Continuous EEG monitoring by a new simplified wireless headset in intensive care unit

Sensitivity of detecting interictal epileptiform activity using rapid reduced montage EEG

OBJECTIVE

Rapid EEG devices (REDs) have demonstrated substantial benefit regarding reduced time to performance of study and diagnosis in cases where urgent EEG is needed to evaluate patients for potentially revealing nonconvulsive status epilepticus and seizures. However, urgent EEG is also important in identifying cases regarding the need for initiation of antiseizure medication as well as triaging the use of continuous EEG monitoring. Some forms of REDs have a reduced montage (RRME) with electrode derivations that are one-half of standard recordings. This could impact spatial resolution and therefore potentially limit recovery of epileptiform abnormalities.

METHODS

In this study we evaluated the use of the Ceribell® rapid response EEG system and compared it to conventional video EEG (CvEEG). After applying inclusion and exclusion criteria, a total of 20 subjects were included in our analysis.

RESULTS

RRME was highly sensitive in detecting abundant and periodic discharges (p = 0.013) as well as discharges with a broad spatial distribution on CvEEG (p = 0.039). Sensitivity for detecting less prevalent discharges or those with more restricted spatial distribution was lower.

SIGNIFICANCE

Given the possibility of less frequent and more restricted epileptiform discharges eluding detection on RRME, we propose a protocol for the approach of using RRME and when to consider CvEEG when RRME is negative for epileptiform activity and highlight that urgent CvEEG may still be warranted following RRME.

Evaluation of simplified wireless EEG recordings in the neurological emergency room

OBJECTIVE

In the neurological emergency room (nER), timely electroencephalography (EEG) diagnostic is often crucial in patients with altered state of consciousness as well as in patients presenting with a first seizure. Yet, routine-EEG (rEEG) is often not available, especially during off-hours.

METHODS

We analyzed the value of a commercially available, simplified wireless eight-channel EEG recording (swEEG, CerebAir® EEG headset, Nihon Kohden), applied by non-EEG-specialized medical students, in patients presenting in our nER with (suspicion of) epileptic seizures and/or loss of or altered state of consciousness between 08/2019 and 08/2022. We evaluated the feasibility and validity compared to a standard rEEG (21 electrodes according to the international 10/20 system) and also included the clinical follow-up of the patients.

RESULTS

100 patients were included in our analysis (mean age 57.6 ± 20.4 years; 61 male). Median time of electrode application was 7 minutes (range 4-20 minutes), with significantly longer duration in patients with altered level of consciousness (median 8 minutes, p = 0.035). Electrode impedances also differed according to state of consciousness (p = 0.032), and were higher in females (p<0.001). 55 patients received additional rEEG, either during their acute nER stay (25) and/or during the next days (38). Considering normal EEG findings vs. pathological slowing vs. epileptiform activity, swEEG matched first rEEG results in 48/55 cases (87.3%). Overall, swEEG detected the same or additional pathological EEG patterns in 52/55 cases (94.5%). In 7/75 patients (9.3%) who did not receive rEEG, or had their rEEG scheduled to a later time point during their hospital stay, swEEG revealed important additional pathological findings (e.g. status epilepticus, interictal epileptiform discharges), which would have triggered acute therapeutic consequences or led to further diagnostics and investigations.

CONCLUSION

The introduced swEEG represents a practicable, valuable technique to be quickly applied by non-EEG-specialized ER staff to initiate timely diagnostic and guide further investigations and treatment in the nER. Moreover, it may help to avoid under-diagnostic with potentially harmful consequences caused by skipped or postponed regular 10/20 EEG examinations, and ultimately improve the outcome of patients.

