DMS Colloquium: Jingyi Zheng

Speaker: Jingyi Zheng,  Ph. D. candidate at the University of California at Davis

Title: A Data-driven Approach to Predict and Classify Epileptic Seizures from Brain-wide Calcium Imaging Video

Abstract: Epilepsy is a neurological disorder in the brain characterized by recurrent, unprovoked seizures. In this talk, we will discuss mainly three aspects of epilepsy study: (epilepsy) classification, (epileptic seizures) prediction, and spatiotemporal structure discovery. Unlike Electroencephalography (EEG) and fMRI data, the calcium imaging video data images the whole brain-wide neurons activities with electrical discharge recorded by calcium fluorescence intensity (CFI). Using zebrafish's brain-wide calcium imaging video data, we first propose a data-driven approach to effectively predict the epileptic seizures. Our approach includes two phases: offline training and online testing. Specifically, during offline training, we confirm the existence of systemic change point, and estimate the ratio of unchanged system duration. For online testing, we implement a statistical model to estimate the change point, and then predict the onset of epileptic seizure. Furthermore, we explore the macroscopic patterns of epileptic and control cases, and then build classifiers using machine learning models. Based on the data structure, we also propose a method to discretize related features, and further visualize the pattern difference using unsupervised learning methods. Finally, we discover the spatial structure based on mutual conditional entropy and recover the temporal system state trajectory that leads to epileptic seizures.