The EEG in coma

Reaching the ceiling? Empirical scaling behaviour for deep EEG pathology classification

Machine learning techniques, particularly deep convolutional neural networks (ConvNets), are increasingly being used to automate clinical EEG analysis, with the potential to reduce the clinical burden and improve patient care. However, further research is required before they can be used in clinical settings, particularly regarding the impact of the number of training samples and model parameters on their testing error. To address this, we present a comprehensive study of the empirical scaling behaviour of ConvNets for EEG pathology classification. We analysed the testing error with increasing the training samples and model size for four different ConvNet architectures. The focus of our experiments is width scaling, and we have increased the number of parameters to up to 1.8 million. Our evaluation was based on two publicly available datasets: the Temple University Hospital (TUH) Abnormal EEG Corpus and the TUH Abnormal Expansion Balanced EEG Corpus, which together contain 10,707 training samples. The results show that the testing error follows a saturating power-law with both model and dataset size. This pattern is consistent across different datasets and ConvNet architectures. Furthermore, empirically observed accuracies saturate at 85%-87%, which may be due to an imperfect inter-rater agreement on the clinical labels. The empirical scaling behaviour of the test performance with dataset and model size has significant implications for deep EEG pathology classification research and practice. Our findings highlight the potential of deep ConvNets for high-performance EEG pathology classification, and the identified scaling relationships provide valuable recommendations for the advancement of automated EEG diagnostics.

Quantitative EEG reactivity induced by electrical stimulation predicts good outcome in comatose patients after cardiac arrest

BACKGROUND

EEG reactivity is a predictor for neurological outcome in comatose patients after cardiac arrest (CA); however, its application is limited by variability in stimulus types and visual assessment. We aimed to evaluate the prognostic value of the quantitative analysis of EEG reactivity induced by standardized electrical stimulation and for early prognostication in this population.

METHODS

This prospective observational study recruited post-CA comatose patients in Xuanwu Hospital, Capital Medical University (Beijing, China) between January 2016 and June 2023. EEG reactivity to electrical or traditional pain stimulation was randomly performed via visual and quantitative analysis. Neurological outcome within 6 months was dichotomized as good (Cerebral Performance Categories, CPC 1-2) or poor (CPC 3-5).

RESULTS

Fifty-eight post-CA comatose patients were admitted, and 52 patients were included in the final analysis, of which 19 (36.5%) had good outcomes. EEG reactivity induced with the electrical stimulation had superior performance to the traditional pain stimulation for good outcome prediction (quantitative analysis: AUC 0.932 vs. 0.849, p = 0.048). When using the electrical stimulation, the AUC of EEG reactivity to predict good outcome by visual analysis was 0.838, increasing to 0.932 by quantitative analysis (p = 0.039). Comparing to the traditional pain stimulation by visual analysis, the AUC of EEG reactivity for good prognostication by the electrical stimulation with quantitative analysis was significantly improved (0.932 vs. 0.770, p = 0.004).

CONCLUSIONS

EEG reactivity induced by the standardized electrical stimulation in combination with quantitative analysis is a promising formula for post-CA comatose patients, with increased predictive accuracy.

Prognosis of comatose patients with reduced EEG montage by combining quantitative EEG features in various domains

Objective

As the frontoparietal network underlies recovery from coma, a limited frontoparietal montage was used, and the prognostic values of EEG features for comatose patients were assessed.

Methods

Collected with a limited frontoparietal EEG montage, continuous EEG recordings of 81 comatose patients in ICU were used retrospectively. By the 60-day Glasgow outcome scale (GOS), the patients were dichotomized into favorable and unfavorable outcome groups. Temporal-, frequency-, and spatial-domain features were automatically extracted for comparison. Partial correlation analysis was applied to eliminate redundant factors, and multiple correspondence analysis was used to explore discrimination between groups. Prognostic characteristics were calculated to assess the performance of EEG feature-based predictors established by logistic regression. Analyses were performed on all-patients group, strokes subgroup, and traumatic brain injury (TBI) subgroup.

Results

By analysis of all patients, raised burst suppression ratio (BSR), suppressed root mean square (RMS), raised power ratio of β to α rhythm (β/α), and suppressed phase-lag index between F3 and P4 (PLI [F3, P4]) were associated with unfavorable outcome, and yielded AUC of 0.790, 0.811, 0.722, and 0.844, respectively. For the strokes subgroup, the significant variables were BSR, RMS, θ/total, θ/δ, and PLI (F3, P4), while for the TBI subgroup, only PLI (F3, P4) was significant. BSR combined with PLI (F3, P4) gave the best predictor by cross-validation analysis in the all-patients group (AUC = 0.889, 95% CI: 0.819-0.960).

Conclusion

Features extracted from limited frontoparietal montage EEG served as valuable coma prognostic tools, where PLI (F3, P4) was always significant. Combining PLI (F3, P4) with features in other domains may achieve better performance.

Significance

A limited-montage EEG coupled with an automated algorithm is valuable for coma prognosis.

The predictive value of EEG reactivity by electrical stimulation and quantitative analysis in critically ill patients with large hemispheric infarction

PURPOSE

The intensive care of critically ill patients with large hemispheric infarction improves the survival rate. However, established prognostic markers for neurological outcome show variable accuracy. We aimed to assess the value of electrical stimulation and quantitative analysis of EEG reactivity for early prognostication in this critically ill population.

MATERIALS AND METHODS

We prospectively enrolled consecutive patients between January 2018 and December 2021. EEG reactivity was randomly performed by pain or electrical stimulation via visual and quantitative analysis. Neurological outcome within 6-month was dichotomized as good (modified Rankin Scale, mRS 0-3) or poor (mRS 4-6).

RESULTS

Ninety-four patients were admitted, and 56 were included in the final analysis. EEG reactivity using electrical stimulation was superior to pain stimulation for good outcome prediction (visual analysis: AUC 0.825 vs. 0.763, P = 0.143; quantitative analysis: AUC 0.931 vs. 0.844, P = 0.058). The AUC of EEG reactivity by pain stimulation with visual analysis was 0.763, which increased to 0.931 by electrical stimulation with quantitative analysis (P = 0.006). When using quantitative analysis, the AUC of EEG reactivity increased (pain stimulation 0.763 vs. 0.844, P = 0.118; electrical stimulation 0.825 vs. 0.931, P = 0.041).

CONCLUSION

EEG reactivity by electrical stimulation and quantitative analysis seems a promising prognostic factor in these critical patients.

Use of magnetic source imaging to assess recovery after severe traumatic brain injury-an MEG pilot study

Rationale

Severe TBI (sTBI) is a devastating neurological injury that comprises a significant global trauma burden. Early comprehensive neurocritical care and rehabilitation improve outcomes for such patients, although better diagnostic and prognostic tools are necessary to guide personalized treatment plans.

Methods

In this study, we explored the feasibility of conducting resting state magnetoencephalography (MEG) in a case series of sTBI patients acutely after injury (~7 days), and then about 1.5 and 8 months after injury. Synthetic aperture magnetometry (SAM) was utilized to localize source power in the canonical frequency bands of delta, theta, alpha, beta, and gamma, as well as DC-80 Hz.

Results

At the first scan, SAM source maps revealed zones of hypofunction, islands of preserved activity, and hemispheric asymmetry across bandwidths, with markedly reduced power on the side of injury for each patient. GCS scores improved at scan 2 and by scan 3 the patients were ambulatory. The SAM maps for scans 2 and 3 varied, with most patients showing increasing power over time, especially in gamma, but a continued reduction in power in damaged areas and hemispheric asymmetry and/or relative diminishment in power at the site of injury. At the group level for scan 1, there was a large excess of neural generators operating within the delta band relative to control participants, while the number of neural generators for beta and gamma were significantly reduced. At scan 2 there was increased beta power relative to controls. At scan 3 there was increased group-wise delta power in comparison to controls.

Conclusion

In summary, this pilot study shows that MEG can be safely used to monitor and track the recovery of brain function in patients with severe TBI as well as to identify patient-specific regions of decreased or altered brain function. Such MEG maps of brain function may be used in the future to tailor patient-specific rehabilitation plans to target regions of altered spectral power with neurostimulation and other treatments.

