Introduction
Intracranial Pressure (ICP) can be continuously and reliably measured using invasive monitoring through an external ventricular catheter or an intraparenchymal probe. We explore Electroencephelograhy (EEG) to identify a reliable real time, non-invasive ICP correlate.
Methods
Utilizing a previously described porcine model of intracranial hypertension, we examine the cross correlation between ICP time series and the slope of the EEG power spectral density as described by Φ. We calculate Φ= tan−1(slope of PSD) and normalized it by π where slope is that of the power-law fit (log frequency versus log power) to the power spectral density of the EEG signal. Additionally, we explore the relationship between the Φ time series and cerebral perfusion pressure (CPP). A total of 11 intracranial hypertension episodes across three different animals are studied.
Results
Mean correlation between Φ-angle and ICP was -0.85 (0.15); mean correlation with CPP 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).
Conclusion
EEG Φ-angle is a promising real-time noninvasive measure of ICP/cerebral perfusion using surface electroencephalography. While intra-species variation is presumably minimal, validation in human subjects is needed.
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.
Las coinfecciones entre SARS-CoV-2 y otros patógenos son una cuestión importante para el tratamiento de los pacientes con COVID-19. Las infecciones por
Aspergillus
forman parte de esta consideración, ya que presentan elevada morbilidad y mortalidad. Presentamos el caso de un paciente con coinfección de COVID-19 y
Aspergillus fumigatus
que evolucionó a muerte cerebral debido a múltiples lesiones heterogéneas en el cerebro donde, tras biopsia post-mortem, se encontraron lesiones patológicas compatibles con
Aspergillus
.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.