Quantitative Electroencephalography After Pediatric Anterior Circulation Stroke

Appavu, Brian; Temkit, Mʼhamed; Foldes, Stephen T.; Burrows, Brian T.; Jacobson, Austin; Mangum, Tara; Boerwinkle, Varina L.; Marku, Iris; Abruzzo, Todd; Adelson, P. David

doi:10.1097/wnp.0000000000000813

Cited by 13 publications

(9 citation statements)

References 13 publications

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“…We have previously identified that alpha and beta power are reduced in regions of anterior circulation in children with AIS not specifically undergoing ECMO support [ 36 ]. We did not observe asymmetry of alpha or beta power to be associated with AIS in this study.…”

Section: Discussionmentioning

confidence: 99%

Neurophysiologic Features Reflecting Brain Injury During Pediatric ECMO Support

Hanalioglu,

Temkit,

Hildebrandt

et al. 2023

Neurocrit Care

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Background Extracorporeal membrane oxygenation (ECMO) provides lifesaving support to critically ill patients who experience refractory cardiopulmonary failure but carries a high risk for acute brain injury. We aimed to identify characteristics reflecting acute brain injury in children requiring ECMO support. Methods This is a prospective observational study from 2019 to 2022 of pediatric ECMO patients undergoing neuromonitoring, including continuous electroencephalography, cerebral oximetry, and transcranial Doppler ultrasound (TCD). The primary outcome was acute brain injury. Clinical and neuromonitoring characteristics were collected. Multivariate logistic regression was implemented to model odds ratios (ORs) and identify the combined characteristics that best discriminate risk of acute brain injury using the area under the receiver operating characteristic curve. Results Seventy-five pediatric patients requiring ECMO support were enrolled in this study, and 62 underwent neuroimaging or autopsy evaluations. Of these 62 patients, 19 experienced acute brain injury (30.6%), including seven (36.8%) with arterial ischemic stroke, four (21.1%) with hemorrhagic stroke, seven with hypoxic-ischemic brain injury (36.8%), and one (5.3%) with both arterial ischemic stroke and hypoxic-ischemic brain injury. A univariate analysis demonstrated acute brain injury to be associated with maximum hourly seizure burden (p = 0.021), electroencephalographic suppression percentage (p = 0.022), increased interhemispheric differences in electroencephalographic total power (p = 0.023) and amplitude (p = 0.017), and increased differences in TCD Thrombolysis in Brain Ischemia (TIBI) scores between bilateral middle cerebral arteries (p = 0.023). Best subset model selection identified increased seizure burden (OR = 2.07, partial R2 = 0.48, p = 0.013), increased quantitative electroencephalographic interhemispheric amplitude differences (OR = 2.41, partial R2 = 0.48, p = 0.013), and increased interhemispheric TCD TIBI score differences (OR = 4.66, partial R2 = 0.49, p = 0.006) to be independently associated with acute brain injury (area under the receiver operating characteristic curve = 0.92). Conclusions Increased seizure burden and increased interhemispheric differences in both quantitative electroencephalographic amplitude and TCD MCA TIBI scores are independently associated with acute brain injury in children undergoing ECMO support.

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Section: Discussionmentioning

confidence: 99%

Neurophysiologic Features Reflecting Brain Injury During Pediatric ECMO Support

Hanalioglu,

Temkit,

Hildebrandt

et al. 2023

Neurocrit Care

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“…New EEG abnormalities, such as focal slowing or new interictal or ictal activity, can be the first indicator of injury in critically ill children who may otherwise have a limited neurologic examination. The roles of both cEEG and qEEG specifically for detection of ischemia and hemorrhage are reviewed elsewhere in this issue in the article titled “Neuromonitoring in Children With Neurovascular Disorders.” A single-center study of children with acute unilateral anterior circulation ischemic stroke ( n = 5) and hemorrhagic stroke ( n = 6) compared qEEG features between the injured and uninjured hemispheres [ 182 ]. Patients with ischemic stroke demonstrated decreased alpha and beta power and lower spectral edge frequency in the injured hemisphere, whereas patients with hemorrhage consistently had a negative correlation between total power and mean arterial blood pressure.…”

