Frontiers in Neurology (Jun 2023)

Qualitative electroencephalogram and its predictors in the diagnosis of stroke

  • Mohd Syahrul Nizam Ag Lamat,
  • Mohd Syahrul Nizam Ag Lamat,
  • Muhammad Samir Haziq Abd Rahman,
  • Muhammad Samir Haziq Abd Rahman,
  • Wan Asyraf Wan Zaidi,
  • Wan Asyraf Wan Zaidi,
  • Wan Nur Nafisah Wan Yahya,
  • Wan Nur Nafisah Wan Yahya,
  • Ching Soong Khoo,
  • Ching Soong Khoo,
  • Rozita Hod,
  • Rozita Hod,
  • Hui Jan Tan,
  • Hui Jan Tan

DOI
https://doi.org/10.3389/fneur.2023.1118903
Journal volume & issue
Vol. 14

Abstract

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IntroductionStroke 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.MethodsA 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.ResultsThe 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).ConclusionThe 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.

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