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Paroxysmal slow wave events as a diagnostic biomarker for epilepsy: Lessons from rural Zambia.

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Title: Paroxysmal slow wave events as a diagnostic biomarker for epilepsy: Lessons from rural Zambia.
Authors: Malunga, Andrew (AUTHOR), Lash, Sina (AUTHOR), Abu‐Ahmad, Alaa (AUTHOR), Alhadeed, Laith (AUTHOR), Benninger, Felix (AUTHOR), Ben‐Arie, Gal (AUTHOR), Fearns, Nicholas (AUTHOR), Imtiaz, Hamza (AUTHOR), Kunst, Stefan (AUTHOR), Minarik, Anna (AUTHOR), Masamu, Mutale (AUTHOR), Mshanga, George (AUTHOR), Neal, Oliver (AUTHOR), Racz, Attila (AUTHOR), Ruber, Theodor (AUTHOR), Saadeh, Khalid (AUTHOR), Serlin, Yonatan (AUTHOR), Shamir, Merav (AUTHOR), Welte, Tamara (AUTHOR), Whatley, Benjamin (AUTHOR)
Source: Epilepsia (Series 4). Dec2025, Vol. 66 Issue 12, p4869-4880. 12p.
Subjects: Epilepsy, Biomarkers, Electroencephalography, Diagnosis, Low-income countries, Brain imaging
Geographic Terms: Zambia
Abstract: 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. [ABSTRACT FROM AUTHOR]
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Database: Psychology and Behavioral Sciences Collection
Description
Abstract: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. [ABSTRACT FROM AUTHOR]
ISSN:00139580
DOI:10.1111/epi.18598