Epilepsy is a neurological disorder affecting 50 million individuals globally. Modern research has inspected the likelihood of forecasting epileptic seizures. Algorithmic investigations are giving promising results for seizure prediction. Though mostly seizure prediction algorithm uses pre-ictal (prodromal symptoms) events for prediction. On the contrary, prodromal symptoms may not necessarily be present in every patient or subject. This paper focuses on seizure forecasting regardless of the presence of pre-ictal (prodromal symptoms) using the single robust feature with maximum accuracy. Method: We evaluated datasets having 4-aminopydine induced seizure-like events rat's hippocampa slices and cortical tissue from pharmacoresistant epilepsy patients. The proposed methodology applies the Discrete Wavelet Transform (DWT) at levels 1-5 utilizing 'Daubechies-4'. Linear Discriminant classifier (LDC), Quadratic Discriminant Classifier (QDC) and Support Vector Machine (SVM) were used to classify each signal using eight discriminative features. Results: Classifier performance was assessed by parameters like true detections (TD), false detection (FD), accuracy (ACC), sensitivity (SEN), specificity (SPF), and positive predicted value (PPC), negative predicted value (NPV). Highest classification feature was selected as a seizure forecasting correlation vector and decision rule was formulated for seizure forecasting. Correlation vector served as a forecaster for current EEG activity. Proposed decision rule forecasted ongoing signal activity towards possible seizure condition true or false. The suggested framework revealed forecasting of ictal events at 10 seconds before the actual seizure. Conclusion: It is worth mentioning that the proposed study utilized a single linear feature to predict seizures precisely. Moreover, utilization of single feature encouraged in subsiding system complexity, processing delays, and system latency.
Keywords: EEG feature extraction; Epilepsy; LDC; QDC; SVM; forecasting; prediction; seizure; wavelet transform.