scholarly journals Comparison between traditional FFT and marginal spectra using the Hilbert-Huang transform method for the broadband spectral analysis of the EEG in healthy humans

2020 ◽  
Author(s):  
Eduardo Arrufat-Pié ◽  
Mario Estévez Báez ◽  
José Mario Estévez Carreras ◽  
Calixto Machado Curbelo ◽  
Gerry Leisman ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Main Tool ◽  
Good Alternative ◽  
Transform Method ◽  
Hilbert Huang Transform ◽  
Multivariate Empirical Mode Decomposition ◽  
Healthy Humans ◽  
Mode Decomposition ◽  
Stationary Signals ◽  
Mean Differences

AbstractThe fast Fourier transform (FFT), has been the main tool for the EEG spectral analysis (SPA). However, as the EEG dynamics shows nonlinear and non-stationary behavior, results using the FFT approach may result meaningless. A novel method has been developed for the analysis of nonlinear and non-stationary signals known as the Hilbert-Huang transform method. In this study we describe and compare the spectral analyses of the EEG using the traditional FFT approach with those calculated with the Hilbert marginal spectra (HMS) after decomposition of the EEG with a multivariate empirical mode decomposition algorithm. Segments of continuous 60-seconds EEG recorded from 19 leads of 47 healthy volunteers were studied. Although the spectral indices calculated for the explored EEG bands showed significant statistical differences for different leads and bands, a detailed analysis showed that for practical purposes both methods performed substantially similar. The HMS showed a reduction of the alpha activity (−5.64%), with increment in the beta-1 (+1.67%), and gamma (+1.38%) fast activity bands, and also an increment in the theta band (+2.14%), and in the delta (+0.45%) band, and vice versa for the FFT method. For the weighted mean frequencies insignificant mean differences (lower than 1Hz) were observed between both methods for the delta, theta, alpha, beta-1 and beta-2 bands, and only for the gamma band values for the HMS were 3 Hz higher than with the FFT method. The HMS may be considered a good alternative for the SPA of the EEG when nonlinearity or non-stationarity may be present.

scholarly journals Analysis in the frequency domain of multicomponent oscillatory modes of the human electroencephalogram extracted with multivariate empirical mode decomposition

2020 ◽  
Author(s):  
Eduardo Arrufat-Pié ◽  
Mario Estévez-Báez ◽  
José Mario Estévez-Carreras ◽  
Calixto Machado Curbelo ◽  
Gerry Leisman ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Empirical Mode Decomposition ◽  
A Priori ◽  
Quantitative Eeg ◽  
Frequency Resolution ◽  
Intrinsic Mode Functions ◽  
Multivariate Empirical Mode Decomposition ◽  
Healthy Humans ◽  
Mode Decomposition ◽  
Mixing Problem

AbstractConsidering the properties of the empirical mode decomposition to extract from a signal its natural oscillatory components known as intrinsic mode functions (IMFs), the spectral analysis of these IMFs could provide a novel alternative for the quantitative EEG analysis without a priori establish more or less arbitrary band limits. This approach has begun to be used in the last years for studies of EEG records of patients included in database repositories or including a low number of individuals or of limited EEG leads, but a detailed study in healthy humans has not yet been reported. Therefore, in this study the aims were to explore and describe the main spectral indices of the IMFs of the EEG in healthy humans using a method based on the FFT and another on the Hilbert-Huang transform (HHT). The EEG of 34 healthy volunteers was recorded and decomposed using a recently developed multivariate empirical mode decomposition algorithm. Extracted IMFs were submitted to spectral analysis with, and the results were compared with an ANOVA test. The first six decomposed IMFs from the EEG showed frequency values in the range of the classical bands of the EEG (1.5 to 56 Hz). Both methods showed in general similar results for mean weighted frequencies and estimations of power spectral density, although the HHT is recommended because of its better frequency resolution. It was shown the presence of the mode-mixing problem producing a slight overlapping of spectral frequencies mainly between the IMF3 and IMF4 modes.

The Hilbert-Huang Spectral Analysis Method Applied to VIV

2006 ◽  
Author(s):  
Celso P. Pesce ◽  
Andre´ L. C. Fujarra ◽  
Leonardo K. Kubota
Keyword(s):  
Spectral Analysis ◽  
Stationary Time Series ◽  
Analysis Method ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Time Modulation ◽  
Mode Decomposition ◽  
Highly Nonlinear ◽  
Stationary Signals ◽  
Spectral Analysis Method

Vortex-Induced Vibration (VIV) is a highly nonlinear dynamic phenomenon. Usual spectral analysis methods rely on the hypotheses of linear and stationary dynamics. A new method envisaged to treat nonlinear and non-stationary signals was presented by Huang et al. [1] : The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. This technique, called thereafter the Hilbert-Huang transform (or spectral analysis) method, is here applied to VIV phenomena, aiming at disclosing some hidden dynamic characteristics, such as the time-modulation and jumps of multi-branched response frequencies and their related energy spectra.

scholarly journals Comparison between traditional fast Fourier transform and marginal spectra using the Hilbert–Huang transform method for the broadband spectral analysis of the electroencephalogram in healthy humans