Impact of Listening to Indian Classical Music, or Rāgas, on the Electroencephalogram: A Meta-Analysis

Ancient Indian classical music (ICM) has long been lauded and recognized for influencing emotional responses by influencing the human body's resonance. A meta-analysis of prospective case studies published in the last ten years on the effect of ancient Indian music rāgas on brain waves is investigated. This meta-analysis aimed to analyze published prospective studies investigating the effect of ancient Indian rāgas on EEG in healthy subjects. The present study included prospective studies published since 2012. Studies were obtained by searching four databases, such as PsychINFO, PubMed, Google Scholar, and JSTOR, and searching related journals. Eligibility criteria included studies assessing the impact of listening to Indian classical music on the EEG. Primary outcomes were changes in the brain waves, frequency, and power and their relationship to activity-related arousal, attention, and mental tasks. The studies were analyzed according to the PRISMA guidelines. There were a total of five included studies with 71 participants in the age range of 19-30, and the conditions for the test groups were generally similar except for varying types of rāgas used and time of day. Analysis of the data collected from 71 participants revealed that music interventions had statistically significant effects on increasing alpha activity and attention scores. Fractal analysis was sensitive enough to detect EEG brainwave changes while and after listening to the rāga musical intervention. Rāgas stimulate arousal in different areas of the brain, depending on the emotions they are designed to evoke. However, the synchronized studies together could not highlight a significant relationship between rāgas and EEG fractal dimension values. Although the meta-analysis failed to reproduce the same results from the individual studies, potentially due to the small sample size and study variation, the meta-analysis opens doors to the potential of rāgas to elicit distinct emotions and serve as robust predictors of emotional response. Future studies can explore the therapeutic potential of various rāgas in the clinical setting, such as in the management of cognitive disorders and stress or in modulating heart rate variability and cognitive performance.

Valproate-related hyperammonemic encephalopathy with generalized suppression EEG: a case report

BACKGROUND

Valproic acid (VPA) is a prevalent antiseizure medication (ASM) used to treat epilepsy. Valproate-related hyperammonemic encephalopathy (VHE) is a type of encephalopathy that can occur during neurocritical situations. In VHE, the electroencephalogram (EEG) displays diffuse slow waves or periodic waves, and there is no generalized suppression pattern.

CASE PRESENTATION

We present a case of a 29-year-old female with a history of epilepsy who was admitted for convulsive status epilepticus (CSE), which was controlled by intravenous VPA, as well as oral VPA and phenytoin. The patient did not experience further convulsions but instead developed impaired consciousness. Continuous EEG monitoring revealed a generalized suppression pattern, and the patient was unresponsive. The patient's blood ammonia level was significantly elevated at 386.8 μmol/L, indicating VHE. Additionally, the patient's serum VPA level was 58.37 μg/ml (normal range: 50-100 μg/ml). After stopping VPA and phenytoin and transitioning to oxcarbazepine for anti-seizure and symptomatic treatment, the patient's EEG gradually returned to normal, and her consciousness was fully restored.

DISCUSSION

VHE can cause the EEG to display a generalized suppression pattern. It is crucial to recognize this specific situation and not to infer a poor prognosis based on this EEG pattern.

Lennox-Gastaut syndrome characterized by super-refractory status epilepticus treated with high-dose anesthetics: A case report

RATIONALE

Super-refractory status epilepticus is a serious illness with high morbidity and mortality, which is defined as an SE that continues or recurs 24 hours or more after the onset of anesthesia. Anesthetic agents can be either pro-convulsant or anticonvulsant or both.

PATIENT CONCERNS

Epilepsy occurred at the age of 3 years. At the age of 4 years, generalized tonic-clonic seizure occurred for the first time. The patient was hospitalized at the age of 27 and 28 years for treating status epilepticus. At the age of 33 years, antiepileptic drugs were stopped due to poor appetite. In an early morning, the patient was found delirious with reduced speech.

DIAGNOSIS

Occasionally, the patient blinked his eyelids, or deflected his eyeballs to 1 side. When propofol was lowered to 10 mL/H, the epileptic wave reduced obviously. Afterwards, the patient opened his eyes autonomously and his consciousness gradually recovered. The patient could answer questions, and the limbs had voluntary movements. The patient breathing also gradually recovered, and his urine gradually returned to pale yellow from green. After anesthetic was stopped for 10 days, the patient lost his consciousness again. The patient eyes turned upward frequently, which was relieved in 1 to 2 seconds with an attack once every 2 to 5 minutes.

INTERVENTIONS

Clonazepam was gradually reduced to 2 mg qn, and the patient gradually woke up during this process. The patient was also treated with levetiracetam 1.5 g bid, oxcarbazepine 0.6 g bid, topiramate 50 mg bid and valproate 0.4 g tid.

OUTCOMES

After 1 month follow-up, status epilepticus did not appear again.

LESSONS

Propofol aggravated the tonic seizures. As tonic seizures occur during natural sleep and after sleep induced by various narcotic drugs, the decrease of consciousness level induced by excessive sedation of narcotic drugs has been suggested as the reason for poor seizure control.

Always Assess the Raw Electroencephalogram: Why Automated Burst Suppression Detection May Not Detect All Episodes

BACKGROUND

Electroencephalogram (EEG)-based monitors of anesthesia are used to assess patients' level of sedation and hypnosis as well as to detect burst suppression during surgery. One of these monitors, the Entropy module, uses an algorithm to calculate the burst suppression ratio (BSR) that reflects the percentage of suppressed EEG. Automated burst suppression detection monitors may not reliably detect this EEG pattern. Hence, we evaluated the detection accuracy of BSR and investigated the EEG features leading to errors in the identification of burst suppression.

METHODS

With our study, we were able to compare the performance of the BSR to the visual burst suppression detection in the raw EEG and obtain insights on the architecture of the unrecognized burst suppression phases.

RESULTS

We showed that the BSR did not detect burst suppression in 13 of 90 (14%) patients. Furthermore, the time comparison between the visually identified burst suppression duration and elevated BSR values strongly depended on the BSR value being used as a cutoff. A possible factor for unrecognized burst suppression by the BSR may be a significantly higher suppression amplitude ( P = .002). Six of the 13 patients with undetected burst suppression by BSR showed intraoperative state entropy values >80, indicating a risk of awareness while being in burst suppression.

CONCLUSIONS

Our results complement previous results regarding the underestimation of burst suppression by other automated detection modules and highlight the importance of not relying solely on the processed index, but to assess the native EEG during anesthesia.

Use of duplex echoencephalography to evaluate brain death in children: A novel approach to the diagnosis

BACKGROUND AND PURPOSE

Brain death is defined as the irreversible cessation of brain function with a known etiology. This study aims to establish the value of duplex echoencephalography (DEG) in children fulfilling clinical brain death diagnostic criteria.

METHODS

DEG must show intracranial brain structures. Power Doppler is used to assess venous flow when feasible. Color Doppler patterns in all major arteries are assessed. Spectral analysis of arterial flow is divided into four grades: grade 1: inverted flow during entire diastole with time average peak velocity (TAPV) less or equal to zero; grade 2: disappearance of the inverted diastolic flow at the end of diastole; grade 3: oscillating pattern in early diastole; and grade 4: no diastolic flow with systolic blip. To fulfill diagnosis of brain death, brain perfusion must be lost for 30 minutes.

RESULTS

DEG is performed in 41 pediatric patients. In infants, loss of venous flow occurs regardless of the etiology. Grade 1 is the most common arterial color flow pattern and TAPV is always below zero. A pulsatile color flow is associated with three other types of flow patterns (grades 2-4). TAPV is not calculated, when there is loss of diastolic flow. Diagnosis of brain death is validated using nuclear brain scan in 4 patients. Two have a grade 1 flow pattern, while the other two have a grade 4 flow pattern.

CONCLUSIONS

In children, DEG following a strict protocol can be used to confirm diagnosis of brain death in the appropriate clinical setting.

