The prediction of epileptic seizures has been an essential problem of epilepsy study. The calcium imaging video data images the whole brain-wide neurons activities with electrical discharge recorded by calcium fluorescence intensity (CFI). In this paper, using the zebrafish's brain-wide calcium image video data, we propose a data-driven approach to effectively detect the systemic change-point, and further predict the epileptic seizures. Our approach includes two phases: offline training and online testing. Specifically, during offline training, we extract features and confirm the existence of systemic change-point, then estimate the ratio of unchanged system duration to interictal period duration. For online testing, we implement a statistical model to estimate the change-point, and then predict the onset of epileptic seizure. The testing results show that our proposed approach could effectively predict the time range of future epileptic seizure. Furthermore, we explore the macroscopic patterns of epileptic and control cases, and extract features based on the pattern difference, then implement and compare the classification performance from four machine learning models. Based on the data structure, we also propose a new method to discretize related features, and combine with hierarchical clustering to better visualize and explain the pattern difference between epileptic and control cases.