Yield of epileptiform abnormalities and predictors on routine electroencephalography in a neurology facility in Ghana
- 1Clinical Research Department, FOCOS Orthopaedic Hospital, Accra, Ghana
- 2Neurology Department, FOCOS Orthopaedic Hospital, Accra, Ghana
- 3Purplepoint Neurodiagnostics, Accra, Ghana
Abstract
Background: Electroencephalography (EEG) is an essential neurodiagnostic tool for the evaluation of epilepsy and other neurological disorders. However, evidence on the yield of epileptiform abnormalities of routine EEG and its predictors in sub-Saharan Africa remains limited.
Objective: To determine the yield of epileptiform abnormalities of routine EEG and identify factors associated with epileptiform discharges in a Ghanaian tertiary referral centre.
Methods: This study included 124 patients who underwent routine outpatient EEG between October 2023 and January 2026. Demographic, clinical, medication, and EEG characteristics were collected using a standardized data extraction form. The primary outcome was the presence of epileptiform discharges. Associations between patient characteristics and epileptiform activity were assessed using Chi-square and Fisher’s exact tests. Multivariable logistic regression was performed to identify independent predictors, with results reported as adjusted odds ratios (aORs) and 95% confidence intervals (CIs).
Results: The mean age of participants was 29.4 ± 19.7 years, and 55.6% were male. Epileptiform discharges were identified in 72 patients, yielding an overall epileptiform abnormality of 58.1%. Generalized epilepsy accounted for 83.3% of epileptiform abnormalities. The highest yield was observed among children aged 0–5 years (83.3%) and adults aged ≥ 60 years (75.0%). On bivariate analysis, generalized slowing (90.9%, p = 0.025) and focal slowing (100.0%, p < 0.001) were significantly associated with epileptiform discharges. In multivariable analysis, abnormal EEG background activity remained the only independent predictor of epileptiform discharges (aOR = 23.47, 95% CI: 2.95–186.86, p = 0.003).
Conclusion: Routine EEG demonstrated a high yield of epileptiform abnormalities in this Ghanaian tertiary centre. Abnormal EEG background activity was a strong independent predictor of epileptiform discharges, highlighting the importance of careful background assessment during EEG interpretation and supporting the continued use of routine EEG in epilepsy evaluation.
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BibTeX
@article{aklamanu2026yield,
title = {{Yield of epileptiform abnormalities and predictors on routine electroencephalography in a neurology facility in Ghana}},
author = {Bright Worlanyo Aklamanu and Clement Okyere Sefa and Ebenezer Kodji Tetteh and Joshua Tiyaliwa Laari},
journal = {BMC Neurology},
year = {2026},
month = aug,
doi = {10.1186/s12883-026-05251-3},
url = {https://doi.org/10.1186/s12883-026-05251-3},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Yield of epileptiform abnormalities and predictors on routine electroencephalography in a neurology facility in Ghana
AU - Bright Worlanyo Aklamanu
AU - Clement Okyere Sefa
AU - Ebenezer Kodji Tetteh
AU - Joshua Tiyaliwa Laari
PY - 2026
DA - 2026/08/07
JO - BMC Neurology
DO - 10.1186/s12883-026-05251-3
UR - https://doi.org/10.1186/s12883-026-05251-3
AB - Background: Electroencephalography (EEG) is an essential neurodiagnostic tool for the evaluation of epilepsy and other neurological disorders. However, evidence on the yield of epileptiform abnormalities of routine EEG and its predictors in sub-Saharan Africa remains limited. Objective: To determine the yield of epileptiform abnormalities of routine EEG and identify factors associated with epileptiform discharges in a Ghanaian tertiary referral centre. Methods: This study included 124 patients who underwent routine outpatient EEG between October 2023 and January 2026. Demographic, clinical, medication, and EEG characteristics were collected using a standardized data extraction form. The primary outcome was the presence of epileptiform discharges. Associations between patient characteristics and epileptiform activity were assessed using Chi-square and Fisher’s exact tests. Multivariable logistic regression was performed to identify independent predictors, with results reported as adjusted odds ratios (aORs) and 95% confidence intervals (CIs). Results: The mean age of participants was 29.4 ± 19.7 years, and 55.6% were male. Epileptiform discharges were identified in 72 patients, yielding an overall epileptiform abnormality of 58.1%. Generalized epilepsy accounted for 83.3% of epileptiform abnormalities. The highest yield was observed among children aged 0–5 years (83.3%) and adults aged ≥ 60 years (75.0%). On bivariate analysis, generalized slowing (90.9%, p = 0.025) and focal slowing (100.0%, p < 0.001) were significantly associated with epileptiform discharges. In multivariable analysis, abnormal EEG background activity remained the only independent predictor of epileptiform discharges (aOR = 23.47, 95% CI: 2.95–186.86, p = 0.003). Conclusion: Routine EEG demonstrated a high yield of epileptiform abnormalities in this Ghanaian tertiary centre. Abnormal EEG background activity was a strong independent predictor of epileptiform discharges, highlighting the importance of careful background assessment during EEG interpretation and supporting the continued use of routine EEG in epilepsy evaluation.
SN - 1471-2377
ER -