Safety, feasibility, and acceptability of a novel device to monitor ischaemic stroke patients

This study assessed the safety, feasibility, and acceptability of a novel device to monitor ischaemic stroke patients. The device captured electroencephalography (EEG) and electrocardiography (ECG) data to compute an ECG-based metric, termed the Electrocardiography Brain Perfusion index (EBPi), which may function as a proxy for cerebral blood flow (CBF). Seventeen ischaemic stroke patients wore the device for nine hours and reported feedback at 1, 3, 6 and 9 h regarding user experience, comfort, and satisfaction (acceptability). Safety was assessed as the number of adverse events reported. Feasibility was assessed as the percentage of uninterrupted EEG/ECG data recorded (data capture efficiency). No adverse events were reported, only minor incidences of discomfort. Overall device comfort (mean ± 1 standard deviation (SD) (range)) (92.5% ± 10.3% (57.0-100%)) and data capture efficiency (mean ± 1 SD (range)) (95.8% ± 6.8% (54.8-100%)) were very high with relatively low variance. The device didn't restrict participants from receiving clinical care and rarely (n = 6) restricted participants from undertaking routine tasks. This study provides a promising evidence base for the deployment of the device in a clinical setting. If clinically validated, EBPi may be able to detect CBF changes to monitor early neurological deterioration and treatment outcomes, thus filling an important gap in current monitoring options.TRIAL REGISTRATION: The study was prospectively registered with the Australian New Zealand Clinical Trials Registry (ACTRN12622000112763).

EEG Characteristics Before and After Dexmedetomidine Treatment in Severe Patients: A Prospective Study

Background. Bedside electroencephalography (EEG) can monitor the changes in brain function in critical patients. Light sedation is recommended in intensive care unit (ICU) patients, but sedation might confuse the EEG readings. There are few studies on the changes of EEG in severe patients with dexmedetomidine. This study aimed to explore the EEG characteristics before and after dexmedetomidine in severe patients in the ICU. Methods. This prospective study enrolled severe patients with sepsis who needed light sedation, we sedated the patients with dexmedetomidine. EEG was recorded for at least 60 min using a quantitative EEG (qEEG) bedside monitor. Amplitude-EEG (aEEG), relative spectral energy, alpha variation, and spectral entropy were recorded and compared before/after dexmedetomidine. Results. Sixty-three participants were enrolled. The relative spectral energy and alpha variation were not different before and after the use of dexmedetomidine (P > .05). The amplitude of the upper and lower boundaries in aEEG and spectral entropy were significantly lower after light sedation with dexmedetomidine compared with before (P < .05). When grouped according to the Glasgow Coma Scale (GCS), the amplitude of qEEG in participants with moderate GCS decreased significantly(P < .05), but not in mild or severe GCS. Conclusion. Relative spectral energy and alpha variation derived from qEEG could be used to evaluate the state of brain function even under light sedation with dexmedetomidine in severe patients during their ICU stay.

Context and domain matter: the error-related negativity in peer presence predicts fear of negative evaluation, not global social anxiety, in adolescents

BACKGROUND

Social anxiety symptoms are most likely to emerge during adolescence, a developmental window marked by heightened concern over peer evaluation. However, the neurocognitive mechanism(s) underlying adolescent social anxiety remain unclear. Emerging work points to the error-related negativity (ERN) as a potential neural marker of exaggerated self/error-monitoring in social anxiety, particularly for errors committed in front of peers. However, social anxiety symptoms are marked by heterogeneity and it remains unclear exactly what domain(s) of social anxiety symptoms are associated with ERN variation in peer presence, particularly within the adolescent period.

METHODS

To advance and deepen the mechanistic understanding of the ERN's putative role as a neural marker for social anxiety in adolescence, we leveraged a social manipulation procedure and assessed a developmentally salient domain of social anxiety during adolescence - fear of negative evaluation (FNE). Adolescents residing in Hanzhong, a small city in the southwestern region of mainland China, had EEG recorded while performing a flanker task, twice (peer presence/absence); FNE, as well as global social anxiety symptoms, was assessed.

RESULTS

Overall ERN increases in peer presence. FNE specifically, but not global levels of social anxiety symptoms, predicted ERN in peer presence.

CONCLUSIONS

These data are the first demonstration that the ERN relates to a specific domain of social anxiety in adolescents, as well as the first evidence of such relations within a non-WEIRD (Western, Educated, Industrialized, Rich and Democratic) sample. Results have important implications for theory and research into adolescent social anxiety.

