Forehead electrodes sufficiently detect propofol-induced slow waves for the assessment of brain function after cardiac arrest

Value of EEG in outcome prediction of hypoxic-ischemic brain injury in the ICU: A narrative review

Prognostication of comatose patients after cardiac arrest aims to identify patients with a large probability of favourable or unfavouble outcome, usually within the first week after the event. Electroencephalography (EEG) is a technique that is increasingly used for this purpose and has many advantages, such as its non-invasive nature and the possibility to monitor the evolution of brain function over time. At the same time, use of EEG in a critical care environment faces a number of challenges. This narrative review describes the current role and future applications of EEG for outcome prediction of comatose patients with postanoxic encephalopathy.

Monitoring of nighttime EEG slow-wave activity during dexmedetomidine infusion in patients with hyperactive ICU delirium: An observational pilot study

BACKGROUND

The disturbance of sleep has been associated with intensive care unit (ICU) delirium. Monitoring of EEG slow-wave activity (SWA) has potential in measuring sleep quality and quantity. We investigated the quantitative monitoring of nighttime SWA and its association with the clinical evaluation of sleep in patients with hyperactive ICU delirium treated with dexmedetomidine.

METHODS

We performed overnight EEG recordings in 15 patients diagnosed with hyperactive delirium during moderate dexmedetomidine sedation. SWA was evaluated by offline calculation of the C-Trend Index, describing SWA in one parameter ranging 0 to 100 in values. Average and percentage of SWA values <50 were categorized as poor. The sleep quality and depth was clinically evaluated by the bedside nurse using the Richards-Campbell Sleep Questionnaire (RCSQ) with scores <70 categorized as poor.

RESULTS

Nighttime SWA revealed individual sleep structures and fundamental variation between patients. SWA was poor in 67%, sleep quality (RCSQ) in 67%, and sleep depth (RCSQ) in 60% of the patients. The category of SWA aligned with that of RCSQ-based sleep quality in 87% and RCSQ-based sleep depth in 67% of the patients.

CONCLUSION

Both, SWA and clinical evaluation suggested that the quality and depth of nighttime sleep were poor in most patients with hyperactive delirium despite dexmedetomidine infusion. Furthermore, the SWA and clinical evaluation classifications were not uniformly in agreement. An objective mode such as practical EEG-based solution for sleep evaluation and individual drug dosing in the ICU setting could offer potential in improving sleep for patients with delirium.

Early recovery of frontal EEG slow wave activity during propofol sedation predicts outcome after cardiac arrest

AIM OF THE STUDY

EEG slow wave activity (SWA) has shown prognostic potential in post-resuscitation care. In this prospective study, we investigated the accuracy of continuously measured early SWA for prediction of the outcome in comatose cardiac arrest (CA) survivors.

METHODS

We recorded EEG with a disposable self-adhesive frontal electrode and wireless device continuously starting from ICU admission until 48 h from return of spontaneous circulation (ROSC) in comatose CA survivors sedated with propofol. We determined SWA by offline calculation of C-Trend® Index describing SWA as a score ranging from 0 to 100. The functional outcome was defined based on Cerebral Performance Category (CPC) at 6 months after the CA to either good (CPC 1-2) or poor (CPC 3-5).

RESULTS

Outcome at six months was good in 67 of the 93 patients. During the first 12 h after ROSC, the median C-Trend Index value was 38.8 (interquartile range 28.0-56.1) in patients with good outcome and 6.49 (3.01-18.2) in those with poor outcome showing significant difference (p < 0.001) at every hour between the groups. The index values of the first 12 h predicted poor outcome with an area under curve of 0.86 (95% CI 0.61-0.99). With a cutoff value of 20, the sensitivity was 83.3% (69.6%-92.3%) and specificity 94.7% (83.4%-99.7%) for categorization of outcome.

CONCLUSION

EEG SWA measured with C-Trend Index during propofol sedation offers a promising practical approach for early bedside evaluation of recovery of brain function and prediction of outcome after CA.

A Correlation-Driven Mapping For Deep Learning application in detecting artifacts within the EEG

OBJECTIVE

When developing approaches for automatic preprocessing of electroencephalogram (EEG) signals in non-isolated demanding environment such as intensive care unit (ICU) or even outdoor environment, one of the major concerns is varying nature of characteristics of different artifacts in time, frequency and spatial domains, which in turn causes a simple approach to be not enough for reliable artifact removal. Considering this, current study aims to use correlation-driven mapping to improve artifact detection performance.

APPROACH

A framework is proposed here for mapping signals from multichannel space (regardless of the number of EEG channels) into two-dimensional RGB space, in which the correlation of all EEG channels is simultaneously taken into account, and a deep convolutional neural network (CNN) model can then learn specific patterns in generated 2D representation related to specific artifact.

MAIN RESULTS

The method with a classification accuracy of 92.30% (AUC = 0.96) in a leave-three-subjects-out cross-validation procedure was evaluated using data including 2310 EEG sequences contaminated by artifacts and 2285 artifact-free EEG sequences collected with BrainStatus self-adhesive electrode and wireless amplifier from 15 intensive care patients. For further assessment, several scenarios were also tested including performance variation of proposed method under different segment lengths, different numbers of isoline and different numbers of channel. The results showed outperformance of CNN fed by correlation coefficients data over both spectrogram-based CNN and EEGNet on the same dataset.

SIGNIFICANCE

This study showed the feasibility of utilizing correlation image of EEG channels coupled with deep learning as a promising tool for dimensionality reduction, channels fusion and capturing various artifacts patterns in temporal-spatial domains. A simplified version of proposed approach was also shown to be feasible in real-time application with latency of 0.0181 s for making real-time decision.

Effect of sleep deprivation on general anesthesia in rats

OBJECTIVE

To explore the effects of sleep deprivation on perioperative general anesthesia in rats.

METHODS

45 healthy male Sprague-Dawley (SD) rats were randomly divided into 3 groups, the control group (Group A), the anesthesia group (Group B) and the sleep deprivation anesthesia group (Group C), 15 in each group. The sleep deprivation model was established by improving multi-platform water environment method. The group B and C were received propofol 80 mg/kg by intraperitoneally, the group A was given the same dose of normal saline. The EEG in each group was measured. The GABAa R-β3 protein in cerebral cortex was detected by Western Blot. The rats were treated with Brennan incision, and the changes of thermal pain sensitive (PWL) and open field behavior were measured in each group.

RESULTS

In group C, the δ band of brainwave of EEG increased significantly, the disappearance time of righting reflex shortened significantly, the recovery time prolonged significantly, the GABAa R-β3 protein was significantly increased, and the time of passing through the central area before operation was significantly decreased.

CONCLUSION

Sleep deprivation can significantly inhibit the electrical activity of rat cerebral cortex induced by propofol, up-regulating the GABAa R-β3 protein in cortex.