FPGA-Based Design for Online Computation of Multivariate Empirical Mode Decomposition

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 their large size and multidimensional nature,...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. I, Regular papers Vol. 67; no. 12; pp. 5040 - 5050
Main Authors: Gul, Sikender, Siddiqui, Muhammad Faisal, Rehman, Naveed ur
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Biomedical engineering
Computer architecture
Datasets
Decomposition
Empirical mode decomposition
Field programmable gate arrays
FPGA
Generators
Hardware
Interpolation
Multivariate analysis
multivariate empirical mode decomposition
Multivariate signals
Real-time systems
Sifting
Signal processing
Signal processing algorithms
time frequency methods
ISSN:1549-8328, 1558-0806
Online Access:Get full text
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Summary: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 their large size and multidimensional nature, thus requiring specialized algorithms and efficient hardware designs for online and real time processing. In this paper, we address this 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 online and real time processing. Our proposed architecture for MEMD uses fixed-point operations and employs cubic spline interpolation (CSI) within the sifting process. Finally, examples of decomposition of multivariate synthetic and real-world biological signals are provided.
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ISSN:1549-8328
1558-0806
DOI:10.1109/TCSI.2020.3012351