Akademik Veri Yönetim Sistemi
Seizure, cilt.126, ss.16-23, 2025 (SCI-Expanded, Scopus)
Purpose: Advancements in Machine Learning (ML) techniques have revolutionized diagnosing and monitoring epileptic seizures using Electroencephalogram (EEG) signals. This analysis aims to determine the effectiveness of ML techniques in recognizing patterns of epileptic seizures in the brain using EEG signals. Methods: We searched PubMed, Scopus, and Google Scholar for relevant RCTs, cohort studies, and case-control studies involving patients with prior epileptic seizures who underwent EEG analysis aided by ML techniques. Using the STATA software, we evaluated the accuracy of predicting epileptic seizures, measured using metrics such as Area under the curve (AUC), Sensitivity, and Specificity. Results: The random effects bivariate model of 4 studies with 214 patients revealed high diagnostic performance for ML techniques in detecting epileptic signals in EEGs. The estimated sensitivity was 0.97 (95 % CI: 0.92–0.99), indicating its ability to accurately detect the condition in 97 % of cases. Similarly, the estimated specificity was 0.99 (95 % CI: 0.98–0.99), demonstrating its ability to correctly identify the absence of the condition in 99 % of cases. There was also a high AUC (1.00, 95 % CI: 0.99–1.00), indicating ML techniques can distinguish epileptic seizures from no seizures in EEG signals 100 % of the time. These findings underscore the test's robust diagnostic utility in sensitivity and specificity. There was a significant between-study variability (heterogeneity) with a chi-square p-value <0.001 and an I2 value of 95 %. A bivariate box plot further confirmed the heterogeneity. Deek's test for publication bias showed a non-significant p-value (p = 0.06) indicating the absence of publication bias. Conclusion: ML techniques can potentially enhance diagnostic accuracy in epilepsy detection, offering valuable insights into developing advanced diagnostic tools for clinical practice.