Correlation of Continuous Electroencephalograms with Cerebral Blood Flow Measurements during Carotid Endarterectomy

Sharbrough, Frank W.; Messick, Joseph M.; Sundt, Thoralf M.

doi:10.1007/978-3-642-65814-3_75

Cited by 39 publications

(50 citation statements)

References 3 publications

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“…EEG changes are closely tied to CBF (Figure 1) [3]. When normal CBF declines to approximately 25-35 ml/100 g/min, the EEG fi rst loses faster frequencies, then as the CBF decreases to approximately 17-18 ml/100 g/min, slower frequencies gradually increase.…”

Section: Eeg Changes In Ischemiamentioning

confidence: 97%

“…Th is represents a crucial ischemic threshold at which neurons begin to lose their transmembrane gradients, leading to cell death (infarction). In the setting of carotid clamping, CBF that decreases instantaneously to the ischemic threshold leads to rapid and reversible changes in the EEG (within 20 seconds) [3]. Infarction may not occur for hours at this degree of fl ow limitation [2] and some electrical activity (mostly delta frequencies) may be seen, but as the CBF continues to decrease toward the infarction threshold (10-12 ml/100 g/min and below), the EEG becomes silent and cellular damage becomes irreversible [2][3][4].…”

Section: Eeg Changes In Ischemiamentioning

confidence: 99%

“…In the setting of carotid clamping, CBF that decreases instantaneously to the ischemic threshold leads to rapid and reversible changes in the EEG (within 20 seconds) [3]. Infarction may not occur for hours at this degree of fl ow limitation [2] and some electrical activity (mostly delta frequencies) may be seen, but as the CBF continues to decrease toward the infarction threshold (10-12 ml/100 g/min and below), the EEG becomes silent and cellular damage becomes irreversible [2][3][4]. Some EEG patterns, such as regional attenuation of faster frequencies without delta (RAWOD), may refl ect early severe loss of CBF (mean 8.6 ml/100 g/ min) as seen during large occlusive infarcts, leading to complications such as edema and herniation [13].…”

Section: Eeg Changes In Ischemiamentioning

confidence: 99%

“…Additionally, EEG may assist in guiding treatment, to rapidly reverse the region at risk if it has not reached the infarction threshold. Examples include shunt placement in carotid endarterec tomy [3], tPA in acute ischemic stroke [18,19], and aug mentation of blood pressure or CBF after acute ischemic stroke [20].…”

Section: Eeg Changes In Ischemiamentioning

confidence: 99%

“…When cerebral blood fl ow (CBF) becomes compro mised, changes occur in both the metabolic and electrical activity of cortical neurons, with associated EEG changes [2]. In the operating room, EEG has an established role in identifying ischemia prior to the develop ment of infarction during carotid endarterectomy [3]. In acute ischemic stroke, the primary injury has typically occurred prior to presentation, but EEG may be able to detect patterns to suggest severity, prognosis, and secondary injury (e.g., reocclusion, edema, or hemorrhagic transformation) [4].…”

Section: Introductionmentioning

confidence: 99%

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Quantitative EEG for the Detection of Brain Ischemia

Foreman¹,

Claassen²

2012

Annual Update in Intensive Care and Emergency Medicine 2012

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Section: Eeg Changes In Ischemiamentioning

confidence: 97%

Section: Eeg Changes In Ischemiamentioning

confidence: 99%

Section: Eeg Changes In Ischemiamentioning

confidence: 99%

Section: Eeg Changes In Ischemiamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Quantitative EEG for the Detection of Brain Ischemia

Foreman¹,

Claassen²

2012

Annual Update in Intensive Care and Emergency Medicine 2012

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Quantitative EEG as a diagnostic and prognostic tool in hemispheric stroke patients undergoing type A aortic dissection surgery

et al. 2023

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Objective The diagnostic and prognostic value of quantitative electroencephalogram (qEEG) parameters, specifically the symmetry of amplitude‐integrated electroencephalography (aEEG) and relative band power (RBP), in the postoperative stroke of the cerebral hemisphere following type A aortic dissection, remains an area of inquiry. Methods We analyzed and processed 56 patients with type A aortic dissection who underwent bedside qEEG monitoring and analyzed the qEEG indices, brain CT, and clinical data of these patients. qEEG (symmetry of aEEG and RBP, and affected/unaffected hemisphere) indices were analyzed at discharge and 60 days after discharge. Results A total of 56 patients were studied. The 60‐day mortality rate was 12.5%. The affected hemisphere's diagnosis and mortality after 1‐year follow‐up were evaluated, and RBP beta demonstrated the highest area under the curve values with 95% confidence intervals (CI) of .849 (95% CI: .771–.928) and .91 (95% CI: .834–.986), respectively. According to the results of the logistic regression analysis, we have identified the strongest predictors for cerebral hemisphere stroke and 1‐year mortality in stroke patients. Specifically, aEEGmin exhibited the highest predictive power with an odds ratio (OR) of .735 for cerebral hemisphere stroke, whereas DTABR was confirmed as one of the strongest predictors with an OR of 1.619 for 1‐year mortality in stroke patients, indicating a high level of reliability. Spearman correlation coefficients showed that aEEGmax and aEEGmin were positively correlated with Alberta Stroke Program Early CT Score (aEEGmax: rho = .50, p < .001; aEEGmin: rho = .44, p < .001). Conclusions QEEG has been proven to be a sensitive indicator for monitoring brain function and can be monitored continuously. It can help clinicians detect and treat these patients early and improve long‐term prognosis.

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Mining of Sensor Data in Healthcare: A Survey

Sow¹,

Turaga²,

Schmidt

2012

Managing and Mining Sensor Data

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Historically, healthcare has been mainly provided in a reactive manner that limits its usefulness. With progress in sensor technologies, the instrumentation of the world has offered unique opportunities to better observe patients physiological signals in order to provide healthcare in a more proactive manner. To reach this goal, it is essential to be able to analyze patient data and turn it into actionable information using data mining. This chapter surveys existing applications of sensor data mining technologies in healthcare. It starts with a description of healthcare data mining challenges before presenting an overview of applications of data mining in both clinical and non clinical settings.

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Correlation of Continuous Electroencephalograms with Cerebral Blood Flow Measurements during Carotid Endarterectomy

Cited by 39 publications

References 3 publications

Quantitative EEG for the Detection of Brain Ischemia

Quantitative EEG for the Detection of Brain Ischemia

Quantitative EEG as a diagnostic and prognostic tool in hemispheric stroke patients undergoing type A aortic dissection surgery

Mining of Sensor Data in Healthcare: A Survey

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