Tackling the Waves of COVID-19: A Planning Model for Intrahospital Resource Allocation.

The current pandemic requires hospitals to ensure care not only for the growing number of COVID-19 patients but also regular patients. Hospital resources must be allocated accordingly. To provide hospitals with a planning model to optimally allocate resources to intensive care units given a certain incidence of COVID-19 cases.

General anesthesia increases temporal precision and decreases power of the brainstem auditory-evoked response-related segments of the electroencephalogram.

Background: Brainstem auditory-evoked responses (BAEP) have been reported to be unchanged in the presence of drugs used for induction and maintenance of general anesthesia. The aim of this study was to investigate if the signal segments after the auditory stimulus that are used to average the evoked response change under the influence of general anesthesia.

Methods: BAEPs of 156 patients scheduled for elective surgery under general anesthesia were investigated.

EEG under anesthesia--feature extraction with TESPAR.

We investigated the problem of automatic depth of anesthesia (DOA) estimation from electroencephalogram (EEG) recordings. We employed Time Encoded Signal Processing And Recognition (TESPAR), a time-domain signal processing technique, in combination with multi-layer perceptrons to identify DOA levels. The presented system learns to discriminate between five DOA classes assessed by human experts whose judgements were based on EEG mid-latency auditory evoked potentials (MLAEPs) and clinical observations.

The discriminant power of simultaneous monitoring of spontaneous electroencephalogram and evoked potentials as a predictor of different clinical states of general anesthesia.

Spontaneous or evoked electrical brain activity is increasingly used to monitor general anesthesia. Previous studies investigated the variables from spontaneous electroencephalogram (EEG), acoustic (AEP), or somatosensory evoked potentials (SSEP). But, by monitoring them separately, the available information from simultaneous gathering could be missed.

The influence of wavelets on multiscale analysis and parametrization of midlatency auditory evoked potentials.

This work shows methodological aspects of heuristic pattern recognition in auditory evoked potentials. A linear and a nonlinear transformation based on wavelet transform are presented. They result in a statistical error model and an entropy function related to the Gibbs function and describe changes in midlatency auditory evoked potentials induced by general anaesthesia.

High-frequency components of auditory evoked potentials are detected in responsive but not in unconscious patients.

Background: The dose-dependent suppression of midlatency auditory evoked potentials by general anesthetics has been proposed to measure depth of anesthesia. In this study, perioperatively recorded midlatency auditory evoked potentials were analyzed in a time-frequency space to identify significant changes induced by general anesthesia.

Methods: Perioperatively recorded auditory evoked potentials of 19 patients, recorded at varying levels of anesthesia, were submitted to a multiscale analysis using the wavelet analysis.