Electroencephalography Measures are Useful for Identifying Large Acute Ischemic Stroke in the Emergency Department

A novel stroke classification model based on EEG feature fusion

Stroke is the leading cause of disability and death worldwide. It severely affects patients' quality of life and imposes a huge burden on the society in general. The diagnosis of stroke relies predominantly on the use of neuroimaging. The identification of stroke using electroencephalogram (EEG) in the clinical assessment of stroke has been underutilized. An EEG feature fusion based light gradient-boosting machine (LightGBM) model was proposed to achieve a fast diagnosis of non-stroke, ischemic stroke, and hemorrhagic stroke. This study aims to capture the essential difference between non-stroke, ischemic stroke, and hemorrhagic stroke. An optimal fusion feature set originated from approximate entropy and fuzzy entropy of EEG signal was constructed. To verify the effectiveness of the EEG fusion feature, the Tree-structured Parzen Estimator optimized LightGBM classifier (TPELGBM) was used for the classification. The ZJU4H EEG dataset used for analysis in this study was obtained from the Fourth Affiliated Hospital of Zhejiang University, China. The proposed ApFu-TPELGBM model exhibited excellent classification results, which achieved a precision of 0.9676, recall of 0.9669, and f1-score of 0.9672. To our knowledge, it was the most accurate classifier for EEG-based stroke diagnosis so far. The ApFu-TPELGBM model can determine the stroke type anywhere EEG signals can be collected, even before the patient is admitted to a hospital. Rapid and accurate diagnosis of stroke using EEG signals may become a promising approach in the clinical assessment of stroke.

Point-of-Care Electroencephalography in Acute Neurological Care: A Narrative Review

Point-of-care electroencephalography (POC-EEG) systems are rapid-access, reduced-montage devices designed to address the limitations of conventional EEG (conv-EEG), enabling faster neurophysiological assessment in acute settings. This review evaluates their clinical impact, diagnostic performance, and feasibility in non-convulsive status epilepticus (NCSE), traumatic brain injury (TBI), stroke, and delirium. A comprehensive search of Medline, Scopus, and Embase identified 69 studies assessing 15 devices. In suspected NCSE, POC-EEG facilitates rapid seizure detection and prompt diagnosis, making it particularly effective in time-sensitive and resource-limited settings. Its after-hours availability and telemedicine integration ensure continuous coverage. AI-assisted tools enhance interpretability and accessibility, enabling use by non-experts. Despite variability in accuracy, it supports triaging, improving management, treatment decisions and outcomes while reducing hospital stays, transfers, and costs. In TBI, POC-EEG-derived quantitative EEG (qEEG) indices reliably detect structural lesions, support triage, and minimize unnecessary CT scans. They also help assess concussion severity and predict recovery. For strokes, POC-EEG aids triage by detecting large vessel occlusions (LVOs) with high feasibility in hospital and prehospital settings. In delirium, spectral analysis and AI-assisted models enhance diagnostic accuracy, broadening its clinical applications. Although POC-EEG is a promising screening tool, challenges remain in diagnostic variability, technical limitations, and AI optimization, requiring further research.

Clinical and neurophysiological predictors of the functional outcome in right-hemisphere stroke

OBJECTIVE

The aim of the present study is to examine the relationship between EEG measures and functional recovery in right-hemisphere stroke patients.

METHODS

Participants with stroke (PS) and neurologically unimpaired controls (UC) were enrolled. At enrolment, all participants were assessed for motor and cognitive functioning with specific scales (motricity index, trunk control test, Level of Cognitive Functioning, and Functional Independence Measure (FIM). Moreover, EEG data were recorded. At discharge, participants were re-tested with the FIM RESULTS: Powers in the delta, theta, alpha, and beta bands and connectivity within the fronto-parietal network were compared between groups. Then, the between-group discriminative EEG measures and the motor/cognitive scales were used to feed a machine learning algorithm to predict FIM scores at discharge and the length of hospitalization (LoH). Higher delta, theta, and beta and impaired connectivity were found in PS compared to UC. Moreover, motor/cognitive functioning, beta power, and fronto-parietal connectivity predicted the FIM score at discharge and the LoH (accuracy=73.2 % and 85.2 % respectively).

CONCLUSIONS

Results show that the integration of motor/cognitive scales and EEG measures can reveal the rehabilitative potentials of PS predicting their functional outcome and LoH.

SIGNIFICANCE

Synergistic clinical and electrophysiological models can support rehabilitative decision-making.

Changes in Neonatal Intraoperative Electroencephalogram Alpha: Delta Ratios Precede Neurologic Injury

Background: Unrecognized intraoperative cerebral ischemia during neonatal aortic arch reconstruction may precede neurologic injury. Electroencephalogram (EEG) alpha:delta ratio (A:D) changes predict cerebral ischemia; however, if A:D differences can identify ischemia during neonatal antegrade cerebral perfusion (ACP) and aortic arch reconstruction is unknown. We hypothesized that A:D changes would precede neurologic injury. Methods: Simultaneous EEG derived left versus right: hemispheric and anterior cerebral A:Ds were retrospectively measured at baseline and every 5 min during arterial cannulation, cooling, ACP, and the rewarming phases of the operation. A paired left versus right A:D difference >25% was considered significant for ischemia, and the duration of a significant and continuous A:D difference was quantified in minutes. Neonates were divided into two groups: (1) new neurologic injury (stroke or seizure) and (2) no known neurologic injury. Results: From 72 neonates, there were no significant differences in the baseline A:Ds. Seven neonates (9.7%) developed a new neurologic injury (seizure = 3, stroke = 2, seizure and stroke = 2). Male gender and longer ACP times were significantly associated with neurologic injury. In neonates with a neurologic injury, the duration of a significant and continuous A:D difference was longer within the hemispheric and anterior regions. Multivariable analysis demonstrated that a significant and continuous anterior A:D difference (odds ratio: 1.345, 95% CI 1.058-1.712; P = .01) was independently associated with neurologic injury. Conclusions: A longer continuous anterior A:D difference > 25% was independently associated with neurologic injury. Intraoperative EEG monitoring could be considered during neonatal arch reconstruction.

