Frequency Detection and Change Point Estimation for Time Series of Complex Oscillation

TL;DR

This paper introduces a high-dimensional frequency detection and change point estimation method for complex oscillatory signals contaminated by non-stationary noise, with applications to sleep EEG analysis.

Contribution

It proposes a novel dense progressive periodogram test and phase-adjusted local change point detection in the frequency domain for complex oscillatory signals.

Findings

Accurately detects all oscillatory frequencies and change points within a prescribed probability

Effective in analyzing physiological time series such as sleep EEG data

Develops Gaussian approximation and bootstrap methods for complex high-dimensional non-stationary series

Abstract

We consider detecting the evolutionary oscillatory pattern of a signal when it is contaminated by non-stationary noises with complexly time-varying data generating mechanism. A high-dimensional dense progressive periodogram test is proposed to accurately detect all oscillatory frequencies. A further phase-adjusted local change point detection algorithm is applied in the frequency domain to detect the locations at which the oscillatory pattern changes. Our method is shown to be able to detect all oscillatory frequencies and the corresponding change points within an accurate range with a prescribed probability asymptotically. This study is motivated by oscillatory frequency estimation and change point detection problems encountered in physiological time series analysis. An application to spindle detection and estimation in sleep EEG data is used to illustrate the usefulness of the proposed methodology. A Gaussian approximation scheme and an overlapping-block multiplier bootstrap methodology for sums of complex-valued high dimensional non-stationary time series without variance lower bounds are established, which could be of independent interest.