A Change Dynamic Model for the Online Detection of Gradual Change

TL;DR

This paper introduces a Bayesian change-dynamic model for online detection of gradual changes in stochastic processes, improving detection speed and accuracy over traditional change-point models, demonstrated on synthetic and EEG data.

Contribution

A novel hierarchical Bayesian model specifically designed for detecting gradual changes in real-time, addressing limitations of traditional change-point approaches.

Findings

Faster detection of gradual changes in synthetic data.

More accurate identification of change onset and termination.

Effective application to EEG data during epileptic seizures.

Abstract

Changes in the statistical properties of a stochastic process are typically assumed to occur via change-points, which demark instantaneous moments of complete and total change in process behavior. In cases where these transitions occur gradually, this assumption can result in a reduced ability to properly identify and respond to process change. With this observation in mind, we introduce a novel change-dynamic model for the online detection of gradual change in a Bayesian framework, in which change-points are used within a hierarchical model to indicate moments of gradual change onset or termination. We apply this model to synthetic data and EEG readings drawn during epileptic seizure, where we find our change-dynamic model can enable faster and more accurate identification of gradual change than traditional change-point models allow.