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.

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.

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.

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.

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.

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

In 2002, Bandt and Pompe [Phys. Rev. Lett.

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.

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.