Trends in the neurological emergency room, focusing on persons with seizures

BACKGROUND AND PURPOSE

Previous studies in neurological emergency rooms (nERs) have reported many non-acute, self-presenting patients, patients with delayed presentation of stroke, and frequent visits of persons with seizures (PWS). The aim of this study was to evaluate trends during the last decade, with special focus on PWS.

METHODS

We retrospectively analyzed patients who presented to our specialized nER during the course of 5 months in 2017 and 2019, and included information on admission/referral, hospitalization, discharge diagnosis, and diagnostic tests/treatment in the nER.

RESULTS

A total of 2791 patients (46.6% male, mean age 57 ± 21 years) were included. The most common diagnoses were cerebrovascular events (26.3%), headache (14.1%), and seizures (10.5%). Most patients presented with symptoms lasting >48 h (41.3%). The PWS group included the largest proportion of patients presenting within 4.5 h of symptom onset (171/293, 58.4%), whereas only 37.1% of stroke patients presented within this time frame (273/735). Self-presentation was the most common admission pathway (31.1%), followed by emergency service referral (30.4%, including the majority of PWS: 197/293, 67.2%). Despite known diagnosis of epilepsy in 49.2%, PWS more often underwent accessory diagnostic testing including cerebral imaging, compared to the overall cohort (accessory diagnostics 93.9% vs. 85.4%; cerebral imaging 70.1% vs. 64.1%). Electroencephalography in the nER was only performed in 20/111 patients (18.0%) with a first seizure. Nearly half of the patients (46.7%) were discharged home after nER work-up, including most self-presenters (632/869, 72.7%) and headache patients (377/393, 88.3%), as well as 37.2% (109/293) of PWS.

CONCLUSION

After 10 years, nER overuse remains a problem. Stroke patients still do not present early enough, whereas PWS, even those with known epilepsy, often seek acute and extensive assessment, indicating gaps in pre-hospital management and possible over-assessment.

Seizure detection with reduced electroencephalogram channels: research trends and outlook

Epilepsy is a prevalent condition characterized by recurrent, unpredictable seizures. Monitoring with surface electroencephalography (EEG) is the gold standard for diagnosing epilepsy, but a time-consuming, uncomfortable and sometimes ineffective process for patients. Further, using EEG over a brief monitoring period has variable success, dependent on patient tolerance and seizure frequency. The availability of hospital resources and hardware and software specifications inherently restrict the options for comfortable, long-term data collection, resulting in limited data for training machine-learning models. This mini-review examines the current patient journey, providing an overview of the current state of EEG monitoring with reduced electrodes and automated channel reduction methods. Opportunities for improving data reliability through multi-modal data fusion are suggested. We assert the need for further research in electrode reduction to advance brain monitoring solutions towards portable, reliable devices that simultaneously offer patient comfort, perform ultra-long-term monitoring and expedite the diagnosis process.

A multimodal approach for the ecological investigation of sustained attention: A pilot study

Natural fluctuations in sustained attention can lead to attentional failures in everyday tasks and even dangerous incidences. These fluctuations depend on personal factors, as well as task characteristics. So far, our understanding of sustained attention is partly due to the common usage of laboratory setups and tasks, and the complex interplay between behavior and brain activity. The focus of the current study was thus to test the feasibility of applying a single-channel wireless EEG to monitor patterns of sustained attention during a set of ecological tasks. An EEG marker of attention (BEI—Brain Engagement Index) was continuously recorded from 42 healthy volunteers during auditory and visual tasks from the Test of Everyday Attention (TEA) and Trail Making Test (TMT). We found a descending pattern of both performance and BEI in the auditory tasks as task complexity increases, while the increase in performance and decrease in BEI on the visual task. In addition, patterns of BEI in the complex tasks were used to detect outliers and the optimal range of attention through exploratory models. The current study supports the feasibility of combined electrophysiological and neurocognitive investigation of sustained attention in ecological tasks yielding unique insights on patterns of sustained attention as a function of task modality and task complexity.