Detection of early neurological deterioration using a quantitative electroencephalography system in patients with large vessel occlusion stroke after endovascular treatment

BACKGROUND

Early neurological deterioration (END) is a serious complication in patients with large vessel occlusion (LVO) stroke. However, modalities to monitor neurological function after endovascular treatment (EVT) are lacking. This study aimed to evaluate the diagnostic accuracy of a quantitative electroencephalography (qEEG) system for detecting END.

METHODS

In this prospective, nested case-control study, we included 47 patients with anterior circulation LVO stroke and 34 healthy adults from different clinical centers in Tianjin, China, from May 2023 to January 2024. Patients with stroke underwent EEG at admission and after EVT. The diagnostic accuracy of qEEG features for END was evaluated by receiver operating characteristic curve analysis, and the feasibility was evaluated by the percentage of artifact-free data and device-related adverse events.

RESULTS

14 patients with stroke had END (29.8%, 95% CI 16.2% to 43.4%), with most developed within 12 hours of recanalization (n=11). qEEG features showed significant correlations with National Institutes of Health Stroke Scale score and infarct volume. After matching, 13 patients with END and 26 controls were included in the diagnostic analysis. Relative alpha power demonstrated the highest diagnostic accuracy for the affected and unaffected hemispheres. The optimal electrode positions were FC3/4 in the unaffected hemisphere, and F7/8 and C3/4 in the affected hemisphere. No device-related adverse events were reported.

CONCLUSION

The qEEG system exhibits a high diagnostic accuracy for END and may be a promising tool for monitoring neurological function. The identification of optimal electrode positions may enhance device convenience.

CLINICAL TRIAL REGISTRATION

ChiCTR 2300070829.

MSE-VGG: A Novel Deep Learning Approach Based on EEG for Rapid Ischemic Stroke Detection

Ischemic stroke is a type of brain dysfunction caused by pathological changes in the blood vessels of the brain which leads to brain tissue ischemia and hypoxia and ultimately results in cell necrosis. Without timely and effective treatment in the early time window, ischemic stroke can lead to long-term disability and even death. Therefore, rapid detection is crucial in patients with ischemic stroke. In this study, we developed a deep learning model based on fusion features extracted from electroencephalography (EEG) signals for the fast detection of ischemic stroke. Specifically, we recruited 20 ischemic stroke patients who underwent EEG examination during the acute phase of stroke and collected EEG signals from 19 adults with no history of stroke as a control group. Afterwards, we constructed correlation-weighted Phase Lag Index (cwPLI), a novel feature, to explore the synchronization information and functional connectivity between EEG channels. Moreover, the spatio-temporal information from functional connectivity and the nonlinear information from complexity were fused by combining the cwPLI matrix and Sample Entropy (SaEn) together to further improve the discriminative ability of the model. Finally, the novel MSE-VGG network was employed as a classifier to distinguish ischemic stroke from non-ischemic stroke data. Five-fold cross-validation experiments demonstrated that the proposed model possesses excellent performance, with accuracy, sensitivity, and specificity reaching 90.17%, 89.86%, and 90.44%, respectively. Experiments on time consumption verified that the proposed method is superior to other state-of-the-art examinations. This study contributes to the advancement of the rapid detection of ischemic stroke, shedding light on the untapped potential of EEG and demonstrating the efficacy of deep learning in ischemic stroke identification.

Differentiating ischemic stroke patients from healthy subjects using a large-scale, retrospective EEG database and machine learning methods

OBJECTIVES

We set out to develop a machine learning model capable of distinguishing patients presenting with ischemic stroke from a healthy cohort of subjects. The model relies on a 3-min resting electroencephalogram (EEG) recording from which features can be computed.

MATERIALS AND METHODS

Using a large-scale, retrospective database of EEG recordings and matching clinical reports, we were able to construct a dataset of 1385 healthy subjects and 374 stroke patients. With subjects often producing more than one recording per session, the final dataset consisted of 2401 EEG recordings (63% healthy, 37% stroke).

RESULTS

Using a rich set of features encompassing both the spectral and temporal domains, our model yielded an AUC of 0.95, with a sensitivity and specificity of 93% and 86%, respectively. Allowing for multiple recordings per subject in the training set boosted sensitivity by 7%, attributable to a more balanced dataset.

CONCLUSIONS

Our work demonstrates strong potential for the use of EEG in conjunction with machine learning methods to distinguish stroke patients from healthy subjects. Our approach provides a solution that is not only timely (3-minutes recording time) but also highly precise and accurate (AUC: 0.95).

Clinical approaches for poststroke seizure: a review

Poststroke seizure is a potential complication of stroke, which is the most frequent acute symptomatic seizure in adults. Patients with stroke may present with an abnormal or aggressive behavior accompanied by altered mental status and symptoms, such as hemiparesis, dysarthria, and sensory deficits. Although stroke manifestations that mimic seizures are rare, diagnosing poststroke seizures can be challenging when accompanied with negative postictal symptoms. Differential diagnoses of poststroke seizures include movement disorders, syncope, and functional (nonepileptic) seizures, which may present with symptoms similar to seizures. Furthermore, it is important to determine whether poststroke seizures occur early or late. Seizures occurring within and after 7 d of stroke onset were classified as early and late seizures, respectively. Early seizures have the same clinical course as acute symptomatic seizures; they rarely recur or require long-term antiseizure medication. Conversely, late seizures are associated with a risk of recurrence similar to that of unprovoked seizures in a patient with a focal lesion, thereby requiring long-term administration of antiseizure medication. After diagnosis, concerns regarding treatment strategies, treatment duration, and administration of primary and secondary prophylaxis often arise. Antiseizure medication decisions for the initiation of short-term primary and long-term secondary seizure prophylaxis should be considered for patients with stroke. Antiseizure drugs such as lamotrigine, carbamazepine, lacosamide, levetiracetam, phenytoin, and valproate may be administered. Poststroke seizures should be diagnosed systematically through history with differential diagnosis; in addition, classifying them as early or late seizures can help to determine treatment strategies.

