ArticleActa epileptologica2025
A hybrid metaheuristic framework for epileptic seizure detection in healthcare decision support systems.
Article in Acta epileptologica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Hybrid quantum classical framework for electroencephalogram driven neurological processing in epileptic seizure taxonomy.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe detection of epileptic seizures is a crucial aspect of epilepsy care, requiring precision and reliability for effective diagnosis and treatment. Seizure detection plays a critical role in healthcare informatics, aiding in the timely diagnosis and management of epilepsy. The use of computational intelligence and optimization techniques has shown significant promise in improving the performance of automated seizure detection systems.
methodsThis research work proposes a novel hybrid approach that combines Ant Colony Optimization (ACO) for feature selection with Gray Wolf Optimization (GWO) to optimize the hyperparameters of a Random Forest (RF) classifier. In this patient-specific seizure detection, ACO effectively reduces the feature set, improving computational efficiency, while GWO ensures optimal RF performance. The method is evaluated on the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) and Seina datasets, which include multichannel EEG data from epileptic patients. Performance metrics such as accuracy, sensitivity, and specificity are employed to evaluate the effectiveness of the seizure detection system.
resultsThe proposed ACO-GWO-RF pipeline demonstrated excellent performance on the CHB-MIT dataset, with a mean accuracy of 96.70%, mean sensitivity of 92.66%, and mean specificity of 99.24%, outperforming existing approaches. The mean values of accuracy, sensitivity, and specificity obtained using the Seina dataset are 93.01%, 89.82%, and 96.26%, respectively. These improvements highlight the robustness of the hybrid metaheuristic method in handling complex EEG data.
conclusionsThe hybrid metaheuristic approach effectively optimizes the processing and classification of EEG data for seizure detection. Its strong performance across datasets suggests potential for integration into interactive health applications. Furthermore, its patient-specific adaptability makes it a promising tool for personalized epilepsy diagnosis, treatment, and long-term management.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.