An Efficient Forecasting Approach to Reduce Boundary Effects in Real-Time Time-Frequency Analysis

TL;DR

This paper introduces a computationally efficient forecasting method to mitigate boundary effects in real-time time-frequency analysis of nonstationary signals, validated on biomedical data.

Contribution

It proposes a novel signal extension technique using forecasting to reduce boundary effects in real-time TF representations, with theoretical and practical validation.

Findings

Effective reduction of boundary artifacts in TF representations.

Theoretical guarantees for locally oscillating signals.

Successful application to biomedical signals.

Abstract

Time-frequency (TF) representations of time series are intrinsically subject to the boundary effects. As a result, the structures of signals that are highlighted by the representations are garbled when approaching the boundaries of the TF domain. In this paper, for the purpose of real-time TF information acquisition of nonstationary oscillatory time series, we propose a numerically efficient approach for the reduction of such boundary effects. The solution relies on an extension of the analyzed signal obtained by a forecasting technique. In the case of the study of a class of locally oscillating signals, we provide a theoretical guarantee of the performance of our approach. Following a numerical verification of the algorithmic performance of our approach, we validate it by implementing it on biomedical signals.