Real-time detection of seizure activities in epileptic patients is crucial and can help improve patients' quality of life. Accurate evaluation, pre-surgery assessments, seizure prevention, and emergency alerts for medical aid all depend on the rapid detection of the onset of seizures. A new method of feature selection and classification for rapid and precise epileptic seizure detection is discussed. In this solution, informative components of Electroencephalogram~(EEG) data are extracted and an automatic method is presented using Infinite Independent Component Analysis~(I-ICA) to select efficiently independent features. The feature space is divided into subspaces via random selection, and multi-channel Support Vector Machines~(SVMs) are used to classify the subspaces, then, the result of each classifier is combined by majority voting to find the final output. To evaluate the solution, a benchmark clinical intracranial EEG~(iEEG) of eight patients with temporal and extratemporal lobe epilepsy has been considered in a multi-tier cloud-computing architecture. Via the leave-one-out cross-validation, accuracy, sensitivity, specificity, and false positive and false negative ratios of the proposed method are 0.95, 0.96, 0.94, 0.06, and 0.04, respectively, which confirm the effectiveness of the proposed solution.