FPGA based design for online computation of Multivariate EMD (MEMD)

TL;DR

This paper presents a novel FPGA-based hardware architecture for real-time, online multivariate empirical mode decomposition (MEMD), enabling efficient processing of large, multichannel data sets in various engineering applications.

Contribution

It introduces the first parallel FPGA implementation of MEMD, utilizing fixed point operations and spline interpolation for on-line multivariate signal processing.

Findings

Successful decomposition of synthetic multivariate signals

Effective real-time processing demonstrated on biological signals

Hardware architecture achieves efficient, on-line computation

Abstract

Multivariate or multichannel data have become ubiquitous in many modern scientific and engineering applications, e.g., biomedical engineering, owing to recent advances in sensor and computing technology. Processing these data sets is challenging owing to: i) their large size and multidimensional nature, thus requiring specialized algorithms and efficient hardware designs for on-line and real-time processing; ii) the nonstationary nature of data arising in many real life applications demanding new extensions of standard multiscale non-stationary signal processing tools. In this paper, we address the former issue by proposing a fully FPGA based hardware architecture of a popular multi-scale and multivariate signal processing algorithm, termed as multivariate empirical mode decomposition (MEMD). MEMD is a data-driven method that extends the functionality of standard empirical mode decomposition (EMD) algorithm to multichannel or multivariate data sets. Since its inception in 2010, the algorithm has found wide spread applications spanning different engineering related fields. Yet, no parallel FPGA based hardware design of the algorithm is available for its on-line and real-time processing. Our proposed architecture for MEMD uses fixed point operations and employs cubic spline interpolation within the sifting process. Finally, examples of decomposition of multivariate synthetic and real world biological signals are provided.