Impact of Data Selection Strategies on EEG-Based Epilepsy Detection Using Stochastic Data Reduction and the EEG Continuity Principle

Epilepsy is a complex neurological condition characterized by recurrent seizures that manifest as both focal and generalized events, presenting unique challenges for diagnosis and treatment. This research investigated the impact of three distinct data selection strategies, the discrete selector technique, the continuous partial selector technique, and the complete selector technique, on electroencephalogram (EEG)-based epilepsy detection using convolutional neural network (CNN)-based approaches. Guided by the EEG continuity principle (ECP) and stochastic data reduction (SDR), we pre-processed EEG signals by normalizing, shuffling, and partitioning them into training and testing sets. The primary objective was to analyze and assess the behavior of these three strategies to recommend the best approach for brain data analysis in clinical applications. Moreover, optimizing model parameters and batch sizes led to a marked improvement in classification performance, enhancing the accuracy of epileptic seizure detection and ultimately advancing patient care outcomes. The discrete selector technique achieved an accuracy of 82.36%, precision of 83.92%, recall of 81.03%, specificity of 83.75%, F1 score of 82.46%, and an area under the receiver operating characteristic curve (ROC-AUC) of 82.39%. In contrast, the continuous partial selector technique recorded a preliminary accuracy of 66.14% in its first stage. The complete selector technique outperformed the other methods by achieving 96.62% accuracy, 94.57% precision, 96.75% recall, 94.50% specificity, 95.65% F1 score, and 95.63% ROC-AUC. Supplementary Material File (sn-article.pdf) - Download - 7.87 MB Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

Authors Metrics & Citations Metrics Article Usage 273views 255downloads Citations Download citation Nayer Seyed Hoseini, Masoud Kargar, Habib Izadkhah, et al. Impact of Data Selection Strategies on EEG-Based Epilepsy Detection Using Stochastic Data Reduction and the EEG Continuity Principle. Authorea. 26 November 2025. DOI: https://doi.org/10.22541/au.176419314.42726046/v1 DOI: https://doi.org/10.22541/au.176419314.42726046/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.