Ensemble Patch Transformation: A New Tool for Signal Decomposition

TL;DR

This paper introduces ensemble patch transformation, a multiscale signal decomposition and visualization tool that improves local feature identification and component extraction in signals, with theoretical analysis and practical validation.

Contribution

It proposes a novel ensemble patch transformation for multiscale signal decomposition, extending data-adaptive methods like EMD with theoretical insights and empirical validation.

Findings

Effective in extracting meaningful signal components

Provides enhanced multiscale visualization of signals

Validated on synthetic and real-world data

Abstract

This paper considers the problem of signal decomposition and data visualization. For this purpose, we introduce a new multiscale transform, termed `ensemble patch transformation' that enhances identification of local characteristics embedded in a signal and provides multiscale visualization according to different levels; hence, it is useful for data analysis and signal decomposition. In literature, there are data-adaptive decomposition methods such as empirical mode decomposition (EMD) by Huang et al. (1998). Along the same line of EMD, we propose a new decomposition algorithm that extracts meaningful components from a signal that belongs to a large class of signals, compared to the previous methods. Some theoretical properties of the proposed algorithm are investigated. To evaluate the proposed method, we analyze several synthetic examples and a real-world signal.