High-density cortical µECoG arrays concurrently track spreading depolarizations and long-term evolution of stroke in awake rats

Spreading depolarizations (SDs) are widely recognized as a major contributor to the progression of tissue damage from ischemic stroke even if blood flow can be restored. They are characterized by negative intracortical waveforms of up to -20 mV, propagation velocities of 3 - 6 mm/min, and massive disturbance of membrane ion homeostasis. High-density, micro-electrocorticographic (μECoG) epidural electrodes and custom, DC-coupled, multiplexed amplifiers, were used to continuously characterize and monitor SD and µECoG cortical signal evolution in awake, moving rats over days. This highly innovative approach can define these events over a large brain surface area (~ 3.4 × 3.4 mm), extending across the boundaries of the stroke, and offers sufficient electrode density (60 contacts total per array for a density of 5.7 electrodes / mm2) to measure and determine the origin of SDs in relation to the infarct boundaries. In addition, spontaneous ECoG activity can simultaneously be detected to further define cortical infarct regions. This technology allows us to understand dynamic stroke evolution and provides immediate cortical functional activity over days. Further translational development of this approach may facilitate improved treatment options for acute stroke patients.

A quantitative EEG index for the recognition of arterial ischemic stroke in children

OBJECTIVE

To describe and assess performance of the Correlate Of Injury to the Nervous system (COIN) index, a quantitative electroencephalography (EEG) metric designed to identify areas of cerebral dysfunction concerning for stroke.

METHODS

Case-control study comparing continuous EEG data from children with acute ischemic stroke to children without stroke, with or without encephalopathy. COIN is calculated continuously and compares EEG power between cerebral hemispheres. Stroke relative infarct volume (RIV) was calculated from quantitative neuroimaging analysis. Significance was determined using a two-sample t-test. Sensitivity, specificity, and accuracy were measured using logistic regression.

RESULTS

Average COIN values were -34.7 in the stroke cohort compared to -9.5 in controls without encephalopathy (p = 0.003) and -10.5 in controls with encephalopathy (p = 0.006). The optimal COIN cutoff to discriminate stroke from controls was -15 in non-encephalopathic and -18 in encephalopathic controls with >92% accuracy in strokes with RIV > 5%. A COIN cutoff of -20 allowed discrimination between strokes with <5% and >5% RIV (p = 0.027).

CONCLUSIONS

We demonstrate that COIN can identify children with acute ischemic stroke.

SIGNIFICANCE

COIN may be a valuable tool for stroke identification in children. Additional studies are needed to determine utility as a monitoring technique for children at risk for stroke.

Subhairline Electroencephalography for the Detection of Large Vessel Occlusion Stroke

BACKGROUND

Endovascular thrombectomy is standard treatment for patients with anterior circulation large vessel occlusion stroke (LVO-a). Prehospital identification of these patients would enable direct routing to an endovascular thrombectomy-capable hospital and consequently reduce time-to-endovascular thrombectomy. Electroencephalography (EEG) has previously proven to be promising for LVO-a stroke detection. Fast and reliable electrode application, however, can remain a challenge. A potential alternative is subhairline EEG. We evaluated the diagnostic accuracy of subhairline EEG for LVO-a stroke detection.

METHODS AND RESULTS

We included adult patients with a suspected stroke or known LVO-a stroke and symptom onset time <24 hours. A single 3-minute EEG recording was performed at the emergency department, before endovascular thrombectomy, using 9 self-adhesive electrodes placed on the forehead and behind the ears. We evaluated the diagnostic accuracies of EEG features quantifying frequency band power and brain symmetry (pairwise derived Brain Symmetry Index) for LVO-a stroke detection using receiver operating characteristic analysis. EEG data were of sufficient quality for analysis in 51/52 (98%) included patients. Of these patients, 16 (31%) had an LVO-a stroke, 16 (31%) a non-LVO-a ischemic stroke, 5 (10%) a transient ischemic attack, and 14 (27%) a stroke mimic. Median symptom-onset-to-EEG-time was 266 (interquartile range 130-709) minutes. The highest diagnostic accuracy for LVO-a stroke detection was reached by the pairwise derived Brain Symmetry Index in the theta frequency band (area under the receiver operating characteristic curve 0.90; sensitivity 86%; specificity 83%).

CONCLUSIONS

Subhairline EEG could detect LVO-a stroke with high diagnostic accuracy and had high data reliability. These data suggest that subhairline EEG is potentially suitable as a prehospital stroke triage instrument.

Modern Prehospital Screening Technology for Emergent Neurovascular Disorders

Stroke is a serious neurological disease and a significant contributor to disability worldwide. Traditional in-hospital imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) remain the standard modalities for diagnosing stroke. The development of prehospital stroke detection devices may facilitate earlier diagnosis, initiation of stroke care, and ultimately better patient outcomes. In this review, the authors summarize the features of eight stroke detection devices using noninvasive brain scanning technology. The review summarizes the features of stroke detection devices including portable CT, MRI, transcranial Doppler ultrasound , microwave tomographic imaging, electroencephalography, near-infrared spectroscopy, volumetric impedance phaseshift spectroscopy, and cranial accelerometry. The technologies utilized, the indications for application, the environments indicated for application, the physical features of the eight stroke detection devices, and current commercial products are discussed. As technology advances, multiple portable stroke detection instruments exhibit the promising potential to expedite the diagnosis of stroke and enhance the time taken for treatment, ultimately aiding in prehospital stroke triage.

Correlation between EEG spectral power and cerebral perfusion in patients with acute ischemic stroke

Dry electrode electroencephalography (EEG) has the potential to diagnose ischemic stroke in the acute phase. In the current study we determined the correlation between EEG spectral power and ischemic stroke size and location as determined by computed tomography perfusion (CTP). Dry electrode EEG recordings were performed in patients with acute ischemic stroke in the emergency room. CTP preceded the EEG recordings as part of standard imaging protocol. Infarct core volume, total hypoperfused volume and local cerebral blood flow (CBF) were estimated with CTP. Additionally, global and local EEG spectral power were determined. We used Spearman's correlation coefficients to evaluate the correlation between variables. We included 27 patients (median age 72 [IQR:69-80] years, 15/27 [56%] men). Median CTP-to-EEG time was 32 (range:8-138) minutes. Hypoperfused volumes were estimated for 12/27 (44%) patients. Infarct core volume correlated best with global delta power (ρ = 0.76, p < 0.01), total hypoperfused volume with global alpha power (ρ = -0.58, p = 0.05), and local CBF with local alpha power (ρ = 0.43, p < 0.01). We conclude that dry electrode EEG signals slow down with increasing hypoperfused volume, which could potentially be used to discriminate between small and large ischemic strokes.