From Alpha to Zeta: A Systematic Review of Zeta Waves

PURPOSE

Electroencephalogram is used for prognostication and diagnosis in critically ill patients and is vital in developing clinical management algorithms. Unique waveforms on EEG may distinguish neurological disorders and define a potential for seizures. To better characterize zeta waves, we sought to define their electroclinical spectrum.

METHODS

We performed a systematic review using MEDLINE, Embase, Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Review [through Ovid], Scopus, Science Citation Index Expanded and Emerging Sources Citation Index [through the Web of Science], and Epistemonikos. Grey literature resources were searched.

RESULTS

Five hundred thirty-seven articles were identified. After excluding duplicates and reviewing titles, abstracts, and bodies and bibliographies of articles, four studies reported 64 cases describing data from patients with zeta waves, with a prevalence of 3 to 4%. Various and often incomplete clinical, neuroimaging, and EEG data were available. 57 patients (89.1%) had a focal cerebral lesion concordant with the location of zeta waves on EEG. 26 patients (40.6%) had clinical seizures, all but one being focal onset. Thirteen patients (20%) had epileptiform activity on EEG. Typically, zeta waves were located in the frontal head regions, often with generalized, frontal, predominant, rhythmic delta activity and associated with focal EEG suppression.

CONCLUSIONS

Zeta waves frequently represent an underlying focal structural lesion. Their presence suggests a heightened risk for seizures. The small number of retrospective cases series in the literature reporting zeta waves might be an underrepresentation. We suggest a need for prospective studies of cEEG in critically ill patients to determine their clinical significance.

General anesthesia and sleep: like and unlike

General anesthesia and sleep have long been discussed in the neurobiological context owingto their commonalities, such as unconsciousness, immobility, non-responsiveness to externalstimuli, and lack of memory upon returning to consciousness. Sleep is regulated bycomplex interactions between wake-promoting and sleep-promoting neural circuits. Anestheticsexert their effects partly by inhibiting wake-promoting neurons or activating sleep-promotingneurons. Unconscious but arousable sedation is more related to sleep-wake circuitries,whereas unconscious and unarousable anesthesia is independent of them. Generalanesthesia is notable for its ability to decrease sleep propensity. Conversely, increasedsleep propensity due to insufficient sleep potentiates anesthetic effects. Taken together, it isplausible that sleep and anesthesia are closely related phenomena but not the same ones.Further investigations on the relationship between sleep and anesthesia are warranted.

Common patterns of EEG reactivity in post-anoxic coma identified by quantitative analyses

OBJECTIVE

Description of typical kinds of EEG reactivity (EEG-R) in post-anoxic coma using a quantitative method.

METHODS

Study of 101 out-of-hospital cardiac arrest patients, 71 with good outcome (cerebral performance category scale ≤ 2). EEG was recorded 12-24 hours after cardiac arrest and four noxious, one auditory, and one visual stimulation were applied for 30 seconds each. Individual reference intervals for the power in the delta, theta, alpha, and beta bands were calculated based on six 2-second resting epochs just prior to stimulations. EEG-R in consecutive 2-second epochs after stimulation was expressed in Z-scores.

RESULTS

EEG-R occurred roughly equally frequent as an increase or as a decrease in EEG activity. Sternal rub and sound stimulation were most provocative with the most pronounced changes as an increase in delta activity 4.5-8.5 seconds after stimulation and a decrease in theta activity 0.5-4.5 seconds after stimulation. These parameters predicted good outcome with an AUC of 0.852 (95 % CI: 0.771-0.932).

CONCLUSIONS

Quantitative EEG-R is a feasible method for identification of common types of reactivity, for evaluation of stimulation methods, and for prognostication.

SIGNIFICANCE

This method provides an objective measure of EEG-R revealing knowledge about the nature of EEG-R and its use as a diagnostic tool.

tDCS-EEG for Predicting Outcome in Patients With Unresponsive Wakefulness Syndrome

Objectives

We aimed to assess the role of transcranial direct current stimulation (tDCS) combined with electroencephalogram (EEG) for predicting prognosis in UWS cases.

Methods

This was a historical control study that enrolled 85 patients with UWS. The subjects were assigned to the control (without tDCS) and tDCS groups. Conventional treatments were implemented in both the control and tDCS groups, along with 40 multi-target tDCS sessions only in the tDCS group. Coma Recovery Scale-Revised (CRS-R) was applied at admission. The non-linear EEG index was evaluated after treatment. The modified Glasgow Outcome Scale (mGOS) was applied 12 months after disease onset.

Results

The mGOS improvement rate in the tDCS group (37.1%) was higher than the control value (22.0%). Linear regression analysis revealed that the local and remote cortical networks under unaffected pain stimulation conditions and the remote cortical network under affected pain stimulation conditions were the main relevant factors for mGOS improvement. Furthermore, the difference in prefrontal-parietal cortical network was used to examine the sensitivity of prognostic assessment in UWS patients. The results showed that prognostic sensitivity could be increased from 54.5% (control group) to 84.6% (tDCS group).

Conclusions

This study proposes a tDCS-EEG protocol for predicting the prognosis of UWS. With multi-target tDCS combined with EEG, the sensitivity of prognostic assessment in patients with UWS was improved. The recovery might be related to improved prefrontal-parietal cortical networks of the unaffected hemisphere.

What is neurorepresentationalism? From neural activity and predictive processing to multi-level representations and consciousness

This review provides an update on Neurorepresentationalism, a theoretical framework that defines conscious experience as multimodal, situational survey and explains its neural basis from brain systems constructing best-guess representations of sensations originating in our environment and body [1]. It posits that conscious experience is characterized by five essential hallmarks: (i) multimodal richness, (ii) situatedness and immersion, (iii) unity and integration, (iv) dynamics and stability, and (v) intentionality. Consciousness is furthermore proposed to have a biological function, framed by the contrast between reflexes and habits (not requiring consciousness) versus goal-directed, planned behavior (requiring multimodal, situational survey). Conscious experience is therefore understood as a sensorily rich, spatially encompassing representation of body and environment, while we nevertheless have the impression of experiencing external reality directly. Contributions to understanding neural mechanisms underlying consciousness are derived from models for predictive processing, which are trained in an unsupervised manner, do not necessarily require overt action, and have been extended to deep neural networks. Even with predictive processing in place, however, the question remains why this type of neural network activity would give rise to phenomenal experience. Here, I propose to tackle the Hard Problem with the concept of multi-level representations which emergently give rise to multimodal, spatially wide superinferences corresponding to phenomenal experiences. Finally, Neurorepresentationalism is compared to other neural theories of consciousness, and its implications for defining indicators of consciousness in animals, artificial intelligence devices and immobile or unresponsive patients with disorders of consciousness are discussed.

Spatial signatures of anesthesia-induced burst-suppression differ between primates and rodents

During deep anesthesia, the electroencephalographic (EEG) signal of the brain alternates between bursts of activity and periods of relative silence (suppressions). The origin of burst-suppression and its distribution across the brain remain matters of debate. In this work, we used functional magnetic resonance imaging (fMRI) to map the brain areas involved in anesthesia-induced burst-suppression across four mammalian species: humans, long-tailed macaques, common marmosets, and rats. At first, we determined the fMRI signatures of burst-suppression in human EEG-fMRI data. Applying this method to animal fMRI datasets, we found distinct burst-suppression signatures in all species. The burst-suppression maps revealed a marked inter-species difference: in rats, the entire neocortex engaged in burst-suppression, while in primates most sensory areas were excluded—predominantly the primary visual cortex. We anticipate that the identified species-specific fMRI signatures and whole-brain maps will guide future targeted studies investigating the cellular and molecular mechanisms of burst-suppression in unconscious states.

The development of anesthesia was a significant advance in medicine. It allows individuals to undergo surgery without feeling pain or remembering the experience. But scientists still do not know how anesthesia works. During anesthesia, scientists have measured brain activity using electroencephalograms (EEG) and found that the brain appears to turn on and off. Comatose patients also have similar switches between bursts of electrical activity and periods of silence. This burst-suppression pattern may be related to unconsciousness.

But scientists still have many questions about how anesthesia causes burst-suppression. One challenge is that while an EEG can tell scientists when the brain turns on and off, it does not show exactly where this occurs. Another imaging method called functional Magnetic Resonance Imaging (fMRI) may fill this gap by allowing scientists to map where the brain activity occurs.

