Stroke identification using a portable EEG device – A pilot study

Gottlibe, Matan; Rosen, Oren; Weller, Boaz; Mahagney, Ayed; Omar, Nicola; Khuri, Ala; Srugo, Isaac; Genizi, Jacob

doi:10.1016/j.neucli.2019.12.004

Cited by 36 publications

(37 citation statements)

References 11 publications

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“…Regardless, these measures show promise in being indicative of stroke severity. A recent pilot study found, using the Muse, that stroke patients had increased brain asymmetry compared to controls using the revised brain symmetry index 27 , 33 . This is contrary to our findings, which show overall decreased asymmetry in the stroke patients.…”

Section: Discussionmentioning

confidence: 99%

“…Despite these limitations, wearable EEG has been successful in diagnostic feasibility for other disorders, such as epilepsy 26 . Recently a pilot study was published that suggested stroke patients could be distinguished from healthy controls using the Muse headband due to significantly increased brain asymmetry in stroke patients 27 . However, this study used healthy volunteers as controls rather than stroke mimics, and it is possible that stroke mimics also present with EEG abnormalities.…”

Section: Introductionmentioning

confidence: 99%

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Predicting stroke severity with a 3-min recording from the Muse portable EEG system for rapid diagnosis of stroke

Wilkinson

Burrell

Kuziek

et al. 2020

Sci Rep

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In this study, we demonstrated the use of low-cost portable electroencephalography (EEG) as a method for prehospital stroke diagnosis. We used a portable EEG system to record data from 25 participants, 16 had acute ischemic stroke events, and compared the results to age-matched controls that included stroke mimics. Delta/alpha ratio (DAR), (delta + theta)/(alpha + beta) ratio (DBATR) and pairwise-derived Brain Symmetry Index (pdBSI) were investigated, as well as head movement using the on-board accelerometer and gyroscope. We then used machine learning to distinguish between different subgroups. DAR and DBATR increased in ischemic stroke patients with increasing stroke severity (p = 0.0021, partial η2 = 0.293; p = 0.01, partial η2 = 0.234). Also, pdBSI decreased in low frequencies and increased in high frequencies in patients who had a stroke (p = 0.036, partial η2 = 0.177). Using classification trees, we were able to distinguish moderate to severe stroke patients and from minor stroke and controls, with a 63% sensitivity, 86% specificity and accuracy of 76%. There are significant differences in DAR, DBATR, and pdBSI between patients with ischemic stroke when compared to controls, and these effects scale with severity. We have shown the utility of a low-cost portable EEG system to aid in patient triage and diagnosis as an early detection tool.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting stroke severity with a 3-min recording from the Muse portable EEG system for rapid diagnosis of stroke

Wilkinson

Burrell

Kuziek

et al. 2020

Sci Rep

View full text Add to dashboard Cite

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“…In many cases, stroke symptoms are not visible at the early level of ischemic events. So, the decision of referral to a clinical diagnostic center and detail neural and pathological assessment may be delayed [3]. Late identification of ischemic stroke may lead to cognitive impairment and the economic burden of stroke care with a mental impairment is three times greater than those without cognitive damage [4].…”

Section: Introductionmentioning

confidence: 99%

“…Changes in symmetric behavior of the spectral power of quantitative electroencephalography (EEG) between the two cerebral hemispheres can be described by the Revised Brain Symmetry Index (rsBSI) [6]. Studies showed that rsBSI is an important marker early prediction of stroke [3,7].…”

Section: Introductionmentioning

confidence: 99%

“…As light affects the circadian rhythms and an eye-mask maintains a balance of light and darkness by obstructing lights, an eye-mask is generally used as a sleep wearable device for resting and sleep quality improvement. In a pilot study, Muse EEG wearables were used for the identification of stroke [3]. The rsBSI was only investigated as the marker of prognostics of stroke, other features were not explored.…”

Section: Introductionmentioning

confidence: 99%

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HealthSOS: Real-Time Health Monitoring System for Stroke Prognostics

