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Related Experiment Video

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Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
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Identification of acute stroke using quantified brain electrical activity.

Edward A Michelson1, Daniel Hanley, Robert Chabot

  • 1Department Emergency Medicine, University Hospitals Case Medical Center, Cleveland, OH.

Academic Emergency Medicine : Official Journal of the Society for Academic Emergency Medicine
|January 8, 2015
PubMed
Summary
This summary is machine-generated.

This study explored using brain electrical activity to detect acute stroke, showing promising results for a new diagnostic tool. The system achieved high sensitivity in identifying strokes, potentially aiding emergency department triage.

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Area of Science:

  • Neuroscience
  • Medical Diagnostics
  • Biomedical Engineering

Background:

  • Acute stroke diagnosis relies heavily on noncontrast head computed tomography (CT) in emergency departments.
  • Clinical presentation of stroke can be mimicked by other conditions, complicating rapid triage.
  • Existing diagnostic technologies for acute stroke lack ease of acquisition and rapid evaluation capabilities.

Purpose of the Study:

  • To investigate the clinical utility of a novel approach using brain electrical activity for acute stroke assessment.
  • To evaluate an independently derived classifier algorithm for identifying structural brain injury based on electroencephalography (EEG) data.
  • To explore the potential of a reduced frontal montage EEG system in aiding acute stroke diagnosis.

Main Methods:

  • Adult patients presenting with stroke-like symptoms or altered mental status were recruited from urban academic emergency departments.
  • Electroencephalographic data were acquired for 10 minutes using a hand-held device with five frontal electrodes.
  • A Structural Brain Injury Index (SBII) classifier, previously validated for traumatic brain injury, was applied to classify patients as CT+ or not CT+.

Main Results:

  • The study included 48 stroke patients (31 ischemic, 17 hemorrhagic) and 135 stroke mimics.
  • The SBII classifier demonstrated a sensitivity of 91.7% and a specificity of 50.4% for detecting stroke.
  • Negative predictive value (NPV) was 94.4%, and positive predictive value (PPV) was 39.6%.
  • Eighty percent of ischemic strokes initially negative on CT but confirmed by MRI were correctly identified.

Conclusions:

  • Despite a small sample size and a classifier not specifically trained on stroke data, the findings suggest potential utility.
  • A rapidly acquired, user-friendly system assessing brain electrical activity could serve as a valuable adjunct in acute stroke evaluation.
  • Further research is warranted to refine the technology and validate its performance in larger, stroke-specific populations.