Multivariate Variational Mode Decomposition
Naveed ur Rehman,Hania Aftab +1 more
TL;DR: In this article, a generic extension of variational mode decomposition (VMD) algorithm to multivariate or multichannel data is presented, which utilizes a model for multivariate modulated oscillations that is based on the presence of a joint or common frequency component among all channels of input data.
read more
Abstract: We present a generic extension of variational mode decomposition (VMD) algorithm to multivariate or multichannel data. The proposed method utilizes a model for multivariate modulated oscillations that is based on the presence of a joint or common frequency component among all channels of input data. We then formulate a variational optimization problem that aims to extract an ensemble of band-limited modes containing inherent multivariate modulated oscillations present in the data. The cost function to be minimized is the sum of bandwidths of all signal modes across all input data channels, which is a generic extension of the cost function used in standard VMD to multivariate data. Minimization of the resulting variational model is achieved through the alternating direction method of multipliers (ADMM) that yields an optimal set of multivariate modes in terms of narrow bandwidth and corresponding center frequencies. The proposed extension is elegant as it does not require any extra user-defined parameters for its operation i.e., it uses the same parameters as standard VMD. We demonstrate the effectiveness of the proposed method through results obtained from extensive simulations involving test (synthetic) and real world multivariate data sets. Specifically, we highlight the utility of the proposed method in two real world applications which include the separation of alpha rhythms in multivariate electroencephalogram (EEG) data and the decomposition of bivariate cardiotocographic signals that consist of fetal heart rate and maternal uterine contraction (FHR-UC) as its two channels.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Schizophrenia detection technique using multivariate iterative filtering and multichannel EEG signals
TL;DR: In this article, a new approach for extension of univariate iterative filtering (IF) for decomposing a signal into intrinsic mode functions (IMFs) or oscillatory modes is proposed for multivariate multi-component signals.
111
Central frequency mode decomposition and its applications to the fault diagnosis of rotating machines
TL;DR: In this article , a central frequency mode decomposition (CFMD) is proposed based on the investigation results of the decomposing characteristics of two sub-models buried in VMD to realize effective and adaptive signal decomposition.
93
Human emotion recognition based on time–frequency analysis of multivariate EEG signal
01 Feb 2022
TL;DR: Zhang et al. as discussed by the authors proposed a novel four-stage method for human emotion recognition using multivariate EEG signals, in which multivariate variational mode decomposition (MVMD) is employed to extract an ensemble of multivariate modulated oscillations (MMOs) from multichannel EEG signals.
82
Multichannel fault diagnosis of wind turbine driving system using multivariate singular spectrum decomposition and improved Kolmogorov complexity
TL;DR: A new approach based on multivariate singular spectrum decomposition (MSSD), an improved complexity metric abbreviated as IKC is proposed to capture the fault information of multichannel mode components, which can enhance fault feature extraction ability of KC.
80
Looseness monitoring of multiple M1 bolt joints using multivariate intrinsic multiscale entropy analysis and Lorentz signal-enhanced piezoelectric active sensing
TL;DR: In this article , a novel bolt joints monitoring method using multivariate intrinsic multiscale entropy (MIME) analysis and Lorentz signal-enhanced piezoelectric active sensing is proposed.
65
References
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Norden E. Huang,Zheng Shen,Steven R. Long,Man-Li C. Wu,Hsing H. Shih,Quanan Zheng,Nai-Chyuan Yen,C. C. Tung,Henry H. Liu +8 more
TL;DR: In this paper, a new method for analysing nonlinear and nonstationary data has been developed, which is the key part of the method is the empirical mode decomposition method with which any complicated data set can be decoded.
•Book
Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers
Stephen Boyd,Neal Parikh,Eric Chu,Borja Peleato,Jonathan Eckstein +4 more
- 23 May 2011
TL;DR: It is argued that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.
PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.
Ary L. Goldberger,Luís A. Nunes Amaral,Leon Glass,Jeffrey M. Hausdorff,Plamen Ch. Ivanov,Roger G. Mark,Joseph E. Mietus,George B. Moody,Chung-Kang Peng,H. Eugene Stanley +9 more
TL;DR: The newly inaugurated Research Resource for Complex Physiologic Signals (RRSPS) as mentioned in this paper was created under the auspices of the National Center for Research Resources (NCR Resources).
14.3K
Orthonormal bases of compactly supported wavelets
TL;DR: This work construct orthonormal bases of compactly supported wavelets, with arbitrarily high regularity, by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction.
Variational Mode Decomposition
TL;DR: This work proposes an entirely non-recursive variational mode decomposition model, where the modes are extracted concurrently and is a generalization of the classic Wiener filter into multiple, adaptive bands.
6.7K