2020

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Electroencephalography (EEG) is immediate and sensitive to cortical impairment resulting from ischemic stroke and is considered as the potential predictive tool of stroke onset, and post-stroke clinical management. Brainwave monitoring outside the heavily equipped clinical environment demands a low-cost, portable, and wearable EEG system. This study aims to assess the feasibility of using an ambulatory EEG system to classify the stroke patient group with neurological changes due to ischemic stroke and the control healthy adult group. HealthSOS, a real-time health monitoring system for stroke prognostics, is proposed here, which consists of an eye-mask embedded portable EEG device, data analytics, and medical ontology based health advisor service. This system was investigated with 37 stroke patients (mean age 71.6 years, 61% male) admitted in the emergency unit of a hospital and 36 healthy elderly volunteers (mean age 76 years, 28% male). EEG was recorded in resting-state using the portable device with frontal cortical electrodes (Fp1, Fp2) embedded in an eye-mask within 120 h after the onset of symptoms of ischemic stroke (confirmed clinically). The EEG data acquisition of the left and right brain hemispheres was done for at least 15 minutes in the awake resting state while subjects laid down on the bed. The statistical result shows that the revised brain symmetry index (rsBSI), the delta-alpha ratio, and the delta-theta ratio of the stroke group differ significantly from those of the healthy control group. In the machine learning analysis, the support vector machine (SVM) model shows the highest accuracy (Overall accuracy: 92%) and the highest Gini coefficient (95%) in classification performance. This study will be useful for early stroke prognostics and the management of post-stroke treatment.

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Exploring postictal recovery with acetaminophen or nimodipine: A randomized‐controlled crossover trial

Pottkämper,

Verdijk,

Stuiver

et al. 2024

Ann Clin Transl Neurol

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ObjectiveThe postictal state is underrecognized in epilepsy. Animal models show improvement of postictal symptoms and cerebral perfusion with acetaminophen or nimodipine. We studied the effects of acetaminophen or nimodipine on postictal electroencephalographic (EEG) recovery, clinical reorientation, and hypoperfusion in patients with ECT‐induced seizures.MethodsIn this prospective clinical trial with three‐condition randomized crossover design, study interventions were administered orally 2 h before ECT sessions (1000 mg acetaminophen, 60 mg nimodipine, or a placebo condition). Primary outcome measure was the speed of postictal EEG recovery. Secondary outcomes were the extent of postictal EEG recovery, clinical reorientation time, and postictal cerebral blood flow as assessed by perfusion‐weighted MRI. Bayesian generalized mixed‐effects models were applied for analyses.ResultsWe included 300 seizures, postictal EEGs, and reorientation time values, and 76 MRI perfusion measures from 33 patients (median age 53 years, 19 female). Pretreatment with acetaminophen or nimodipine was not associated with change in speed of EEG recovery compared to placebo (1.13 [95%CI 0.92, 1.40] and 1.07 [95%CI 0.87, 1.31], respectively), nor with the secondary outcomes. No patient reached full EEG recovery at 1 h post‐seizure, despite clinical recovery in 89%. Longer seizures were associated with slower EEG recovery and lower postictal perfusion. Nimodipine altered regional perfusion in the posterior cortex.InterpretationPretreatment with acetaminophen or nimodipine did not alleviate symptoms and signs of the postictal state. Systematic study of the postictal state after ECT‐induced seizures is feasible.

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Stroke identification using a portable EEG device – A pilot study

Cited by 36 publications

References 11 publications

Predicting stroke severity with a 3-min recording from the Muse portable EEG system for rapid diagnosis of stroke

Predicting stroke severity with a 3-min recording from the Muse portable EEG system for rapid diagnosis of stroke

HealthSOS: Real-Time Health Monitoring System for Stroke Prognostics

Exploring postictal recovery with acetaminophen or nimodipine: A randomized‐controlled crossover trial

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