Qualitative electroencephalogram and its predictors in the diagnosis of stroke

Introduction

Stroke is a typical medical emergency that carries significant disability and morbidity. The diagnosis of stroke relies predominantly on the use of neuroimaging. Accurate diagnosis is pertinent for management decisions of thrombolysis and/or thrombectomy. Early identification of stroke using electroencephalogram (EEG) in the clinical assessment of stroke has been underutilized. This study was conducted to determine the relevance of EEG and its predictors with the clinical and stroke features.

Methods

A cross-sectional study was carried out where routine EEG assessment was performed in 206 consecutive acute stroke patients without seizures. The demographic data and clinical stroke assessment were collated using the National Institutes of Health Stroke Scale (NIHSS) score with neuroimaging. Associations between EEG abnormalities and clinical features, stroke characteristics, and NIHSS scores were evaluated.

Results

The mean age of the study population was 64.32 ± 12 years old, with 57.28% consisting of men. The median NIHSS score on admission was 6 (IQR 3-13). EEG was abnormal in more than half of the patients (106, 51.5%), which consisted of focal slowing (58, 28.2%) followed by generalized slowing (39, 18.9%) and epileptiform changes (9, 4.4%). NIHSS score was significantly associated with focal slowing (13 vs. 5, p < 0.05). Type of stroke and imaging characteristics were significantly associated with EEG abnormalities (p < 0.05). For every increment in NIHSS score, there are 1.08 times likely for focal slowing (OR 1.089; 95% CI 1.033, 1.147, p = 0.002). Anterior circulation stroke has 3.6 times more likely to have abnormal EEG (OR 3.628; 95% CI 1.615, 8.150, p = 0.002) and 4.55 times higher to exhibit focal slowing (OR 4.554; 95% CI 1.922, 10.789, p = 0.01).

Conclusion

The type of stroke and imaging characteristics are associated with EEG abnormalities. Predictors of focal EEG slowing are NIHSS score and anterior circulation stroke. The study emphasized that EEG is a simple yet feasible investigational tool, and further plans for advancing stroke evaluation should consider the inclusion of this functional modality.

Validating Existing Scales for Identification of Acute Stroke in an Inpatient Setting

Background and Purpose

A significant proportion of strokes occur while patients are hospitalized for other reasons. Numerous stroke scales have been developed and validated for use in pre-hospital and emergency department settings, and there is growing interest to adapt these scales for use in the inpatient setting. We aimed to validate existing stroke scales for inpatient stroke codes.

Methods

We retrospectively reviewed charts from inpatient stroke code activations at an urban academic medical center from January 2016 through December 2018. Receiver operating characteristic analysis was performed for each specified stroke scale including NIHSS, FAST, BE-FAST, 2CAN, FABS, TeleStroke Mimic, and LAMS. We also used logistic regression to identify independent predictors of stroke and to derive a novel scale.

Results

Of the 958 stroke code activations reviewed, 151 (15.8%) had a final diagnosis of ischemic or hemorrhagic stroke. The area under the curve (AUC) of existing scales varied from .465 (FABS score) to .563 (2CAN score). Four risk factors independently predicted stroke: (1) recent cardiovascular procedure, (2) platelet count less than 50 × 109 per liter, (3) gaze deviation, and (4) presence of unilateral leg weakness. Combining these 4 factors into a new score yielded an AUC of .653 (95% confidence interval [CI] .604-.702).

Conclusion

This study suggests that currently available stroke scales may not be sufficient to differentiate strokes from mimics in the inpatient setting. Our data suggest that novel approaches may be required to help with diagnosis in this unique population.

A Measure of Neural Function Provides Unique Insights into Behavioral Deficits in Acute Stroke

BACKGROUND

Clinical and neuroimaging measures incompletely explain behavioral deficits in the acute stroke setting. We hypothesized that electroencephalography (EEG)-based measures of neural function would significantly improve prediction of acute stroke deficits.

METHODS

Patients with acute stroke (n=50) seen in the emergency department of a university hospital from 2017 to 2018 underwent standard evaluation followed by a 3-minute recording of EEG at rest using a wireless, 17-electrode, dry-lead system. Artifacts in EEG recordings were removed offline and then spectral power was calculated for each lead pair. A primary EEG metric was DTABR, which is calculated as a ratio of spectral power: [(Delta*Theta)/(Alpha*Beta)]. Bivariate analyses and least absolute shrinkage and selection operator (LASSO) regression identified clinical and neuroimaging measures that best predicted initial National Institutes of Health Stroke Scale (NIHSS) score. Multivariable linear regression was then performed before versus after adding EEG findings to these measures, using initial NIHSS score as the dependent measure.

RESULTS

Age, diabetes status, and infarct volume were the best predictors of initial NIHSS score in bivariate analyses, confirmed using LASSO regression. Combined in a multivariate model, these 3 explained initial NIHSS score (adjusted r2=0.47). Adding any of several different EEG measures to this clinical model significantly improved prediction; the greatest amount of additional variance was explained by adding contralesional DTABR (adjusted r2=0.60, P<0.001).

CONCLUSIONS

EEG measures of neural function significantly add to clinical and neuroimaging for explaining initial NIHSS score in the acute stroke emergency department setting. A dry-lead EEG system can be rapidly and easily implemented. EEG contains information that may be useful early after stroke.

Normative Structure of Resting-State EEG in Bipolar Derivations for Daily Clinical Practice: A Pilot Study

We used numerical methods to define the normative structure of resting-state EEG (rsEEG) in a pilot study of 37 healthy subjects (10-74 years old), using a double-banana bipolar montage. Artifact-free 120-200 s epoch lengths were visually identified and divided into 1 s windows with a 10% overlap. Differential channels were grouped by frontal, parieto-occipital, and temporal lobes. For every channel, the power spectrum was calculated and used to compute the area for delta (0-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) bands and was log-transformed. Furthermore, Shannon's spectral entropy (SSE) and coherence by bands were computed. Finally, we also calculated the main frequency and amplitude of the posterior dominant rhythm. According to the age-dependent distribution of the bands, we divided the patients in the following three groups: younger than 20; between 21 and 50; and older than 51 years old. The distribution of bands and coherence was different for the three groups depending on the brain lobes. We described the normative equations for the three age groups and for every brain lobe. We showed the feasibility of a normative structure of rsEEG picked up with a double-banana montage.