Sirmpilatze et al. have created detailed maps of burst-suppression in humans, primates, and rats under anesthesia by analyzing brain scans using fMRI. In rats, the entire outer layer or cortex of the brain underwent a synchronized pattern of burst-suppression. In humans and primates, areas of the brain like those responsible for eyesight did not follow the rest of the cortex in switching on and off.

The experiments reveal crucial differences in how rats and humans and other primates respond to anesthesia. The fMRI mapping technique Sirmpilatze et al. created may help scientists learn more about these differences and why some parts of human brains do not undergo burst-suppression. This may help scientists learn more about unconsciousness and help improve anesthesia or the care of comatose patients.

Electroencephalogram patterns in critical care: A primer for acute care doctors

Electroencephalograms are commonly ordered by acute care doctors but not always understood. Other reviews have covered when and how to perform electroencephalograms. This primer has a different, unique, and complementary goal. We review basic electroencephalogram interpretation and terminology for nonexperts. Our goal is to encourage common understanding, facilitate inter specialty collaboration, dispel common misunderstandings, and inform the current and future use of this precious resource. This primer is categorically not to replace the expert neurologist or technician. Quite the contrary, it should help explain how nuanced electroencephalogram can be, and why indiscriminate electroencephalogram is inappropriate. Some might argue not to teach nonexperts lest they overestimate their abilities or reach. We humbly submit that it is even more inappropriate to not know the basics of a test that is ordered frequently and resource intensive. We cover the characteristics of the "normal" electroencephalogram, electroencephalogram slowing, periodic epileptiform discharges (and its subtypes), burst suppression, and electrographic seizures (and its subtypes). Alongside characteristic electroencephalogram findings, we provide clinical pearls. These should further explain what the reporter is communicating and whether additional testing is beneficial. Along with teaching the basics and whetting the appetite of the general clinician, this resource could increase mutual understanding and mutual appreciation between those who order electroencephalograms and those who interpret them. While there is more to electroencephalogram than can be delivered via a single concise primer, it offers a multidisciplinary starting point for those interested in the present and future of this commonly ordered test.

Electroencephalographic Burst-Suppression, Perioperative Neuroprotection, Postoperative Cognitive Function, and Mortality: A Focused Narrative Review of the Literature

Burst-suppression is an electroencephalographic pattern that results from a diverse array of pathophysiological causes and/or metabolic neuronal suppression secondary to the administration of anesthetic medications. The purpose of this review is to provide an overview of the physiological mechanisms that underlie the burst-suppression pattern and to present in a comprehensive way the available evidence both supporting and in opposition to the clinical use of this electroencephalographic pattern as a therapeutic measure in various perioperative settings.

Low neuronal metabolism during isoflurane-induced burst suppression is related to synaptic inhibition while neurovascular coupling and mitochondrial function remain intact

Deep anaesthesia may impair neuronal, vascular and mitochondrial function facilitating neurological complications, such as delirium and stroke. On the other hand, deep anaesthesia is performed for neuroprotection in critical brain diseases such as status epilepticus or traumatic brain injury. Since the commonly used anaesthetic propofol causes mitochondrial dysfunction, we investigated the impact of the alternative anaesthetic isoflurane on neuro-metabolism. In deeply anaesthetised Wistar rats (burst suppression pattern), we measured increased cortical tissue oxygen pressure (p_tiO₂), a ∼35% drop in regional cerebral blood flow (rCBF) and burst-associated neurovascular responses. In vitro, 3% isoflurane blocked synaptic transmission and impaired network oscillations, thereby decreasing the cerebral metabolic rate of oxygen (CMRO₂). Concerning mitochondrial function, isoflurane induced a reductive shift in flavin adenine dinucleotide (FAD) and decreased stimulus-induced FAD transients as Ca²⁺ influx was reduced by ∼50%. Computer simulations based on experimental results predicted no direct effects of isoflurane on mitochondrial complexes or ATP-synthesis. We found that isoflurane-induced burst suppression is related to decreased ATP consumption due to inhibition of synaptic activity while neurovascular coupling and mitochondrial function remain intact. The neurometabolic profile of isoflurane thus appears to be superior to that of propofol which has been shown to impair the mitochondrial respiratory chain.

The relationship between fasting-induced torpor, sleep, and wakefulness in laboratory mice

STUDY OBJECTIVES

Torpor is a regulated and reversible state of metabolic suppression used by many mammalian species to conserve energy. Whereas the relationship between torpor and sleep has been well-studied in seasonal hibernators, less is known about the effects of fasting-induced torpor on states of vigilance and brain activity in laboratory mice.

METHODS

Continuous monitoring of electroencephalogram (EEG), electromyogram (EMG), and surface body temperature was undertaken in adult, male C57BL/6 mice over consecutive days of scheduled restricted feeding.

RESULTS

All animals showed bouts of hypothermia that became progressively deeper and longer as fasting progressed. EEG and EMG were markedly affected by hypothermia, although the typical electrophysiological signatures of non-rapid eye movement (NREM) sleep, rapid eye movement (REM) sleep, and wakefulness enabled us to perform vigilance-state classification in all cases. Consistent with previous studies, hypothermic bouts were initiated from a state indistinguishable from NREM sleep, with EEG power decreasing gradually in parallel with decreasing surface body temperature. During deep hypothermia, REM sleep was largely abolished, and we observed shivering-associated intense bursts of muscle activity.

CONCLUSIONS

Our study highlights important similarities between EEG signatures of fasting-induced torpor in mice, daily torpor in Djungarian hamsters and hibernation in seasonally hibernating species. Future studies are necessary to clarify the effects on fasting-induced torpor on subsequent sleep.

Etiology of Burst Suppression EEG Patterns

Burst-suppression electroencephalography (EEG) patterns of electrical activity, characterized by intermittent high-power broad-spectrum oscillations alternating with isoelectricity, have long been observed in the human brain during general anesthesia, hypothermia, coma and early infantile encephalopathy. Recently, commonalities between conditions associated with burst-suppression patterns have led to new insights into the origin of burst-suppression EEG patterns, their effects on the brain, and their use as a therapeutic tool for protection against deleterious neural states. These insights have been further supported by advances in mechanistic modeling of burst suppression. In this Perspective, we review the origins of burst-suppression patterns and use recent insights to weigh evidence in the controversy regarding the extent to which burst-suppression patterns observed during profound anesthetic-induced brain inactivation are associated with adverse clinical outcomes. Whether the clinical intent is to avoid or maintain the brain in a state producing burst-suppression patterns, monitoring and controlling neural activity presents a technical challenge. We discuss recent advances that enable monitoring and control of burst suppression.

Prognostic value of 'late' electroencephalography recordings in patients with cardiopulmonal resuscitation after cardiac arrest

Background

Electroencephalography (EEG) significantly contributes to the neuroprognostication after resuscitation from cardiac arrest. Recent studies suggest that the prognostic value of EEG is highest for continuous recording within the first days after cardiac arrest. Early continuous EEG, however, is not available in all hospitals. In this observational study, we sought to evaluate the predictive value of a ‘late’ EEG recording 5–14 days after cardiac arrest without sedatives.

Methods

We retrospectively analyzed EEG data in consecutive adult patients treated at the medical intensive care units (ICU) of the Charité—Universitätsmedizin Berlin. Outcome was assessed as cerebral performance category (CPC) at discharge from ICU, with an unfavorable outcome being defined as CPC 4 and 5.

Results

In 187 patients, a ‘late’ EEG recording was performed. Of these patients, 127 were without continuous administration of sedative agents for at least 24 h before the EEG recording. In this patient group, a continuously suppressed background activity < 10 µV predicted an unfavorable outcome with a sensitivity of 31% (95% confidence interval (CI) 20–45) and a specificity of 99% (95% CI 91–100). In patients with suppressed background activity and generalized periodic discharges, sensitivity was 15% (95% CI 7–27) and specificity was 100% (95% CI 94–100). GPDs on unsuppressed background activity were associated with a sensitivity of 42% (95% CI 29–46) and a specificity of 92% (95% CI 82–97).

