A confidence limit for the empirical mode decomposition and Hilbert spectral analysis

Huang, Norden E.; Wu, Man‐Li C.; Long, Steven; Shen, Samuel S. P.; Qu, Wendong; Gloersen, P.; Fan, Kuang-Lung

doi:10.1098/rspa.2003.1123

Cited by 1,068 publications

(640 citation statements)

References 18 publications

Supporting

Mentioning

581

Contrasting

Unclassified

Order By: Relevance

“…The squared deviation results obtained from all 14 patients included in this study are presented in figure 1. The minimum value of squared deviation has occurred between S values of 4-9 which is quite close to the interval suggested by Huang et al (2003), 4-8. In fact only in one of our datasets the minimum has occurred at S = 9.…”

Section: Hrv Data From Locally Anaesthetized Patientssupporting

confidence: 49%

“…More details about the IMF properties and extraction can be found in the literature (Huang et al 1998(Huang et al , 2003. A systematic way for extracting the IMF from a complicated dataset is known as sifting.…”

Section: Empirical Mode Decompositionmentioning

confidence: 99%

“…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). As in this case the number of IMFs produced by different S values was not the same, the mean and the standard deviation values were estimated by averaging the corresponding Hilbert spectrum, which has the same number of bins in frequency and temporal space pre-assigned (Huang et al 2003).…”

Section: Empirical Mode Decompositionmentioning

confidence: 99%

“…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). As in this case the number of IMFs produced by different S values was not the same, the mean and the standard deviation values were estimated by averaging the corresponding Hilbert spectrum, which has the same number of bins in frequency and temporal space pre-assigned (Huang et al 2003). The S value that gave the smallest squared deviation from the mean was considered optimal and was used as the stopping criterion for the sifting process.…”

Section: Empirical Mode Decompositionmentioning

confidence: 99%

See 3 more Smart Citations

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

Shafqat¹,

Pal²,

Kumari³

et al. 2011

Physiol. Meas.

View full text Add to dashboard Cite

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.

show abstract

Section: Hrv Data From Locally Anaesthetized Patientssupporting

confidence: 49%

Section: Empirical Mode Decompositionmentioning

confidence: 99%

Section: Empirical Mode Decompositionmentioning

confidence: 99%

Section: Empirical Mode Decompositionmentioning

confidence: 99%

See 2 more Smart Citations

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

Shafqat¹,

Pal²,

Kumari³

et al. 2011

Physiol. Meas.

View full text Add to dashboard Cite

show abstract

“…In the multivariate case, the residue corresponds to a signal whose projections do not contain enough extrema to form a meaningful multivariate envelope. The stopping criterion for mEMD of IMFs is similar to standard EMD [32], the difference being that the condition for equality of the number of extrema and zero crossings is not imposed, as extrema cannot be properly defined for multivariate signals.…”

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

View full text Add to dashboard Cite

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.

show abstract

Latitudinal dependence of short timescale fluctuations during intense geomagnetic storms: A permutation entropy approach

2015

View full text Add to dashboard Cite

The present study is focused on the analysis of the latitudinal dependence of complexity degree of the short timescale geomagnetic field fluctuations during four intense geomagnetic storms occurring between 2000 and 2003. The aim is that of investigating the qualitative and quantitative changes in the dynamical properties of the magnetospheric system during magnetically disturbed periods. We analyze the short timescale fluctuations (<120 min) of the horizontal component of the Earth's magnetic field at nine different geomagnetic observatories from 49°N magnetic latitude (MLat) to 87°N MLat, by filtering the signal via empirical mode decomposition and successively by applying the permutation entropy analysis to the filtered time series. We show that some properties of the geomagnetic fluctuations during the selected periods are the result of dynamical changes in the magnetospheric system response to the solar wind changes and that these geomagnetic fluctuations display a complex character during the geomagnetic storm development. This evidence supports the idea that the geomagnetic storm nature may be the result of a cooperative and collective dynamics. We also find that the permutation entropy values change with latitude suggesting a latitudinal dependence of the complexity degree of the signals.

show abstract

A confidence limit for the empirical mode decomposition and Hilbert spectral analysis

Cited by 1,068 publications

References 18 publications

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

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

Empirical Mode Decomposition for Advanced Speech Signal Processing

Latitudinal dependence of short timescale fluctuations during intense geomagnetic storms: A permutation entropy approach

Contact Info

Product

Resources

About