The role of neurophysiological tools in the evaluation of ischemic stroke evolution: a narrative review

Ischemic stroke is characterized by a complex cascade of events starting from vessel occlusion. The term "penumbra" denotes the area of severely hypo-perfused brain tissue surrounding the ischemic core that can be potentially recovered if blood flow is reestablished. From the neurophysiological perspective, there are local alterations-reflecting the loss of function of the core and the penumbra-and widespread changes in neural networks functioning, since structural and functional connectivity is disrupted. These dynamic changes are closely related to blood flow in the affected area. However, the pathological process of stroke does not end after the acute phase, but it determines a long-term cascade of events, including changes of cortical excitability, that are quite precocious and might precede clinical evolution. Neurophysiological tools-such as Transcranial Magnetic Stimulation (TMS) or Electroencephalography (EEG)-have enough time resolution to efficiently reflect the pathological changes occurring after stroke. Even if they do not have a role in acute stroke management, EEG and TMS might be helpful for monitoring ischemia evolution-also in the sub-acute and chronic stages. The present review aims to describe the changes occurring in the infarcted area after stroke from the neurophysiological perspective, starting from the acute to the chronic phase.

Pathological changes of brain oscillations following ischemic stroke

Brain oscillations recorded in the extracellular space are among the most important aspects of neurophysiology data reflecting the activity and function of neurons in a population or a network. The signal strength and patterns of brain oscillations can be powerful biomarkers used for disease detection and prediction of the recovery of function. Electrophysiological signals can also serve as an index for many cutting-edge technologies aiming to interface between the nervous system and neuroprosthetic devices and to monitor the efficacy of boosting neural activity. In this review, we provided an overview of the basic knowledge regarding local field potential, electro- or magneto- encephalography signals, and their biological relevance, followed by a summary of the findings reported in various clinical and experimental stroke studies. We reviewed evidence of stroke-induced changes in hippocampal oscillations and disruption of communication between brain networks as potential mechanisms underlying post-stroke cognitive dysfunction. We also discussed the promise of brain stimulation in promoting post stroke functional recovery via restoring neural activity and enhancing brain plasticity.

The value of mobile magnetic resonance imaging in early warning for stroke: A prospective case-control study

AIMS

To evaluate the predictive value of mobile magnetic resonance imaging (MRI) in screening stroke.

METHODS

This was a prospective case-control study performed on healthy residents over 40 years old in remote rural areas of northern China between May 2019 and May 2020. Multivariate logistic regression and receiver operator characteristic curve (ROC) analysis were used to evaluate the screening model.

RESULTS

A total of 1,224 patients (500 [40.8%] men) enrolled, including 56 patients who suffered from stroke (aged 64.05 ± 7.27). The individuals who developed stroke were significantly older (P < 0.001), had a significantly higher occurrence of heart disease (P = 0.015), diabetes (P = 0.005), dyslipidemia (P = 0.009), and significantly increased waist circumference (P = 0.02), systolic blood pressure (SBP) (P = 0.003), glycosylated hemoglobin (HbA1c) level (P = 0.007), triglyceride (TG) level (P = 0.025), low density lipoprotein cholesterol (LDL-c) level (P = 0.04), and homocysteine (HCY) level (P < 0.001). Multivariate logistic regression analysis showed that age (OR = 1.055, 95% CI: 1.017-1.094, P = 0.004), HCY (OR = 1.029, 95% CI: 1.012-1.047, P = 0.001) and mobile MRI (OR = 4.539, 95% CI: 1.726-11.939, P = 0.002) were independently associated with stroke. The area under the curve (AUC) of the combined model including national screening criteria, mobile MRI results, and stroke risk factors was 0.786 (95% CI: 0.721-0.851), with a sensitivity of 69.6% and specificity of 80.4%.

CONCLUSION

Mobile MRI can be used as a simple and easy means to screen stroke.

Neuroprotective Effects of Electrical Stimulation Following Ischemic Stroke in Non-Human Primates

Brain stimulation has emerged as a novel therapy for ischemic stroke, a major cause of brain injury that often results in lifelong disability. Although past works in rodents have demonstrated protective effects of stimulation following stroke, few of these results have been replicated in humans due to the anatomical differences between rodent and human brains and a limited understanding of stimulation-induced network changes. Therefore, we combined electrophysiology and histology to study the neuroprotective mechanisms of electrical stimulation following cortical ischemic stroke in non-human primates. To produce controlled focal lesions, we used the photothrombotic method to induce targeted vasculature damage in the sensorimotor cortices of two macaques while collecting electrocorticography (ECoG) signals bilaterally. In another two monkeys, we followed the same lesioning procedures and applied repeated electrical stimulation via an ECoG electrode adjacent to the lesion. We studied the protective effects of stimulation on neural dynamics using ECoG signal power and coherence. In addition, we performed histological analysis to evaluate the differences in lesion volume. In comparison to controls, the ECoG signals showed decreased gamma power across the sensorimotor cortices in stimulated animals. Meanwhile, Nissl staining revealed smaller lesion volumes for the stimulated group, suggesting that electrical stimulation may exert neuroprotection by suppressing post-ischemic neural activity. With the similarity between NHP and human brains, this study paves the path for developing effective stimulation-based therapy for acute stroke in clinical studies.

Portable stroke detection devices: a systematic scoping review of prehospital applications

BACKGROUND

The worldwide burden of stroke remains high, with increasing time-to-treatment correlated with worse outcomes. Yet stroke subtype determination, most importantly between stroke/non-stroke and ischemic/hemorrhagic stroke, is not confirmed until hospital CT diagnosis, resulting in suboptimal prehospital triage and delayed treatment. In this study, we survey portable, non-invasive diagnostic technologies that could streamline triage by making this initial determination of stroke type, thereby reducing time-to-treatment.

METHODS

Following PRISMA guidelines, we performed a scoping review of portable stroke diagnostic devices. The search was executed in PubMed and Scopus, and all studies testing technology for the detection of stroke or intracranial hemorrhage were eligible for inclusion. Extracted data included type of technology, location, feasibility, time to results, and diagnostic accuracy.

RESULTS

After a screening of 296 studies, 16 papers were selected for inclusion. Studied devices utilized various types of diagnostic technology, including near-infrared spectroscopy (6), ultrasound (4), electroencephalography (4), microwave technology (1), and volumetric impedance spectroscopy (1). Three devices were tested prior to hospital arrival, 6 were tested in the emergency department, and 7 were tested in unspecified hospital settings. Median measurement time was 3 minutes (IQR: 3 minutes to 5.6 minutes). Several technologies showed high diagnostic accuracy in severe stroke and intracranial hematoma detection.

