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Epileptic seizure prediction is a critical patient-specific challenging task, relies on big data streams, and is essential for patient care. With the aid of recent advances, together with forthcoming technologies and powerful computing capabilities, smart healthcare attracts great attention in harnessing Intelligent Computing to yield seizure prediction and detection. However, it is unclear how even simple classification methods can be employed to carry out such challenging and mission-critical tasks. This paper investigates the performance impact of mainstream supervised Machine Learning techniques with different configurations in predicting epileptic seizures. A lab-premised testbed, along with neurophysiological data in dogs, enables the set of tests. Through analysis in the Area Under the ROC Curve (AUC) Key Performance Information (KPI), it was found that classification committees show improved performance capabilities than single classifiers. Overall, we believe this work represents a step towards making seizure prediction more accurate and widely available in different computational platforms. |