Conclusions

A ‘late’ EEG performed 5 to 14 days after resuscitation from cardiac arrest can aide in prognosticating functional outcome. A suppressed EEG background activity in this time period indicates poor outcome.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00415-021-10549-y.

Consciousness among delta waves: a paradox?

A common observation in EEG research is that consciousness vanishes with the appearance of delta (1 - 4 Hz) waves, particularly when those waves are high amplitude. High amplitude delta oscillations are very frequently observed in states of diminished consciousness, including slow wave sleep, anaesthesia, generalised epileptic seizures, and disorders of consciousness such as coma and vegetative state. This strong correlation between loss of consciousness and high amplitude delta oscillations is thought to stem from the widespread cortical deactivation that occurs during the "down states" or troughs of these slow oscillations. Recently, however, many studies have reported the presence of prominent delta activity during conscious states, which casts doubt on the hypothesis that high amplitude delta oscillations are an indicator of unconsciousness. These studies include work in Angelman syndrome, epilepsy, behavioural responsiveness during propofol anaesthesia, postoperative delirium, and states of dissociation from the environment such as dreaming and powerful psychedelic states. The foregoing studies complement an older, yet largely unacknowledged, body of literature that has documented awake, conscious patients with high amplitude delta oscillations in clinical reports from Rett syndrome, Lennox-Gastaut syndrome, schizophrenia, mitochondrial diseases, hepatic encephalopathy, and nonconvulsive status epilepticus. At the same time, a largely parallel body of recent work has reported convincing evidence that the complexity or entropy of EEG and magnetoencephalogram or MEG signals strongly relates to an individual's level of consciousness. Having reviewed this literature, we discuss plausible mechanisms that would resolve the seeming contradiction between high amplitude delta oscillations and consciousness. We also consider implications concerning theories of consciousness, such as integrated information theory and the entropic brain hypothesis. Finally, we conclude that false inferences of unconscious states can be best avoided by examining measures of electrophysiological complexity in addition to spectral power.

Outcome Prediction in Unresponsive Wakefulness Syndrome and Minimally Conscious State by Non-linear Dynamic Analysis of the EEG

Objectives: This study aimed to investigate the role of non-linear dynamic analysis (NDA) of the electroencephalogram (EEG) in predicting patient outcome in unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS).

Methods: This was a prospective longitudinal cohort study. A total of 98 and 64 UWS and MCS cases, respectively, were assessed. During admission, EEGs were acquired under eyes-closed and pain stimulation conditions. EEG nonlinear indices, including approximate entropy (ApEn) and cross-ApEn, were calculated. The modified Glasgow Outcome Scale (mGOS) was employed to assess functional prognosis 1 year following brain injury.

Results: The mGOS scores were improved in 25 (26%) patients with UWS and 42 (66%) with MCS. Under the painful stimulation condition, both non-linear indices were lower in patients with UWS than in those with MCS. The frontal region, periphery of the primary sensory area (S1), and forebrain structure might be the key points modulating disorders of consciousness. The affected local cortical networks connected to S1 and unaffected distant cortical networks connecting S1 to the prefrontal area played important roles in mGOS score improvement.

Conclusions: NDA provides an objective assessment of cortical excitability and interconnections of residual cortical functional islands. The impaired interconnection of the residual cortical functional island meant a poorer prognosis. The activation in the affected periphery of the S1 and the increase in the interconnection of affected local cortical areas around the S1 and unaffected S1 to the prefrontal and temporal areas meant a relatively favorable prognosis.

Are neurophysiologic tests reliable, ultra-early prognostic indices after cardiac arrest?

OBJECTIVES

Determining early and reliable prognosis in comatose subjects after cardiac arrest is a central component of post-cardiac arrest care both for developing realistic prognostic expectations for families, and for better determining which resources are mobilized or withheld for individual patients. The aim of the study was to evaluate the prognostic accuracy of EEG and SEP patterns during the very early period (within the first 6 h) after cardiac arrest.

METHODS

We retrospectively analysed comatose patients after CA, either inside or outside the hospital, in which prognostic evaluation was made during the first 6 h from CA. Prognostic evaluation comprised clinical evaluation (GCS and pupillary light reflex) and neurophysiological (electroencephalography (EEG) and somatosensory evoked potentials (SEP)) studies. Prognosis was evaluated with regards to likelihood of recovery of consciousness and also likelihood of failure to regain consciousness.

RESULTS

Forty-one comatose patients after cardiac arrest were included. All patients with continuous and nearly continuous EEG recovered consciousness. Isoelectric EEG was always associated with poor outcome. Burst-suppression, suppression and discontinuous patterns were usually associated with poor outcome although some consciousness recovery was observed. Bilaterally absent SEP responses were always associated with poor outcome. Continuous and nearly continuous EEG patterns were never associated with bilaterally absent SEP.

CONCLUSIONS

During the very early period following cardiac arrest (first 6 h), EEG and SEP maintain their high predictive value to predict respectively recovery and failure of recovery of consciousness. A very early EEG exam allows identification of patients with very high probability of a good outcome, allowing rapid use of the most appropriate therapeutic procedures.

Electroencephalographic findings in Bickerstaff's brainstem encephalitis: A possible reflection of the dysfunction of the ascending reticular activating system

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BBE impairs the ascending reticular activating system, and causes EEG changes.

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Stereotypical EEG changes correlating with the level of consciousness were observed.

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Characteristic “unarousable sleep-like” EEG can be a diagnostic clue for BBE.

Objectives

Bickerstaff’s brainstem encephalitis (BBE) is a rare post-infectious inflammatory disease, which causes impaired consciousness by the dysfunction of the ascending reticular activating system (ARAS). We aimed to clarify EEG changes possibly caused by the dysfunction of the ARAS in BBE.

Methods

We retrospectively investigated 15 EEGs from 5 patients with definite BBE (i.e., the positivity for serum IgG anti-GQ1b antibodies was mandatory for the diagnosis) admitted to our hospital from January 2014 through December 2019, particularly focusing on whether N1 and N2 sleep patterns were maintained.

Results

All of the 10 EEGs recorded when patients had consciousness disturbance were abnormal. Stereotypical EEG changes correlating with their level of consciousness were identified: poorly organized posterior dominant rhythms with maintenance of sleep patterns in patients with mild consciousness disturbance (n = 5); predominant N1 and/or N2 sleep patterns even with external stimuli, including spindle coma pattern, in patients with moderate consciousness disturbance (“unarousable sleep-like” EEG) (n = 4); and generalized slow waves without N1 and N2 sleep patterns in patients with severe consciousness disturbance (n = 1). Among 5 patients, 3 (60%) had “unarousable sleep-like” EEG in their clinical course.

Conclusions

Patients with BBE showed stereotypical EEG changes correlating with their level of consciousness, mostly with maintenance of N1 and N2 sleep patterns, and often exhibited characteristic “unarousable sleep-like” EEG.

Significance

This study revealed characteristic EEG changes possibly caused by the dysfunction of the ARAS, which can be a diagnostic clue for BBE.

Does electroencephalographic burst suppression still play a role in the perioperative setting?

With the widespread use of electroencephalogram [EEG] monitoring during surgery or in the Intensive Care Unit [ICU], clinicians can sometimes face the pattern of burst suppression [BS]. The BS pattern corresponds to the continuous quasi-periodic alternation between high-voltage slow waves [the bursts] and periods of low voltage or even isoelectricity of the EEG signal [the suppression] and is extremely rare outside ICU and the operative room. BS can be secondary to increased anesthetic depth or a marker of cerebral damage, as a therapeutic endpoint [i.e., refractory status epilepticus or refractory intracranial hypertension]. In this review, we report the neurophysiological features of BS to better define its role during intraoperative and critical care settings.

