This study provides a performance evaluation of the correlation sum in terms of accuracy, sensitivity, and specificity in its ability to classify seizure files from non-seizure files. The main thrust of the study is whether computable properties ("metrics") of EEG tracings over time allow a seizure to be detected. This study evaluates raw intracranial EEG (iEEG) recordings with the intent to detect a seizure and classify different EEG epoch files. One hundred twenty-six iEEG files from eleven sequential patients are processed and the correlation sum is extracted from non-overlapping scrolling windows of 1-s duration. The novelty of this research is in defining a generalized nonlinear approach to classify EEG seizure segments by introducing nonlinear decision functions with the flexibility in choosing any degree of complexity and with any number of dimensions, lending resiliency to data overlap and opportunity for multidimensional data analysis. A singular contribution of this work is in determining a 2-D decision plane, in this case, where duration is one dimension and window-based minima of the correlation sum is the second dimension. Also, experimental observations clearly indicate that a significant drop in the magnitude of the correlation sum signal actually coincides with the clinical seizure onset more so than the electrographic seizure onset as provided by the medical experts. The method with k-fold cross validation performed with an accuracy of 91.84%, sensitivity of 92.31%, and specificity of 91.67%, which makes this classification method most suitable for offline seizure detection applications.