Section: Quantitative Eegmentioning

confidence: 99%

Review of Noninvasive Neuromonitoring Modalities in Children II: EEG, qEEG

2023

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Critically ill children with acute neurologic dysfunction are at risk for a variety of complications that can be detected by noninvasive bedside neuromonitoring. Continuous electroencephalography (cEEG) is the most widely available and utilized form of neuromonitoring in the pediatric intensive care unit. In this article, we review the role of cEEG and the emerging role of quantitative EEG (qEEG) in this patient population. cEEG has long been established as the gold standard for detecting seizures in critically ill children and assessing treatment response, and its role in background assessment and neuroprognostication after brain injury is also discussed. We explore the emerging utility of both cEEG and qEEG as biomarkers of degree of cerebral dysfunction after specific injuries and their ability to detect both neurologic deterioration and improvement. Supplementary Information The online version contains supplementary material available at 10.1007/s12028-023-01686-5.

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“…DAR), α and δ power ratio (alpha-delta power ratio, ADR), δ+ θ and α+β power ratio [(delta+ theta)/(alpha+ beta), DTABR], as well as relative α wave power (relative alpha power, RAP), relative δ wave power (relative delta power, RDP), and brain symmetry index (brain symmetry index, BSI). Studies have found [21] that δ wave index, slow wave index, θ wave index, BSI, ADR, and DTABR, the six main indicators, are correlated with NIHSS scores. Therefore, this article collects these 6 QEEG features of patients as indicators to determine ischemic stroke, as shown in Table 2.…”

Section: D-d D-dimermentioning

confidence: 99%

Deep Learning-Based Multi-Feature Auxiliary Diagnosis Method for Early Detection of Ischemic Stroke

Zhou¹,

Zhang²,

Li³

2023

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Stroke, an acute cerebrovascular disease, has become the second leading cause of death worldwide after coronary heart disease, characterized by high incidence, disability, and mortality rates, with an increasingly younger affected population. Clinically, stroke types are primarily divided into ischemic and hemorrhagic strokes, with ischemic stroke being the most common. Presently, early identification methods rely heavily on physicians' experience, leading to misdiagnosis, missed diagnosis, diagnostic delay, and other issues, potentially resulting in worsened conditions or severe complications. Although artificial intelligence-based stroke auxiliary diagnosis systems have been employed in recent years to reduce missed diagnoses and enhance work efficiency, their impact on improving diagnostic accuracy has been limited. The main reason for this limitation is the selection of relatively singular or atypical feature types in neural networks, which affects diagnostic accuracy. To address this issue, this study leverages the rapid and sensitive response of electroencephalogram (EEG) data to cerebral ischemia and combines it with clinical indicators to propose a comprehensive "clinical indicators + quantitative electroencephalogram" multi-feature pattern recognition method. Initially, 23 key features for neural network training are selected. Subsequently, an ischemic stroke diagnosis model combining LSTM attention and multi-feature is constructed. In an experiment involving 500 ischemic stroke patients, the diagnostic model demonstrates an accuracy of 0.81, a sensitivity of 0.82, and an F1-score of 0.81. Moreover, to accurately locate the lesion area, the three-dimensional features of MRI images are used. A cascaded 3D deep residual network stroke precise segmentation method is constructed by incorporating residual units and cascade concepts into the 3DCNN network. The evaluation indicators of this segmentation algorithm on the training set are: DICE coefficient 0.91, precision 0.94, and sensitivity 0.89. Experimental results indicate that the proposed method outperforms existing clinical diagnosis schemes and CNN segmentation models in terms of diagnostic performance. The implementation of rapid and accurate diagnosis during early stages of stroke onset is crucial for improving ischemic prognosis, minimizing brain damage, and reducing mortality and disability rates.

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Quantitative Electroencephalography After Pediatric Anterior Circulation Stroke

Abstract: Supplemental Digital Content is Available in the Text.

Cited by 13 publications

References 13 publications

Neurophysiologic Features Reflecting Brain Injury During Pediatric ECMO Support

Neurophysiologic Features Reflecting Brain Injury During Pediatric ECMO Support

Review of Noninvasive Neuromonitoring Modalities in Children II: EEG, qEEG

Deep Learning-Based Multi-Feature Auxiliary Diagnosis Method for Early Detection of Ischemic Stroke

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