Neuronal excitability and sensory responsiveness in the thalamo-cortical network in a novel rat model of isoelectric brain state

KEY POINTS

The neuronal and network properties that persist during an isoelectric coma remain largely unknown.  We developed a new in vivo rat model to assess cell excitability and sensory responsiveness in the thalamo-cortical pathway during an isoflurane-induced isoelectric brain state.  The isoelectric electrocorticogram reflected a complete interruption of spontaneous synaptic and firing activities in cortical and thalamic neurons.  Cell excitability and sensory responses in the thalamo-cortical network persisted at a reduced level in the isoelectric condition and returned to control values after resumption of background brain activity.  These findings could lead to a reassessment of the functional status of the drug-induced isoelectric state: a latent state in which individual neurons and networks retain to some extent the ability of being activated by external inputs.

ABSTRACT

The neuronal and network properties that persist in an isoelectric brain completely deprived of spontaneous electrical activity remain largely unexplored. Here, we developed a new in vivo rat model to examine cell excitability and sensory responsiveness in somatosensory thalamo-cortical networks during the interruption of endogenous brain activity induced by high doses of isoflurane. Electrocorticograms (ECoGs) from the barrel cortex were captured simultaneously with either intracellular recordings of subjacent cortical pyramidal neurons or extracellular records of the related thalamo-cortical neurons. Isoelectric ECoG periods reflected the disappearance of spontaneous synaptic and firing activities in cortical and thalamic neurons. This was associated with a sustained membrane hyperpolarization and a reduced intrinsic excitability in deep-layer cortical neurons, without significant changes in their membrane input resistance. Concomitantly, we found that whisker-evoked potentials in the ECoG and synaptic responses in cortical neurons were attenuated in amplitude and increased in latency. Impaired responsiveness in the barrel cortex paralleled with a lowering of the sensory-induced firing in thalamic cells. The return of endogenous brain electrical activities, after reinstatement of a control isoflurane concentration, led to the recovery of cortical neurons excitability and sensory responsiveness. These findings demonstrate the persistence of a certain level of cell excitability and sensory integration in the isoelectric state and the full recovery of cortico-thalamic functions after restoration of internal cerebral activities.

Electroencephalography (EEG) Technology Applications and Available Devices

The electroencephalography (EEG) sensor has become a prominent sensor in the study of brain activity. Its applications extend from research studies to medical applications. This review paper explores various types of EEG sensors and their applications. This paper is for an audience that comprises engineers, scientists and clinicians who are interested in learning more about the EEG sensors, the various types, their applications and which EEG sensor would suit a specific task. The paper also lists the details of each of the sensors currently available in the market, their technical specs, battery life, and where they have been used and what their limitations are.

Managing disorders of consciousness: the role of electroencephalography

Disorders of consciousness (DOC) are an important but still underexplored entity in neurology. Novel electroencephalography (EEG) measures are currently being employed for improving diagnostic classification, estimating prognosis and supporting medicolegal decision-making in DOC patients. However, complex recording protocols, a confusing variety of EEG measures, and complicated analysis algorithms create roadblocks against broad application. We conducted a systematic review based on English-language studies in PubMed, Medline and Web of Science databases. The review structures the available knowledge based on EEG measures and analysis principles, and aims at promoting its translation into clinical management of DOC patients.

Early Consciousness Disorder in Acute Large Hemispheric Infarction: An Analysis Based on Quantitative EEG and Brain Network Characteristics

BACKGROUND

Large hemispheric infarction (LHI) is an ischemic stroke affecting at least two-thirds of the middle cerebral artery territory, with or without involvement of the anterior cerebral artery or posterior cerebral artery, and approximately 77% of LHI patients have early consciousness disorder (ECD). We constructed a functional brain network for LHI patients with an acute consciousness disorder to identify new diagnostic markers related to ECDs by analyzing brain network characteristics and mechanisms.

METHODS

Between August 1, 2017, and September 30, 2018, patients with acute (< 1 month) LHI were admitted to the neurocritical care unit at Xuanwu Hospital of Capital Medical University. Electroencephalography (EEG) data were recorded, and the MATLAB platform (2017b) was used to calculate spectral power, entropy, coherence and phase synchronization. The quantitative EEG and brain network characteristics of different consciousness states and different frequency bands were analyzed (α = 0.05). EEG data were recorded 38 times in 30 patients, 25 of whom were in the ECD group, while 13 patients were in the conscious group.

RESULTS

(1) Spectral power analysis: The conscious group had higher beta relative spectral power across the whole brain, higher alpha spectral power in the frontal-parietal lobe on the infarction contralateral side, and lower theta and delta spectral power in the central-occipital lobe on the infarction contralateral side than the ECD group. (2) Entropy analysis: The conscious group had higher approximate entropy (ApEn) and permutation entropy (PeEn) across the whole brain than the ECD group. (3) Coherence: The conscious group had higher alpha coherence in nearly the whole brain and higher beta coherence in the bilateral frontal-parietal and parietal-occipital lobes than the ECD group. (4) Phase synchronization: The conscious group had higher alpha and beta synchronization in nearly the whole brain, particularly in the frontal-parietal and parietal-occipital lobes, than the ECD group. (5) Graph theory: The conscious group had higher small-worldness in each frequency band than the ECD group.

CONCLUSION

In patients with LHI, higher levels of consciousness were associated with more alpha and beta oscillations and fewer delta and theta oscillations. Higher ApEn, PeEn, total brain connectivity, and small-worldness and a wider signal distribution range corresponded to a higher consciousness level.

Prevalence of electrographic seizure in dogs and cats undergoing electroencephalography and clinical characteristics and outcome for dogs and cats with and without electrographic seizure: 104 cases (2009-2015)

OBJECTIVE

To determine the prevalence of electrographic seizure (ES) and electrographic status epilepticus (ESE) in dogs and cats that underwent electroencephalography (EEG) because of suspected seizure activity and to characterize the clinical characteristics, risk factors, and in-hospital mortality rates for dogs and cats with ES or ESE.

DESIGN

Retrospective case series.

ANIMALS

89 dogs and 15 cats.

PROCEDURES

Medical records of dogs and cats that underwent EEG at a veterinary neurology service between May 2009 and April 2015 were reviewed. Electrographic seizure was defined as ictal discharges that evolved in frequency, duration, or morphology and lasted at least 10 seconds, and ESE was defined as ES that lasted ≥ 10 minutes. Patient signalment and history, physical and neurologic examination findings, diagnostic test results, and outcome were compared between patients with and without ES or ESE.

RESULTS

Among the 104 patients, ES and ESE were diagnosed in 21 (20%) and 12 (12%), respectively. Seventeen (81%) patients with ES had no or only subtle signs of seizure activity. The in-hospital mortality rate was 48% and 50% for patients with ES and ESE, respectively, compared with 19% for patients without ES or ESE. Risk factors for ES and ESE included young age, overt seizure activity within 8 hours before EEG, and history of cluster seizures.

CONCLUSIONS AND CLINICAL REVELANCE

Results indicated that ES and ESE were fairly common in dogs and cats with suspected seizure activity and affected patients often had only subtle clinical signs. Therefore, EEG is necessary to detect patients with ES and ESE.

EEG signal varies with different outcomes in comatose patients: A quantitative method of electroencephalography reactivity

BACKGROUND

Electroencephalographic reactivity (EEG-R) is a major predictor of outcome in comatose patients; however, the inter-rater reliability is limited due to the lack of homogeneous stimuli and quantitative interpretation.

NEW METHODS

EEG-R testing was employed in comatose patients by quantifiable electrical stimulation. Reactivity at different frequency bands was computed as the difference between pre- and post-stimulations in power spectra and connectivity function (including magnitude squared coherence and transfer entropy). The clinical outcomes were dichotomized as good and poor according to the recovery of consciousness. Signal discrimination of EEG-R was compared between the two groups.

RESULTS

A total of 18 patients (43%) regained consciousness at a 3-month follow-up. In the patients who regained consciousness, the EEG power increased significantly (P < 0.05) at the Alpha and Beta frequency bands after stimulation as compared to those with no behavioral awakening. Also, connectivity enhancement (including linear and nonlinear analysis) in the Beta and Gamma bands and connectivity decrease (nonlinear transfer entropy analysis) in the Delta band after stimulus were observed in the good outcome group.

COMPARISON WITH EXISTING METHOD(S)

In this study, the combined use of quantifiable stimulation and quantitative analysis shed new light on differentiating brain responses in comatose patients with good and poor outcomes as well as exploring the nature of EEG changes concerning the recovery of consciousness.

