Multivariate empirical mode decomposition

Rehman, Naveed ur; Mandic, Danilo P.

doi:10.1098/rspa.2009.0502

Cited by 796 publications

(703 citation statements)

References 17 publications

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“…To obtain monocomponent bases common for all the data channels within multivariate signals in a data-adaptive manner, a generalized multivariate extension of EMD (MEMD) has been developed recently [16], which operate directly on multivariate signals, containing any number of data channels. The operation of the MEMD algorithm rests on the estimation of the local mean of multivariate signals, a key step in EMD-based algorithms which is achieved in single-channel EMD by taking the average of the upper and lower envelopes of the signal in hand, obtained by interpolating the local maxima (upper envelope) and the local minima (lower envelope).…”

Section: Multivariate Empirical Mode Decompositionmentioning

confidence: 99%

Dynamical complexity of human responses: a multivariate data-adaptive framework

Ahmed¹,

Rehman²,

Looney³

et al. 2012

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract. Established complexity measures typically operate at a single scale and thus fail to quantify inherent long-range correlations in real-world data, a key feature of complex systems. The recently introduced multiscale entropy (MSE) method has the ability to detect fractal correlations and has been used successfully to assess the complexity of univariate data. However, multivariate observations are common in many real-world scenarios and a simultaneous analysis of their structural complexity is a prerequisite for the understanding of the underlying signal-generating mechanism. For this purpose, based on the notion of multivariate sample entropy, the standard MSE method is extended to the multivariate case, whereby for rigor, the intrinsic multivariate scales of the input data are generated adaptively via the multivariate empirical mode decomposition (MEMD) algorithm. This allows us to gain better understanding of the complexity of the underlying multivariate real-world process, together with more degrees of freedom and physical interpretation in the analysis. Simulations on both synthetic and real-world biological multivariate data sets support the analysis.

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Section: Multivariate Empirical Mode Decompositionmentioning

confidence: 99%

Dynamical complexity of human responses: a multivariate data-adaptive framework

Ahmed¹,

Rehman²,

Looney³

et al. 2012

Bulletin of the Polish Academy of Sciences: Technical Sciences

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“…The multivariate EMD (mEMD) is more generalized extension of EMD suitable for dealing with direct processing of multivariate data for real world applications [30]. Standard EMD revealed that IMFs tend to mimic a filter bank-like decomposition, similar to wavelet decompositions.…”

Section: Multivariate Emd (Memd)mentioning

confidence: 99%

“…This step is complex to perform due to the lack of formal definition of maxima and minima in n-dimensional domains in general EMD. The sampling based on low discrepancy Hammersley sequence is used to generate projections of input signal in [30]. Once the projections along different directions in multidimensional spaces are obtained, their extrema are interpolated via cubic spline interpolation to obtain multiple signal envelopes.…”

Section: Multivariate Emd (Memd)mentioning

confidence: 99%

Empirical Mode Decomposition for Advanced Speech Signal Processing

Molla

Das

Hamid

et al. 2013

Journal of Signal Processing

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Empirical mode decomposition (EMD) is a newly developed tool to analyze nonlinear and non-stationary signals.It is used to decompose any signal into a finite number of time varying subband signals termed as intrinsic mode functions (IMFs). Such data adaptive decomposition is recently used in speech enhancement. This study presents the concept of EMD and its application to advanced speech signal processing paradigms including speech enhancement by soft-thresholding, voiced/unvoiced (V/Uv) speech discrimination and pitch estimation. The speech processing is frequently performed in the transformed domain and the transformation is usually achieved by traditional signal analysis techniques i.e. Fourier and wavelet transformations. These analysis methods employ priori basis function and it is not suitable for data adaptive analysis for non-stationary signal like speech. Recently, EMD is taken much attention for speech signal processing in data adaptive way. Several EMD based potential soft-thresholding algorithms for speech enhancement are discussed here. The V/Uv discrimination is an important concern in speech processing. It is usually performed by using acoustic features. The training data is used to determine the threshold for classification. The EMD based data adaptive thresholding approach is developed for V/Uv discrimination without any training phase. Noticeable improvement is achieved with the application of EMD in pitch estimation of noisy speech signals. The related experimental results are also presented to realize the effectiveness of EMD in advanced speech processing algorithms.

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“…Empirical mode decomposition (Huang et al, 1998) separates fast and slow oscillatory components of non-linear and non-stationary signals into intrinsic mode functions (IMFs), unlike harmonic analysis, which decomposes signals into components that are integer multiples of the fundamental frequency. To align the oscillatory components of ENSO and influenza time series and reduce mode mixing, noise assisted multivariate empirical mode decomposition (Rehman and Mandic, 2010a) which applies multivariate empirical mode decomposition algorithm (Rehman and Mandic, 2010a), was used. The algorithm of multivariate EMD (Rehman and Mandic, 2010a) is as follows:…”

Section: Oscillatory Components Of Enso and Influenza Time Seriesmentioning

confidence: 99%

“…To align the oscillatory components of ENSO and influenza time series and reduce mode mixing, noise assisted multivariate empirical mode decomposition (Rehman and Mandic, 2010a) which applies multivariate empirical mode decomposition algorithm (Rehman and Mandic, 2010a), was used. The algorithm of multivariate EMD (Rehman and Mandic, 2010a) is as follows:…”

Section: Oscillatory Components Of Enso and Influenza Time Seriesmentioning

confidence: 99%

Deterministic Chaos, El Niño Southern Oscillation, and Seasonal Influenza Epidemics

Oluwole

2017

Front. Environ. Sci.

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Low precipitation enhances transmission of influenza viruses, which cause seasonal epidemics during the winter in northern and southern hemispheres. El Niño southern oscillation (ENSO) which modulates global precipitation is a multicomponent signal that is composed of sub-annual to multi-decadal oscillations. The dynamics of oscillatory components of ENSO and influenza are characterized, and causal relationship of annual oscillatory components is determined. Seasonality of influenza was determined in five geographical areas of north and south hemispheres. Monthly influenza time series of these regions and of ENSO were decomposed to oscillatory components. The oscillatory components were characterized in time-frequency and phase space domains. Periodicities of the oscillatory components of ENSO and influenza range from sub-annual to multidecadal. Time-dependent intrinsic correlations of instantaneous amplitude and frequencies of annual oscillatory components of ENSO and influenza are > 0.9. The dynamics of ENSO and influenza, which are dissipative with multifractal chaotic attractors, transit from quasi-periodic to chaotic regimes. Five most severe peaks of epidemic, which include 2009-2010 pandemic, occur during chaos. ENSO and influenza dynamics are phase coherent, but there is unidirectional causal effect of ENSO on influenza. Amplitude and frequency modulations of annual oscillatory components of ENSO and influenza are strongly coupled. Chaotic dynamics of ENSO determines the timing and severity of influenza epidemics. Monitoring of ENSO dynamics will aid public health surveillance of influenza epidemics.

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Multivariate empirical mode decomposition

Cited by 796 publications

References 17 publications

Dynamical complexity of human responses: a multivariate data-adaptive framework

Dynamical complexity of human responses: a multivariate data-adaptive framework

Empirical Mode Decomposition for Advanced Speech Signal Processing

Deterministic Chaos, El Niño Southern Oscillation, and Seasonal Influenza Epidemics

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