with continuous multimodal neuromonitoring.

BACKGROUND: Intracranial hypertension (HI) is associated with worse neurological outcomes and higher mortality. Although there are several experimental models of HI, in this article we present a reproducible, reversible, and reliable model of intracranial hypertension, with continuous multimodal monitoring. NEW METHOD: A reversible intracranial hypertension model in swine with multimodal monitoring including intracranial pressure, arterial blood pressure, heart rate variation, brain tissue oxygenation, and electroencephalogram is developed to understand the relationship of ICP and EEG. By inflating and deflating a balloon, located 20 mm anterior to the coronal suture and a 15 mm sagittal suture, we generate intracranial hypertension events and simultaneously measure intracranial pressure and oxygenation in the contralateral hemisphere and the EEG, simulating the usual configuration in humans. RESULTS: We completed 5 experiments and in all of them, we were able to complete at least 6 events of intracranial hypertension in a stable and safe way. For events of 20-40 mmHg of ICP we need an median (IQR) of 4.2 (3.64) ml of saline solution into the Foley balloon, a median (IQR) infusion time of 226 (185) second in each event and for events of 40-50 mmHg of ICP we need a median (IQR) of 5.1 (4.66) ml of saline solution, a median (IQR) infusion time of 280 (48) seconds and a median (IQR). The median (IQR) maintenance time was 352 (77) seconds and 392 (166) seconds for 20-40 mmHg and 40-50 mmHg of ICP, respectively. COMPARISON WITH EXISTING METHOD(S): Existing methods do not include EEG measures and do not present the reversibility of intracranial hypertension. CONCLUSIONS: Our model is fully reproducible, it is capable of generating reversible focal intracranial hypertension through strict control of the injected volume, it is possible to generate different infusion rates of the volume in the balloon, in order to generate different scenarios, the data obtained are sufficient to determine the brain complacency in real time. and useful for understanding the pathophysiology of ICP and the relationship between ICP (CPP) and EEG.

Heart rate variability follow-up during COVID-19 -a case report.

BACKGROUND: To detect an early increase in the inflammatory response might prove to be vital for mitigating the deleterious effects of the disease over time. CASES: A 52-year-old obese man with moderate asthma and hypertension, who developed COVID-19 and had moderate symptoms, used a wearable device to record heart rate variability (HRV) during his illness. He had low parasympathetic tone, which decreased daily until it reached almost 2 standard deviations (SD) below normal values at the end of the second week. His sympathetic tone increased from > 3 SD to > 5 SD. CONCLUSIONS: Conclusions: These findings suggest an altered modulation of the sympathetic and parasympathetic nervous systems in COVID-19, such that the sympathetic tone is augmented and the parasympathetic tone is reduced. Population norms of COVID-19 infections should be further studied over the short-term and using 24 h HRV measurements.

Bandt-Pompe symbolization dynamics for time series with tied values: A data-driven approach.

In 2002, Bandt and Pompe [Phys. Rev. Lett. 88, 174102 (2002)] introduced a successfully symbolic encoding scheme based on the ordinal relation between the amplitude of neighboring values of a given data sequence, from which the permutation entropy can be evaluated. Equalities in the analyzed sequence, for example, repeated equal values, deserve special attention and treatment as was shown recently by Zunino and co-workers [Phys. Lett. A 381, 1883 (2017)]. A significant number of equal values can give rise to false conclusions regarding the underlying temporal structures in practical contexts. In the present contribution, we review the different existing methodologies for treating time series with tied values by classifying them according to their different strategies. In addition, a novel data-driven imputation is presented that proves to outperform the existing methodologies and avoid the false conclusions pointed by Zunino and co-workers.

Analysis of ischaemic crisis using the informational causal entropy-complexity plane.

In the present work, an ischaemic process, mainly focused on the reperfusion stage, is studied using the informational causal entropy-complexity plane. Ischaemic wall behavior under this condition was analyzed through wall thickness and ventricular pressure variations, acquired during an obstructive flow maneuver performed on left coronary arteries of surgically instrumented animals. Basically, the induction of ischaemia depends on the temporary occlusion of left circumflex coronary artery (which supplies blood to the posterior left ventricular wall) that lasts for a few seconds. Normal perfusion of the wall was then reestablished while the anterior ventricular wall remained adequately perfused during the entire maneuver. The obtained results showed that system dynamics could be effectively described by entropy-complexity loops, in both abnormally and well perfused walls. These results could contribute to making an objective indicator of the recovery heart tissues after an ischaemic process, in a way to quantify the restoration of myocardial behavior after the supply of oxygen to the ventricular wall was suppressed for a brief period.

Big Data in the ICU: Experience in the Hospital Italiano de Buenos Aires.

Intensive care represents the critical care setting of a hospital, where fundamental, precise, and fast decisions have to be made. These decisions will affect the outcome of the patients in a matter of few hours. The knowledge of the therapeutic interventions applied in this setting is evolving, thus the perspective of Big Data may provide a new paradigm in the ICU. The conformation of a multidisciplinary team is essential to develop Big Data in the ICU.

Infrastructure for Big Data in the Intensive Care Unit.

The Big Data paradigm can be applied in intensive care unit, in order to improve the treatment of the patients, with the aim of customized decisions. This poster is about the infrastructure necessary to built a Big Data system for the ICU. Together with the infrastructure, the conformation of a multidisciplinary team is essential to develop Big Data to use in critical care medicine.