A NEW VIEW OF NONLINEAR WATER WAVES: The Hilbert Spectrum

Huang, Norden E.; Shen, Zheng; Long, Steven

doi:10.1146/annurev.fluid.31.1.417

Cited by 1,864 publications

(1,111 citation statements)

References 68 publications

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“…In the EMD applications reported in literature [18,19,20,21], the extracted modes are speculatively associated with specific physical or physiological aspects of the phenomenon investigated. In this application we demonstrated the association of the first IMF extracted from a tachogram with the simultaneously recorded respiratory signal.…”

Section: Discussionmentioning

confidence: 99%

Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition

Balocchi

Menicucci

Santarcangelo

et al. 2004

Chaos, Solitons & Fractals

143

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Heart rate variability (HRV) is a well-known phenomenon whose characteristics are of great clinical relevance in pathophysiologic investigations. In particular, respiration is a powerful modulator of HRV contributing to the oscillations at highest frequency. Like almost all natural phenomena, HRV is the result of many nonlinearly interacting processes; therefore any linear analysis has the potential risk of underestimating, or even missing, a great amount of information content. Recently the technique of Empirical Mode Decomposition (EMD) has been proposed as a new tool for the analysis of nonlinear and nonstationary data. We applied EMD analysis to decompose the heartbeat intervals series, derived from one electrocardiographic (ECG) signal of 13 subjects, into their components in order to identify the modes associated with breathing. After each decomposition the mode showing the highest frequency and the corresponding respiratory signal were Hilbert transformed and the instantaneous phases extracted were then compared. The results obtained indicate a synchronization of order 1:1 between the two series proving the existence of phase and frequency coupling between the component associated with breathing and the respiratory signal itself in all subjects.

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Section: Discussionmentioning

confidence: 99%

Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition

Balocchi

Menicucci

Santarcangelo

et al. 2004

Chaos, Solitons & Fractals

143

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show abstract

“…Even though challenging, new methods to examine data from the real world are certainly needed. A recently developed method, empirical mode decomposition (EMD) [7][8][9] seems to be able to meet the requirement of posterior basis function necessary for adaptive data analysis. in time to be preserved.…”

Section: Empirical Mode Decomposition (Emd) Is Newlymentioning

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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“…In the literature so far, different techniques have been used to define the stopping criterion (Huang et al 1999, 1998, Rilling et al 2003. In this study the criterion suggested by Huang et al (1999) was used and the sifting process was stopped when the number of zero crossings and extrema remains the same for S successive sifting steps. In order to establish the confidence limit for the parameter S each dataset was decomposed using different values for the S parameter (S = 2-10, 15 and 20) as proposed by Huang et al (2003).…”

Section: Empirical Mode Decompositionmentioning

confidence: 99%

Empirical mode decomposition analysis of HRV data from patients undergoing local anaesthesia (brachial plexus block)

Shafqat¹,

Pal²,

Kumari³

et al. 2011

Physiol. Meas.

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Spectral analysis of heart rate variability (HRV) is used for the assessment of cardiovascular autonomic control. In this study, a data-driven adaptive technique called empirical mode decomposition (EMD) and the associated Hilbert spectrum has been used to evaluate the effect of local anaesthesia on HRV parameters in a group of 14 patients undergoing axillary brachial plexus block. The normalized amplitude Hilbert spectrum was used to calculate the error index associated with the instantaneous frequency. The amplitude and the frequency values were corrected in the region where the error was higher than twice standard deviation. The intrinsic mode function (IMF) components were assigned to the LF and the HF part of the signal by making use of the centre frequency and the standard deviation spectral extension estimated from the marginal spectrum of the IMF components. The optimal range of the stopping criterion was found to be between 4 and 9 for the HRV data. The statistical analysis showed that the LF/HF ratio decreased within an hour of the application of the brachial plexus block compared to the values at the start of the procedure. These changes were observed in 13 of the 14 patients included in this study.

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A NEW VIEW OF NONLINEAR WATER WAVES: The Hilbert Spectrum

Cited by 1,864 publications

References 68 publications

Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition

Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition

Empirical Mode Decomposition for Advanced Speech Signal Processing

Empirical mode decomposition analysis of HRV data from patients undergoing local anaesthesia (brachial plexus block)

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