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            <title xml:lang="en">A novel optimization model to detect stages of epileptiform activities</title>
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                <term xml:lang="en">Empirical Mode Decomposition (EMD)</term>
                <term xml:lang="en">Double-Density Complex Discrete Wavelet Transform (DDCDWT)</term>
                <term xml:lang="en">Epileptiform activities</term>
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              <p>The transient and heterogeneous nature of seizures pose significant challenges in diagnosing epilepsy. Although various automated and computer-aided diagnosis solutions have been proposed but reproducibility and practical utility of many existing solutions remains challenging due to limited "annotated" datasets and use of artificial intelligence models as "black box". It is even more changeling to find a large publicly available annotated datasets for multiple stages of epileptiform activities. Current work proposed a convex optimization model that overcomes the limitations of less annotated datasets. A combination of linear, non-linear, and statistical features was extracted using Double-Density Complex Discrete Wavelet Transform and Empirical Mode Decomposition and Generalized Neural Network was used for multi-class classification. To check the robustness of the expert system for scenarios when less training data is available, the performance was validated through Monte Carlo cross-validation with different train-to-test ratios. The results using benchmark datasets demonstrated high-performance metrics, including class-specific sensitivity, specificity, overall accuracy, and Matthew’s Correlation Coefficient. These findings suggest that the proposed expert system can be translated into a tool to facilitate large-scale epilepsy screening.</p>
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