CONCLUSIONS

The combination of quantifiable electrical stimulation and quantitative analysis with spectral power and connectivity for the EEG-R may be a promising method to predict the outcome of comatose patients.

Electroencephalography in Predicting Short-Term Clinical Outcomes after Cardiac Arrest

BACKGROUND

Early consciousness recovery after cardiac arrest (CA) is one of the most explicit and self-evident prognostic factors for clinical outcomes. We aimed to evaluate the prognostic value of electroencephalography (EEG) phenotypes according to the American Clinical Neurophysiology Society's Critical Care EEG classification for predicting early recovery after CA.

METHODS

Consecutive patients admitted to the ICU after CA were enrolled. We analyzed Glasgow Coma Scale (GCS) score within 10 days after CA and evaluated mortality within 28 days according to EEG pattern subtype.

RESULTS

Among the total of 71 patients, 9 had periodic discharges (PDs) EEG pattern, 4 had rhythmic delta activity (RDA), 8 had spike-and-wave (SW), 22 had low voltage, 5 had burst suppression, and 23 had other EEG patterns. Initial GCS scores, GCS scores 3 days after CA (or 3 days after targeted temperature management [TTM]), and 10 days after CA (or 10 days after TTM) were significantly different among EEG subtypes (p < 0.001, respectively) (Table 2). GCS scores were significantly higher in RDA and the other EEG group compared to the PDs, SW, low voltage, and burst suppression groups (p < 0.001). Significant group × time interactions were observed for the follow-up period between EEG phenotypes (p < 0.001) demonstrating the most increase in the other EEG pattern group.

CONCLUSIONS

Consciousness states were significantly worse in the PDs, SW, burst suppression, and low-voltage groups compared to the RDA and the other EEG pattern within 10 days after CA. The degree of consciousness recovery differed significantly by EEG pattern subtype within 10 days.

Topography of MR lesions correlates with standardized EEG pattern in early comatose survivors after cardiac arrest

AIM

Multimodal prognostication in comatose patients after cardiac arrest (CA) is complicated by the fact that different modalities are usually not independent. Here we set out to systematically correlate early EEG and MRI findings.

METHODS

89 adult patients from a prospective register who underwent at least one EEG and one MRI in the acute phase after CA were included. The EEGs were characterized using pre-existent standardized categories (highly malignant, malignant, benign). For MRIs, the apparent diffusion coefficient (ADC) was computed in pre-defined regions. We then introduced a novel classification based on the topography of ADC reduction (MR-lesion pattern (MLP) 1: no lesion; MLP 2: purely cortical lesions; MLP 3: involvement of the basal ganglia; MLP 4 involvement of other deep grey matter regions).

RESULTS

EEG background reactivity and EEG background continuity were strongly associated with a lower MLP value (p < 0.001 and p = 0.003 respectively). The EEG categories highly malignant, malignant and benign were strongly correlated with the MLP values (rho = 0.46, p < 0.001).

CONCLUSION

The MRI lesions are highly correlated with the EEG pattern. Our results suggest that performing MRI in comatose patients after CA with either highly malignant or with a benign EEG pattern is unlikely to yield additional useful information for prognostication, and should therefore be performed in priority in patients with intermediate EEG patterns ("malignant pattern").

Electroencephalographic Reporting for Refractory Status Epilepticus

PURPOSE

We aimed to determine whether clinical EEG reports obtained from children in the intensive care unit with refractory status epilepticus could provide data for comparative effectiveness research studies.

METHODS

We conducted a retrospective descriptive study to assess the documentation of key variables within clinical continuous EEG monitoring reports based on the American Clinical Neurophysiology Society's standardized EEG terminology for children with refractory status epilepticus from 10 academic centers. Two pediatric electroencephalographers reviewed the EEG reports. We compared reports generated using free text or templates.

RESULTS

We reviewed 191 EEG reports. Agreement between the electroencephalographers regarding whether a variable was described in the report ranged from fair to very good. The presence of electrographic seizures (ES) was documented in 46% (87/191) of reports, and these reports documented the time of first ES in 64% (56/87), ES duration in 72% (63/85), and ES frequency in 68% (59/87). Reactivity was documented in 16% (31/191) of reports, and it was more often documented in template than in free-text reports (40% vs. 14%, P = 0.006). Other variables were not differentially reported in template versus free-text reports.

CONCLUSIONS

Many key EEG features are not documented consistently in clinical continuous EEG monitoring reports, including ES characteristics and reactivity assessment. Standardization may be needed for clinical EEG reports to provide informative data for large multicenter observational studies.

Predicting outcome in patients with moderate to severe traumatic brain injury using electroencephalography

BACKGROUND

Better outcome prediction could assist in reliable quantification and classification of traumatic brain injury (TBI) severity to support clinical decision-making. We developed a multifactorial model combining quantitative electroencephalography (qEEG) measurements and clinically relevant parameters as proof of concept for outcome prediction of patients with moderate to severe TBI.

METHODS

Continuous EEG measurements were performed during the first 7 days of ICU admission. Patient outcome at 12 months was dichotomized based on the Extended Glasgow Outcome Score (GOSE) as poor (GOSE 1-2) or good (GOSE 3-8). Twenty-three qEEG features were extracted. Prediction models were created using a Random Forest classifier based on qEEG features, age, and mean arterial blood pressure (MAP) at 24, 48, 72, and 96 h after TBI and combinations of two time intervals. After optimization of the models, we added parameters from the International Mission for Prognosis And Clinical Trial Design (IMPACT) predictor, existing of clinical, CT, and laboratory parameters at admission. Furthermore, we compared our best models to the online IMPACT predictor.

RESULTS

Fifty-seven patients with moderate to severe TBI were included and divided into a training set (n = 38) and a validation set (n = 19). Our best model included eight qEEG parameters and MAP at 72 and 96 h after TBI, age, and nine other IMPACT parameters. This model had high predictive ability for poor outcome on both the training set using leave-one-out (area under the receiver operating characteristic curve (AUC) = 0.94, specificity 100%, sensitivity 75%) and validation set (AUC = 0.81, specificity 75%, sensitivity 100%). The IMPACT predictor independently predicted both groups with an AUC of 0.74 (specificity 81%, sensitivity 65%) and 0.84 (sensitivity 88%, specificity 73%), respectively.

CONCLUSIONS

Our study shows the potential of multifactorial Random Forest models using qEEG parameters to predict outcome in patients with moderate to severe TBI.

Up and Down States of Cortical Neurons in Focal Limbic Seizures

Recent work suggests an important role for cortical-subcortical networks in seizure-related loss of consciousness. Temporal lobe seizures disrupt subcortical arousal systems, which may lead to depressed cortical function and loss of consciousness. Extracellular recordings show ictal neocortical slow waves at about 1 Hz, but it is not known whether these simply represent seizure propagation or alternatively deep sleep-like activity, which should include cortical neuronal Up and Down states. In this study, using in vivo whole-cell recordings in a rat model of focal limbic seizures, we directly examine the electrophysiological properties of cortical neurons during seizures and deep anesthesia. We found that during seizures, the membrane potential of frontal cortical secondary motor cortex layer 5 neurons fluctuates between Up and Down states, with decreased input resistance and increased firing rate in Up states when compared to Down states. Importantly, Up and Down states in seizures are not significantly different from those in deep anesthesia, in terms of membrane potential, oscillation frequency, firing rate, and input resistance. By demonstrating these fundamental similarities in cortical electrophysiology between deep anesthesia and seizures, our results support the idea that a state of decreased cortical arousal may contribute to mechanisms of loss of consciousness during seizures.

EEG pattern predicts awakening of comatose patients after cardiopulmonary resuscitation

OBJECTIVE

To explore the value of electroencephalogram (EEG) pattern in predicting awakening of comatose patients after cardiopulmonary resuscitation (CPR).

