Automated Change-Point Detection of EEG Signals Based on Structural Time-Series Analysis

Automated change-point detection of EEG signals is becoming essential for the monitoring of health behaviors and health status in a wide range of clinical applications. This paper presents a structural time-series analysis to capture and characterize the dynamic behavior of EEG signals, and develops a method to detect the EEG change points. For a given EEG signal, the proposed method is operated as follows: 1) a sub-band pass filter is fist designed to capture those frequency components that can characterize the dynamic behavior of the data, and the so-called power spectrum is extracted as the EEG features; 2) together with a sliding-window technique, an automatic `segment-to-segment' analysis of EEG signal, is developed with a null hypothesis testing for decision making. In particular, the main challenge of the proposed method is to design an appropriate distance metric that is compatible with our considered data/problem. To achieve this end, we first collect a variety of metrics from other areas that would be potentially available for our problem, and then compare them for the considered EEG change point detection. Experiments are conducted on two different data sets. Results show the Bhattacharyya distance achieves the best detection result among all investigated metrics. Meanwhile, comparison with state-of-the-arts demonstrates the effectiveness of the method in real applications.