CONCLUSION

Numerous emerging portable technologies have been reported to detect and stratify stroke to potentially improve prehospital triage. However, the majority of these current technologies are still in development and utilize a variety of accuracy metrics, making inter-technology comparisons difficult. Standardizing evaluation of diagnostic accuracy may be helpful in further optimizing portable stroke detection technology for clinical use.

Reversible focal intracranial hypertension swine model with continuous multimodal neuromonitoring

BACKGROUND

Intracranial hypertension (HI) is associated with worse neurological outcomes and higher mortality. Although there are several experimental models of HI, in this article we present a reproducible, reversible, and reliable model of intracranial hypertension, with continuous multimodal monitoring.

NEW METHOD

A reversible intracranial hypertension model in swine with multimodal monitoring including intracranial pressure, arterial blood pressure, heart rate variation, brain tissue oxygenation, and electroencephalogram is developed to understand the relationship of ICP and EEG. By inflating and deflating a balloon, located 20 mm anterior to the coronal suture and a 15 mm sagittal suture, we generate intracranial hypertension events and simultaneously measure intracranial pressure and oxygenation in the contralateral hemisphere and the EEG, simulating the usual configuration in humans.

RESULTS

We completed 5 experiments and in all of them, we were able to complete at least 6 events of intracranial hypertension in a stable and safe way. For events of 20-40 mmHg of ICP we need an median (IQR) of 4.2 (3.64) ml of saline solution into the Foley balloon, a median (IQR) infusion time of 226 (185) second in each event and for events of 40-50 mmHg of ICP we need a median (IQR) of 5.1 (4.66) ml of saline solution, a median (IQR) infusion time of 280 (48) seconds and a median (IQR). The median (IQR) maintenance time was 352 (77) seconds and 392 (166) seconds for 20-40 mmHg and 40-50 mmHg of ICP, respectively.

COMPARISON WITH EXISTING METHOD(S)

Existing methods do not include EEG measures and do not present the reversibility of intracranial hypertension.

CONCLUSIONS

Our model is fully reproducible, it is capable of generating reversible focal intracranial hypertension through strict control of the injected volume, it is possible to generate different infusion rates of the volume in the balloon, in order to generate different scenarios, the data obtained are sufficient to determine the brain complacency in real time. and useful for understanding the pathophysiology of ICP and the relationship between ICP (CPP) and EEG.

EEG spectral exponent as a synthetic index for the longitudinal assessment of stroke recovery

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The Spectral Exponent (SE) indexes power-law features of the resting EEG in stroke patients.

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SE is consistently steeper in the affected hemisphere of patients after middle cerebral artery stroke.

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SE is linked to clinical status and seems to be a good predictor of clinical outcome.

Objective

Quantitative Electroencephalography (qEEG) can capture changes in brain activity following stroke. qEEG metrics traditionally focus on oscillatory activity, however recent findings highlight the importance of aperiodic (power-law) structure in characterizing pathological brain states. We assessed neurophysiological alterations and recovery after mono-hemispheric stroke by means of the Spectral Exponent (SE), a metric that reflects EEG slowing and quantifies the power-law decay of the EEG Power Spectral Density (PSD).

Methods

Eighteen patients (n = 18) with mild to moderate mono-hemispheric Middle Cerebral Artery (MCA) ischaemic stroke were retrospectively enrolled for this study. Patients underwent EEG recording in the sub-acute phase (T0) and after 2 months of physical rehabilitation (T1). Sixteen healthy controls (HC; n = 16) matched by age and sex were enrolled as a normative group. SE values and narrow-band PSD were estimated for each recording. We compared SE and band-power between patients and HC, and between the affected (AH) and unaffected hemisphere (UH) at T0 and T1 in patients.

Results

At T0, stroke patients showed significantly more negative SE values than HC (p = 0.003), reflecting broad-band EEG slowing. Most important, in patients SE over the AH was consistently more negative compared to the UH and showed a renormalization at T1. This SE renormalization significantly correlated with National Institute of Health Stroke Scale (NIHSS) improvement (R = 0.63, p = 0.005).

Conclusions

SE is a reliable readout of the neurophysiological and clinical alterations occurring after an ischaemic cortical lesion.

Significance

SE promise to be a robust method to monitor and predict patients’ functional outcome.

Surface electroencephalography (EEG) during the acute phase of stroke to assist with diagnosis and prediction of prognosis: a scoping review

BACKGROUND

Stroke is a common medical emergency responsible for significant mortality and disability. Early identification improves outcomes by promoting access to time-critical treatments such as thrombectomy for large vessel occlusion (LVO), whilst accurate prognosis could inform many acute management decisions. Surface electroencephalography (EEG) shows promise for stroke identification and outcome prediction, but evaluations have varied in technology, setting, population and purpose. This scoping review aimed to summarise published literature addressing the following questions: 1. Can EEG during acute clinical assessment identify: a) Stroke versus non-stroke mimic conditions. b) Ischaemic versus haemorrhagic stroke. c) Ischaemic stroke due to LVO. 2. Can these states be identified if EEG is applied < 6 h since onset. 3. Does EEG during acute assessment predict clinical recovery following confirmed stroke.

METHODS

We performed a systematic search of five bibliographic databases ending 19/10/2020. Two reviewers assessed eligibility of articles describing diagnostic and/or prognostic EEG application < 72 h since suspected or confirmed stroke.

RESULTS

From 5892 abstracts, 210 full text articles were screened and 39 retained. Studies were small and heterogeneous. Amongst 21 reports of diagnostic data, consistent associations were reported between stroke, greater delta power, reduced alpha/beta power, corresponding ratios and greater brain asymmetry. When reported, the area under the curve (AUC) was at least good (0.81-1.00). Only one study combined clinical and EEG data (AUC 0.88). There was little data found describing whether EEG could identify ischaemic versus haemorrhagic stroke. Radiological changes suggestive of LVO were also associated with increased slow and decreased fast waves. The only study with angiographic proof of LVO reported AUC 0.86 for detection < 24 h since onset. Amongst 26 reports of prognostic data, increased slow and reduced fast wave EEG changes were associated with future dependency, neurological impairment, mortality and poor cognition, but there was little evidence that EEG enhanced outcome prediction relative to clinical and/or radiological variables. Only one study focussed solely on patients < 6 h since onset for predicting neurological prognosis post-thrombolysis, with more favourable outcomes associated with greater hemispheric symmetry and a greater ratio of fast to slow waves.

