Paroxysmal slow wave events as a diagnostic biomarker for epilepsy: Lessons from rural Zambia
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".