ArticleJournal of neurology2022
An integrative prediction algorithm of drug-refractory epilepsy based on combined clinical-EEG functional connectivity features.
Article in Journal of neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Machine Learning for Predicting Thromboembolic Events Following Flow Diverter Treatment of Intracranial Aneurysms: A Multicenter Retrospective Study.Neurology and therapy · 2025Article
- Increased coherence predicts medical refractoriness in patients with temporal lobe epilepsy on monotherapy.Scientific reports · 2024Article
- MSE-VGG: A Novel Deep Learning Approach Based on EEG for Rapid Ischemic Stroke Detection.Sensors (Basel, Switzerland) · 2024Article
- Neuronal avalanches in temporal lobe epilepsy as a noninvasive diagnostic tool investigating large scale brain dynamics.Scientific reports · 2024Article
- A comprehensive prediction model of drug-refractory epilepsy based on combined clinical-EEG microstate features.Therapeutic advances in neurological disorders · 2024Article
- Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder.Brain sciences · 2023Article
- Feature Extraction of the Brain's Dynamic Complex Network Based on EEG and a Framework for Discrimination of Pediatric Epilepsy.Sensors (Basel, Switzerland) · 2022Article
- The performance evaluation of the state-of-the-art EEG-based seizure prediction models.Frontiers in neurology · 2022Review
- Development and validation of a nomogram for the early prediction of drug resistance in children with epilepsy.Frontiers in pediatrics · 2022Article
- EEG-Driven Prediction Model of Oxcarbazepine Treatment Outcomes in Patients With Newly-Diagnosed Focal Epilepsy.Frontiers in medicine · 2021Article
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Authors and funding
14 authors.
Funding
Abstract
objectiveAlthough the use of antiepileptic drugs (AEDs) is routine, 30-40% of patients with epilepsy (PWEs) experience drug resistance. Thus, early identification of AED resistance will help optimize treatment regimens and improve patients' prognoses. However, there have been few studies on this topic to date. Here, we try to establish an integrative prediction model of AED resistance for drug-naive PWEs, and to identify the clinical and Electroencephalogram (EEG) factors that affect their outcomes.
methodsOne hundred sixty-four PWEs naive to AEDs treated at a tertiary care center from January 2014 to June 2020 were retrospectively analyzed. A total of 113 of these patients were well controlled and 53 were drug refractory with regular AED treatment for more than one year. Eighty clinical characteristics and 684 EEG functional connectivity variables based on phase lag index before drug initiation were identified. Overall, 80% of each group was chosen to establish a support vector machine (SVM) model with ten-fold cross validation, and the other 20% were used to evaluate the model's performance. Absolute weight value was used to rank the features that had impacts on classification.
resultsAn integrative algorithm was modeled to predict AED resistance for drug-naive PWEs by SVM based on clinical characteristics and EEG functional connectivity values. The model had an accuracy of 94% [95% confidence interval (CI) 0.85-1.0], sensitivity of 95% [95% CI 0.82-1.0], specificity of 93% [95% CI 0.77-1.0], and an area under the curve (AUC) of 0.98 [95% CI 0.91-1.0]. The p values of accuracy, sensitivity specificity and AUC were calculated as 0.001, 0.001, 0.01 and 0.001, respectively. The δ band fromT4-FZ and T3-PZ, α band from T3-T6 and β band from F7-CZ and FP2-F3 were the top five EEG features that impacted the SVM classifier.
conclusionWe constructed an integrative prediction algorithm of AED resistance for drug-naive PWEs. Its utility in clinical settings should be evaluated in the future.
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