TY - JOUR
T1 - Paroxysmal slow wave events as a diagnostic biomarker for epilepsy
T2 - Lessons from rural Zambia
AU - Malunga, Andrew
AU - Lash, Sina
AU - Abu-Ahmad, Alaa
AU - Alhadeed, Laith
AU - Benninger, Felix
AU - Ben-Arie, Gal
AU - Fearns, Nicholas
AU - Imtiaz, Hamza
AU - Kunst, Stefan
AU - Minarik, Anna
AU - Masamu, Mutale
AU - Mshanga, George
AU - Neal, Oliver
AU - Racz, Attila
AU - Ruber, Theodor
AU - Saadeh, Khalid
AU - Serlin, Yonatan
AU - Shamir, Merav
AU - Welte, Tamara
AU - Whatley, Benjamin
AU - Friedman, Alon
AU - Zimba, Stanley
N1 - Publisher Copyright:
© 2025 The Author(s). Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.
PY - 2025/12/1
Y1 - 2025/12/1
N2 - Objective: Epilepsy affects more than 50 million people globally, with low- and middle-income countries (LMICs) bearing the greatest burden due to limited medical resources and stigma. Electroencephalography (EEG) is a cost-effective diagnostic tool, but its interpretation often requires unavailable expertise in rural areas. There is a pressing need for reliable, quantitative EEG biomarkers to enhance diagnosis, guide imaging, and monitor treatment. Methods: We investigated paroxysmal slow wave events (PSWEs), transient markers of cortical network slowing, in scalp EEG recordings from epilepsy patients at the Kakumbi Rural Health Center in Zambia (n = 127) and from Bonn Epilepsy Center (n = 132). PSWE characteristics, including occurrence, duration, and spatial distribution, were analyzed. Source localization of PSWEs was performed using standardized low-resolution brain electromagnetic tomography software. Results: PSWEs were observed in all patients with epilepsy. Time in PSWE showed negative correlation with patient age (r = −.26, p =.003) and disease onset (r = −.25, p =.005), regardless of age. PSWE characteristics, including temporal and spatial distribution, were associated with disease severity and similar to drug-resistant patients from Bonn Epilepsy Center. EEGs reported as “abnormal” had greater time in PSWE compared with “normal” EEGs (p =.024). Focal PSWE source localization suggested the presence of an intracranial lesion on computed tomography (area under the curve =.7). Significance: This study supports previous research on the potential of PSWEs as a quantitative EEG biomarker in epilepsy. Automated analysis of PSWEs can enhance diagnostic accuracy and assist in screening patients for brain imaging, particularly in resource-constrained settings. This approach offers a practical solution to bridge the diagnostic gap in LMICs that can potentially be used to improve epilepsy management and patient outcomes.
AB - Objective: Epilepsy affects more than 50 million people globally, with low- and middle-income countries (LMICs) bearing the greatest burden due to limited medical resources and stigma. Electroencephalography (EEG) is a cost-effective diagnostic tool, but its interpretation often requires unavailable expertise in rural areas. There is a pressing need for reliable, quantitative EEG biomarkers to enhance diagnosis, guide imaging, and monitor treatment. Methods: We investigated paroxysmal slow wave events (PSWEs), transient markers of cortical network slowing, in scalp EEG recordings from epilepsy patients at the Kakumbi Rural Health Center in Zambia (n = 127) and from Bonn Epilepsy Center (n = 132). PSWE characteristics, including occurrence, duration, and spatial distribution, were analyzed. Source localization of PSWEs was performed using standardized low-resolution brain electromagnetic tomography software. Results: PSWEs were observed in all patients with epilepsy. Time in PSWE showed negative correlation with patient age (r = −.26, p =.003) and disease onset (r = −.25, p =.005), regardless of age. PSWE characteristics, including temporal and spatial distribution, were associated with disease severity and similar to drug-resistant patients from Bonn Epilepsy Center. EEGs reported as “abnormal” had greater time in PSWE compared with “normal” EEGs (p =.024). Focal PSWE source localization suggested the presence of an intracranial lesion on computed tomography (area under the curve =.7). Significance: This study supports previous research on the potential of PSWEs as a quantitative EEG biomarker in epilepsy. Automated analysis of PSWEs can enhance diagnostic accuracy and assist in screening patients for brain imaging, particularly in resource-constrained settings. This approach offers a practical solution to bridge the diagnostic gap in LMICs that can potentially be used to improve epilepsy management and patient outcomes.
KW - biomarker
KW - computerized tomography
KW - electroencephalogram
KW - epilepsy
KW - paroxysmal slow wave events
KW - rural Zambia
KW - sLORETA
UR - https://www.scopus.com/pages/publications/105019329144
U2 - 10.1111/epi.18598
DO - 10.1111/epi.18598
M3 - Article
C2 - 41105006
AN - SCOPUS:105019329144
SN - 0013-9580
VL - 66
SP - 4869
EP - 4880
JO - Epilepsia
JF - Epilepsia
IS - 12
ER -