CONCLUSIONS

Although studies report important associations with EEG biomarkers, further technological development and adequately powered real-world studies are required before recommendations can be made regarding application during acute stroke assessment.

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.

Cerebral hemorrhagic infarction was diagnosed subsequently after high-amplitude slow waves detected on processed electroencephalogram during sedation: a case report

Background

Continuous electroencephalogram (EEG) monitoring is useful for assessing the level of sedation and detecting non-convulsive epileptic seizures and cerebral ischemia in the intensive care unit. This report describes a case of cerebral hemorrhagic infarction diagnosed after the detection of high-amplitude slow waves on processed EEG during sedation.

Case presentation

A 68-year-old man who underwent cardiac surgery was sedated in the intensive care unit following an invasive procedure. High-amplitude slow waves appeared on processed EEG monitoring before the detection of anisocoria. Computed tomography revealed a cerebral hemorrhagic infarction.

Conclusions

In the management of critically ill patients, continuous EEG monitoring with forehead electrodes may be useful in the early detection of brain lesions.

Detection of large vessel occlusion stroke with electroencephalography in the emergency room: first results of the ELECTRA-STROKE study

Background

Prehospital detection of large vessel occlusion stroke of the anterior circulation (LVO-a) would enable direct transportation of these patients to an endovascular thrombectomy (EVT) capable hospital. The ongoing ELECTRA-STROKE study investigates the diagnostic accuracy of dry electrode electroencephalography (EEG) for LVO-a stroke in the prehospital setting. To determine which EEG features are most useful for this purpose and assess EEG data quality, EEG recordings are also performed in the emergency room (ER). Here, we report data of the first 100 patients included in the ER.

Methods

Patients presented to the ER with a suspected stroke or known LVO-a stroke underwent a single EEG prior to EVT. Diagnostic accuracy for LVO-a stroke of frequency band power, brain symmetry and phase synchronization measures were evaluated by calculating receiver operating characteristic curves. Optimal cut-offs were determined as the highest sensitivity at a specificity of ≥ 80%.

Results

EEG data were of sufficient quality for analysis in 65/100 included patients. Of these, 35/65 (54%) had an acute ischemic stroke, of whom 9/65 (14%) had an LVO-a stroke. Median onset-to-EEG-time was 266 min (IQR 121–655) and median EEG-recording-time was 3 min (IQR 3–5). The EEG feature with the highest diagnostic accuracy for LVO-a stroke was theta–alpha ratio (AUC 0.83; sensitivity 75%; specificity 81%). Combined, weighted phase lag index and relative theta power best identified LVO-a stroke (sensitivity 100%; specificity 84%).

Conclusion

Dry electrode EEG is a promising tool for LVO-a stroke detection, but data quality needs to be improved and validation in the prehospital setting is necessary. (TRN: NCT03699397, registered October 9 2018).

Supplementary Information

The online version contains supplementary material available at 10.1007/s00415-021-10781-6.

A systematic review of next-generation point-of-care stroke diagnostic technologies

OBJECTIVE

Stroke is a leading cause of morbidity and mortality. Current diagnostic modalities include CT and MRI. Over the last decade, novel technologies to facilitate stroke diagnosis, with the hope of shortening time to treatment and reducing rates of morbidity and mortality, have been developed. The authors conducted a systematic review to identify studies reporting on next-generation point-of-care stroke diagnostic technologies described within the last decade.

METHODS

A systematic review was performed according to PRISMA guidelines to identify studies reporting noninvasive stroke diagnostics. The QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) tool was utilized to assess risk of bias. PubMed, Web of Science, and Scopus databases were utilized. Primary outcomes assessed included accuracy and timing compared with standard imaging, potential risks or complications, potential limitations, cost of the technology, size/portability, and range/size of detection.

RESULTS

Of the 2646 reviewed articles, 19 studies met the inclusion criteria and included the following modalities of noninvasive stoke detection: microwave technology (6 studies, 31.6%), electroencephalography (EEG; 4 studies, 21.1%), ultrasonography (3 studies, 15.8%), near-infrared spectroscopy (NIRS; 2 studies, 10.5%), portable MRI devices (2 studies, 10.5%), volumetric impedance phase-shift spectroscopy (VIPS; 1 study, 5.3%), and eddy current damping (1 study, 5.3%). Notable medical devices that accurately predicted stroke in this review were EEG-based diagnosis, with a maximum sensitivity of 91.7% for predicting a stroke, microwave-based diagnosis, with an area under the receiver operating characteristic curve (AUC) of 0.88 for differentiating ischemic stroke and intracerebral hemorrhage (ICH), ultrasound with an AUC of 0.92, VIPS with an AUC of 0.93, and portable MRI with a diagnostic accuracy similar to that of traditional MRI. NIRS offers significant potential for more superficially located hemorrhage but is limited in detecting deep-seated ICH (2.5-cm scanning depth).

CONCLUSIONS

As technology and computational resources have advanced, several novel point-of-care medical devices show promise in facilitating rapid stroke diagnosis, with the potential for improving time to treatment and informing prehospital stroke triage.

Detection of Large Vessel Occlusion Stroke in the Prehospital Setting: Electroencephalography as a Potential Triage Instrument

A reliable and fast instrument for prehospital detection of large vessel occlusion (LVO) stroke would be a game-changer in stroke care, because it would enable direct transportation of LVO stroke patients to the nearest comprehensive stroke center for endovascular treatment. This strategy would substantially improve treatment times and thus clinical outcomes of patients. Here, we outline our view on the requirements of an effective prehospital LVO detection method, namely: high diagnostic accuracy; fast application and interpretation; user-friendliness; compactness; and low costs. We argue that existing methods for prehospital LVO detection, including clinical scales, mobile stroke units and transcranial Doppler, do not fulfill all criteria, hindering broad implementation of these methods. Instead, electroencephalography may be suitable for prehospital LVO detection since in-hospital studies have shown that quantification of hypoxia-induced changes in the electroencephalography signal have good diagnostic accuracy for LVO stroke. Although performing electroencephalography measurements in the prehospital setting comes with challenges, solutions for fast and simple application of this method are available. Currently, the feasibility and diagnostic accuracy of electroencephalography in the prehospital setting are being investigated in clinical trials.