METHODS

A retrospective cohort study was conducted on comatose patients after CPR in the neuro-critical care unit of Xuanwu Hospital, Capital Medical University, from 2002 to 2018. The included patients received clinical evaluation, and the Glasgow coma scale (GCS) score was recorded. Bedside EEG monitoring was performed for visual grading and reactivity detection. The 3-month prognostic assessment was performed using the Glasgow outcome scale (GOS). The patients were dichotomized into the awakening group (GOS 3-5) and the unawakening group (GOS 1-2).

RESULTS

A total of 160 patients were included. There was no significant difference in the baseline data between the two groups except that the GCS score of the awakening group was higher (P = 0.000). Different EEG patterns were used to predict awakening from coma. As a result, the slow wave pattern showed the highest accuracy (73.1%, 95% CI: 0.66-0.79), and the sensitivity and specificity reached 61.3% (95% CI: 0.48-0.73) and 80.6% (95% CI: 0.71-0.88), respectively. Compared with EEG reactivity, the slow wave pattern was more sensitive in predicting awakening. It was also more specific than GCS in predicting awakening. The slow wave pattern within different time frame after coma was used to predict the prognosis of awakening, suggesting that the accuracy (100%, 95% CI: 0.75-1.00), sensitivity (100%, 95% CI: 0.46-1.00), and specificity (100%, 95% CI: 0.63-1.00) of predicting awakening within 8-14 days were the highest.

CONCLUSIONS

The slow wave pattern of EEG had a good predictive value for awakening in comatose patients after CPR, and the highest accuracy occurred within 8-14 days from coma.

A pragmatic approach to intravenous anaesthetics and electroencephalographic endpoints for the treatment of refractory and super-refractory status epilepticus in critical care

Status epilepticus is a common neurological emergency, with overall mortality around 20%. Over half of cases are first time presentations of seizures. The pathological process by which spontaneous seizures are generated arises from an imbalance in excitatory and inhibitory neuronal networks, which if unchecked, can result in alterations in intracellular signalling pathways and electrolyte shifts, which bring about changes in the blood brain barrier, neuronal cell death and eventually cerebral atrophy. This narrative review focusses on the treatment of status epilepticus in adults. Anaesthetic agents interrupt neuronal activity by enhancing inhibitory or decreasing excitatory transmission, primarily via GABA and NMDA receptors. Intravenous anaesthetic agents are commonly used as second or third line drugs in the treatment of refractory status epilepticus, but the optimal timing and choice of anaesthetic drug has not yet been established by high quality evidence. Titration of antiepileptic and anaesthetic drugs in critically ill patients presents a particular challenge, due to alterations in drug absorbtion and metabolism as well as changes in drug distrubution, which arise from fluid shifts and altered protein binding. Furthermore, side effects associated with prolonged infusions of anaesthetic drugs can lead to multi-organ dysfunction and a need for critical care support. Electroencelography can identify patterns of burst suppression, which may be a target to guide weaning of intravenous therapy. Continuous elctroencephalography has the potential to directly impact clinical care, but despite its utility, major barriers exist which have limited its widespread use in clinical practice. A flow chart outlining the timing and dosage of anaesthetic agents used at our institution is provided.

Alpha rhythm collapse predicts iso-electric suppressions during anesthesia

Could an overly deep sedation be anticipated from ElectroEncephaloGram (EEG) patterns? We report here motifs hidden in the EEG signal that predict the appearance of Iso-Electric Suppressions (IES), observed during epileptic encephalopathies, drug intoxications, comatose, brain death or during anesthetic over-dosage that are considered to be detrimental. To show that IES occurrences can be predicted from EEG traces dynamics, we focus on transient suppression of the alpha rhythm (8-14 Hz) recorded for 80 patients, that had a Propofol target controlled infusion of 5 μg/ml during a general anesthesia. We found that the first time of appearance as well as changes in duration of these Alpha-Suppressions (αS) are two parameters that anticipate the appearance of IES. Using machine learning, we predicted IES appearance from the first 10 min of EEG (AUC of 0.93). To conclude, transient motifs in the alpha rhythm predict IES during anesthesia and can be used to identify patients, with higher risks of post-operative complications.

Malnutrition-related hyperammonemic encephalopathy presenting with burst suppression: a case report

BACKGROUND

Hyperammonemia is a common cause of metabolic encephalopathy, mainly related to hepatic cirrhosis. Numerous nonhepatic etiologies exist but they are infrequent and not well known, thus, leading to misdiagnosis and inadequate care. Electroencephalography has a proven diagnostic and prognostic role in comatose patients. Burst suppression is a preterminal pattern found in deep coma states and is rarely associated with metabolic causes.

CASE PRESENTATION

We report the case of an 81-year-old Caucasian man presenting with rapidly progressive somnolence and mutism. Soon after his arrival in our hospital, he developed profound coma. A comprehensive diagnostic workup was unremarkable except for admission electroencephalography showing diffuse slowing of cerebral activity with an intermittent pattern of burst suppression. He was admitted to our intensive care unit for supportive care where malnutrition-related hyperammonemia was diagnosed. His clinical course was spontaneously favorable and follow-up electroencephalography demonstrated normal cerebral activity.

CONCLUSIONS

Nonhepatic hyperammonemia is a rare and potentially reversible cause of encephalopathy. Ammonia level measurement should be part of the diagnostic workup in patients with unexplained coma, particularly in the setting of nutritional deficiencies or nutritional supply. Detection of diffuse and nonspecific mild to moderate slowing of cerebral activity (theta-delta ranges) on electroencephalography is common. In contrast, to the best of our knowledge, burst suppression has never been described in association with hyperammonemia.

New criteria of burst suppression on electroencephalogram in dogs anesthetized with sevoflurane

Burst suppression on electroencephalogram (EEG) is defined as suppression periods longer than 0.5 s during which the amplitude does not exceed 5 μV in human. The aims of this study were; 1) an attempt of creating new criteria of burst suppression in dogs; and 2) a survey on accuracy of sub-parameter of Bispectral index (BIS). Using a BIS monitor, suppression ratio (SR_BIS) and raw-EEG data were recorded at 2.0%, 2.5%, 3.0%, 3.5%, 4.0%, and 5.0% end-tidal sevoflurane concentration (ETSEV) in 6 beagle dogs. The minimum ETSEV at which burst suppression was visually confirmed (ETSEV_BS) was determined. By applying various duration and voltage threshold to criteria, suppression ratio was calculated (SR). Using the minimum balanced error rate (BER), new criteria consisting of the minimum duration of 0.35 s and the maximum threshold of 2.25 μV that provided SR > 0 above ETSEV_BS was screened. SR was set by these criteria (SR_BER) and by manual inspection (SR_TRUE). The median detection rate of SR_BER/SR_TRUE was a statistically significant increase (p < .01) compared to that of SR_BIS/SR_TRUE (77% and 17% at 3.5% ETSEV, 89% and 19% at 4.0% ETSEV, and 86% and 84% at 5.0% ETSEV, respectively). In addition, between SR_BER and SR_TRUE evaluated by regression and Bland-Altman analyses, there was a strong correlation (r = 0.967, p < .001) and a moderate agreement (Limits of agreement: -7.14 ± 13.95). The method using BER may help to establish new criteria of burst suppression to grasp the excessive deep level of anesthesia.

Delirium in the critically ill patient

Delirium is an acute and transient brain dysfunction that is characterized by disturbances in consciousness, affecting both its content (i.e., attention) and level (i.e., arousal). It affects as many as 50% of those admitted to an intensive care unit (ICU). Once believed to be an inconsequential outcome of critical illness, it is now recognized that delirium is harmful in both the short- and long-term. Despite occurring frequently in critically ill patients, delirium often goes unrecognized. Well-validated delirium screening tools, designed for use in the ICU, should be used to reliably detect delirium. The first step in delirium treatment is to identify and address potentially modifiable risk factors. Multiple trials have shown that benzodiazepines are a risk factor for delirium in a dose-dependent manner. Sedation with nonbenzodiazepine-based strategies are an effective means by which to reduce delirium. Nonpharmacologic strategies such as those which seek to reduce sensory impairment, sleep deprivation, and immobility are effective. Pharmacologic treatment with antipsychotics, though commonly used, is not supported by findings from placebo-controlled trials. Recent data support from multiple trials support the use of the "ABCDEF bundle" as a means by which to reduce delirium.