The future of intensive care: delirium should no longer be an issue

In the ideal intensive care unit (ICU) of the future, all patients are free from delirium, a syndrome of brain dysfunction frequently observed in critical illness and associated with worse ICU-related outcomes and long-term cognitive impairment. Although screening for delirium requires limited time and effort, this devastating disorder remains underestimated during routine ICU care. The COVID-19 pandemic brought a catastrophic reduction in delirium monitoring, prevention, and patient care due to organizational issues, lack of personnel, increased use of benzodiazepines and restricted family visitation. These limitations led to increases in delirium incidence, a situation that should never be repeated. Good sedation practices should be complemented by novel ICU design and connectivity, which will facilitate non-pharmacological sedation, anxiolysis and comfort that can be supplemented by balanced pharmacological interventions when necessary. Improvements in the ICU sound, light control, floor planning, and room arrangement can facilitate a healing environment that minimizes stressors and aids delirium prevention and management. The fundamental prerequisite to realize the delirium-free ICU, is an awake non-sedated, pain-free comfortable patient whose management follows the A to F (A-F) bundle. Moreover, the bundle should be expanded with three additional letters, incorporating humanitarian care: gaining (G) insight into patient needs, delivering holistic care with a 'home-like' (H) environment, and redefining ICU architectural design (I). Above all, the delirium-free world relies upon people, with personal challenges for critical care teams to optimize design, environmental factors, management, time spent with the patient and family and to humanize ICU care.

Rapid Response EEG: Current State and Future Directions

PURPOSE OF REVIEW

To critically appraise the literature on the application, methods, and advances in emergency electroencephalography (EEG).

RECENT FINDINGS

The development of rapid EEG (rEEG) technologies and other reduced montage approaches, along with advances in machine learning over the past decade, has increased the rate and access to EEG acquisition. These achievements have made EEG in the emergency setting a practical diagnostic technique for detecting seizures, suspected nonconvulsive status epilepticus (NCSE), altered mental status, stroke, and in the setting of sedation. Growing evidence supports using EEG to expedite medical decision-making in the setting of suspected acute neurological injury. This review covers approaches to acquiring EEG in the emergency setting in the adult and pediatric populations. We also cover the clinical impact of this data, the time associated with emergency EEG, and the costs of acquiring EEG in these settings. Finally, we discuss the advances in artificial intelligence for rapid electrophysiological interpretation.

Artificial Intelligence Analysis of EEG Amplitude in Intensive Heart Care

This article first studied the morphological characteristics of the EEG for intensive cardiac care; that is, based on the analysis of the mechanism of disease diagnosis and treatment, a signal processing and machine learning model was constructed. Then, the methods of signal preprocessing, signal feature extraction, new neural network model structure, training mechanism, optimization algorithm, and efficiency are studied, and experimental verification is carried out for public data sets and clinical big data. Then, the principle of intensive cardiac monitoring, the mechanism of disease diagnosis, the types of arrhythmia, and the characteristics of the typical signal are studied, and the rhythm performance, individual variability, and neurophysiological basis of electrical signals in intensive cardiac monitoring are researched. Finally, the automatic signal recognition technology is studied. In order to improve the training speed and generalization ability, a multiclassification model based on Least Squares Twin Support Vector Machine (LS-TWIN-SVM) is proposed. The computational complexity of the classification model algorithm is compared, and intelligence is adopted. The optimization algorithm selects the parameters of the classifier and uses the EEG signal to simulate the model. Support Vector Machines and their improved algorithms have achieved the ultimum in shallow neural networks and have achieved good results in the classification and recognition of bioelectric signals. The LS-TWIN-SVM algorithm proposed in this paper has achieved good results in the classification and recognition of bioelectric signals. It can perform bioinformatics processing on intensive cardiac care EEG signals, systematically biometric information, diagnose diseases, the real-time detection, auxiliary diagnosis, and rehabilitation of patients.