Early EEG Alterations Correlate with CTP Hypoperfused Volumes and Neurological Deficit: A Wireless EEG Study in Hyper-Acute Ischemic Stroke

Brain electrical activity in acute ischemic stroke is related to the hypoperfusion of cerebral tissue as manifestation of neurovascular coupling. EEG could be applicable for bedside functional monitoring in emergency settings. We aimed to investigate the relation between hyper-acute ischemic stroke EEG changes, measured with bedside wireless-EEG, and hypoperfused core-penumbra CT-perfusion (CTP) volumes. In addition, we investigated the association of EEG and CTP parameters with neurological deficit measured by NIHSS. We analyzed and processed EEG, CTP and clinical data of 31 anterior acute ischemic stroke patients registered within 4.5 h from symptom onset. Delta/alpha ratio (DAR), (delta + theta)/(alpha + beta) ratio (DTABR) and relative delta power correlated directly (ρ = 0.72; 0.63; 0.65, respectively), while alpha correlated inversely (ρ = − 0.66) with total hypoperfused volume. DAR, DTBAR and relative delta and alpha parameters also correlated with ischemic core volume (ρ = 0.55; 0.50; 0.59; − 0.51, respectively). The same EEG parameters and CTP volumes showed significant relation with NIHSS at admission. The multivariate stepwise regression showed that DAR was the strongest predictor of NIHSS at admission (p < 0.001). The results of this study showed that hyper-acute alterations of EEG parameters are highly related to the extent of hypoperfused tissue highlighting the value of quantitative EEG as a possible complementary tool in the evaluation of stroke severity and its potential role in acute ischemic stroke monitoring.

Antiepileptic Drug Management in Acute Ischemic Stroke: Are Vascular Neurologists Utilizing Electroencephalograms? An Observational Cohort Study

Introduction

This study examines the utility of electroencephalography (EEG) in clinical decision making in acute ischemic stroke (AIS) patients in regards to the prescription of antiseizure medications.

Methods

Patients were grouped as having positive EEG (+) for epileptiform activity or negative EEG (-). These studies were no more than 30 minutes in length. Patients' charts were retrospectively reviewed for antiepileptic drug (AED) use before, during, and on discharge from AIS hospitalization.

Results

Of the 509 patients meeting inclusion criteria, 24 (4.7%) had a positive EEG. Patients did not significantly differ with respect to any demographic or baseline characteristics with the exception of prior history of seizure. In the EEG- group, AEDs were discontinued in only 3.5% of patients. In the EEG+ group, only 37.5% of patients had an initiation or change to their AED regimen within 36 hours of the study. 62.5% of the EEG+ group had a cortical stroke. Significance. Our results indicate that vascular neurologists are not using spot EEGs to routinely guide inpatient AED management. EEGs may have greater utility in those with a prior history of seizures and cortical strokes. Longer or continuous EEG monitoring may have better utility in the AIS population if there is clinical suspicion of seizure.

Rapid development of strong, persistent, spatiotemporally extensive cortical synchrony and underlying oscillations following acute MCA focal ischemia

Stroke is a leading cause of death and the leading cause of long-term disability, but its electrophysiological basis is poorly understood. Characterizing acute ischemic neuronal activity dynamics is important for understanding the temporal and spatial development of ischemic pathophysiology and determining neuronal activity signatures of ischemia. Using a 32-microelectrode array spanning the depth of cortex, electrophysiological recordings generated for the first time a continuous spatiotemporal profile of local field potentials (LFP) and multi-unit activity (MUA) before (baseline) and directly after (0-5 h) distal, permanent MCA occlusion (pMCAo) in a rat model. Although evoked activity persisted for hours after pMCAo with minor differences from baseline, spatiotemporal analyses of spontaneous activity revealed that LFP became spatially and temporally synchronized regardless of cortical depth within minutes after pMCAo and extended over large parts of cortex. Such enhanced post-ischemic synchrony was found to be driven by increased bursts of low multi-frequency oscillations and continued throughout the acute ischemic period whereas synchrony measures minimally changed over the same recording period in surgical sham controls. EEG recordings of a similar frequency range have been applied to successfully predict stroke damage and recovery, suggesting clear clinical relevance for our rat model.

Relative Alpha Variability Changes Precede Alpha-Delta Ratio Changes in Cerebral Ischemia

The utility of quantitative EEG in early detection of cerebral ischemia is still underappreciated in clinical practice. We present a case of aneurysmal subarachnoid hemorrhage complicated by vasospasm as detected by the cerebral angiogram. The patient was being monitored on electroencephalogram. It showed early signs of cerebral ischemia represented by decline in the Alpha-Delta-Ratio (ADR) and the Relative-Alpha-Variability (RAV). Surprisingly, the RAV changes preceded the ADR changes. This is a significant finding that can also apply to early reocclusion or reperfusion injuries after mechanical thrombectomy.

The Relationship between Carotid Doppler Ultrasound and EEG Metrics in Healthy Preschoolers and Adults

Despite widespread using electroencephalography (EEG) and Doppler ultrasound in pediatric neurology clinical practice, there are still no well-known correlations between these methods that could contribute to a better understanding of brain processes and development of neurological pathology. This study aims to reveal relationship between EEG and Doppler ultrasound methods. We compared two cohorts of adults and preschool children with no history of neurological or mental diseases. The data analysis included investigation of EEG and carotid blood flow indexes, which are significant in neurological diagnosis, as well as calculation of linear and non-linear EEG parameters and ratios between the systolic peak velocities of carotid arteries and carotid blood asymmetry. We have found age-dependent correlations between EEG and power Doppler ultrasound imaging (PDUI) data. Carotid blood flow asymmetry correlated with delta-rhythm power spectral density only in preschoolers. The ratios of blood flow velocities in the internal carotid arteries to those in the common carotid arteries correlated with higher peak alpha frequency and lower fractal dimension; moreover, they were associated with lower Epworth sleepiness scale scores. The study revealed significant correlations between EEG and PDUI imaging indexes, which are different for healthy children and adults. Despite the fact that the correlations were associated with non-clinical states such as overwork or stress, we assumed that the investigated parameters could be applicable for clinical trials.