Qualitative electroencephalogram and its predictors in the diagnosis of stroke

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

doi:10.3389/fneur.2023.1118903

Qualitative electroencephalogram and its predictors in the diagnosis of stroke

Front Neurol. 2023 Jun 12:14:1118903. doi: 10.3389/fneur.2023.1118903. eCollection 2023.

Authors

Mohd Syahrul Nizam Ag Lamat^{1

2}, Muhammad Samir Haziq Abd Rahman^{1

2}, Wan Asyraf Wan Zaidi^{1

2}, Wan Nur Nafisah Wan Yahya^{1

2}, Ching Soong Khoo^{1

2}, Rozita Hod^{2

3}, Hui Jan Tan^{1

2}

Affiliations

¹ Department of Medicine, Faculty of Medicine, National University of Malaysia, Kuala Lumpur, Malaysia.
² Hospital Canselor Tuanku Muhriz, Jalan Yaacob Latif, Bandar Tun Razak, Kuala Lumpur, Malaysia.
³ Department of Community Health, Faculty of Medicine, National University of Malaysia, Kuala Lumpur, Malaysia.

Abstract

Introduction: Stroke 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.

Methods: A 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.

Results: The 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).

Conclusion: The 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.

Keywords: diagnosis; electroencephalography; predictors; qualitative; stroke.