2021 ◽  
Author(s):  
Eduardo Arrufat‐Pié ◽  
Mario Estévez‐Báez ◽  
José Mario Estévez‐Carreras ◽  
Calixto Machado‐Curbelo ◽  
Gerry Leisman ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Transform Method ◽  
Hilbert Huang Transform ◽  
Healthy Humans

Application of Hilbert-Huang Transform Method on Fault Diagnosis for Wind Turbine Rotor

2009 ◽  
Vol 413-414 ◽  
pp. 159-166
Author(s):  
Qian Huang ◽  
Dong Xiang Jiang ◽  
Liang You Hong
Keyword(s):  
Wind Turbine ◽  
Original Data ◽  
Turbine Rotor ◽  
Noticeable Effect ◽  
Transform Method ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Stationary Signals ◽  
Wind Turbine Rotor

Many signals of wind turbine faults are non-stationary and have highly complex time-frequency characteristics. Traditional time-frequency analysis method, such as Windowed Fourier Transform method, has no noticeable effect in handing non-stationary signals. Hilbert-Huang Transform (HHT) is a new signal processing method for analyzing the non-stationary mechanical signals. Based on Empirical Mode Decomposition (EMD), the Intrinsic Mode Function (IMF) in HHT can reflect the intrinsic physical characteristics of original data. Moreover, it is a good way to identify the faults involving a breakdown change. First, the principles and advantages of the HHT are presented in detail in this paper. Then, three typical faults of wind turbine rotor, such as rotor imbalance, aerodynamic asymmetry due to blade surface roughness and yaw misalignment are discussed by the HHT. Last, reasonable conclusions are drawn by the comparison between this method and the Wavelet Transform (WT) method with the help of simulation fault signals. The results show the effectiveness of HHT method for diagnosing those faults of wind turbine rotor.

scholarly journals Assessment of Lift Passenger Comfort by the Hilbert–Huang Transform

2019 ◽  
Vol 8 (2) ◽  
pp. 373-380 ◽  
Author(s):  
Kamil Szydło ◽  
Piotr Wolszczak ◽  
Rafał Longwic ◽  
Grzegorz Litak ◽  
Mieczysław Dziubiński ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Human Body ◽  
Dominant Frequency ◽  
Health Condition ◽  
Passenger Comfort ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Resonance Frequencies ◽  
As Stress ◽  
The Hilbert Transform

Abstract Purpose The comfort of lift passengers has a significant effect on their general health condition as well as stress levels during travel. This study reports the results of vibration measurements taken during travel in a passenger lift. Methods Vibration signals were analyzed by the empirical mode decomposition method and the Hilbert transform. Results Selected modes from the Hilbert spectral analysis were compared with the resonance frequencies of human body organs (range 20–90 Hz) as well as with the resonance frequencies of lift components. Conclusion The use of Hilbert spectral analysis enables the isolation of individual signal components and the determination of the dominant frequency in the signal. This, in turn, allows for the isolation of raw vibration frequencies from the signal that are particularly significant for passenger comfort assessment (resonance frequencies of human body organs) and analysis of their occurrence.

scholarly journals On Holo-Hilbert spectral analysis: a full informational spectral representation for nonlinear and non-stationary data

2016 ◽  
Vol 374 (2065) ◽  
pp. 20150206 ◽  
Author(s):  
Norden E. Huang ◽  
Kun Hu ◽  
Albert C. C. Yang ◽  
Hsing-Chih Chang ◽  
Deng Jia ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
A Priori ◽  
Integral Transforms ◽  
Density Variation ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Wave Nonlinearity ◽  
Frequency Modulations ◽  
Multiplicative Processes

The Holo-Hilbert spectral analysis (HHSA) method is introduced to cure the deficiencies of traditional spectral analysis and to give a full informational representation of nonlinear and non-stationary data. It uses a nested empirical mode decomposition and Hilbert–Huang transform (HHT) approach to identify intrinsic amplitude and frequency modulations often present in nonlinear systems. Comparisons are first made with traditional spectrum analysis, which usually achieved its results through convolutional integral transforms based on additive expansions of an a priori determined basis, mostly under linear and stationary assumptions. Thus, for non-stationary processes, the best one could do historically was to use the time–frequency representations, in which the amplitude (or energy density) variation is still represented in terms of time. For nonlinear processes, the data can have both amplitude and frequency modulations (intra-mode and inter-mode) generated by two different mechanisms: linear additive or nonlinear multiplicative processes. As all existing spectral analysis methods are based on additive expansions, either a priori or adaptive, none of them could possibly represent the multiplicative processes. While the earlier adaptive HHT spectral analysis approach could accommodate the intra-wave nonlinearity quite remarkably, it remained that any inter-wave nonlinear multiplicative mechanisms that include cross-scale coupling and phase-lock modulations were left untreated. To resolve the multiplicative processes issue, additional dimensions in the spectrum result are needed to account for the variations in both the amplitude and frequency modulations simultaneously. HHSA accommodates all the processes: additive and multiplicative, intra-mode and inter-mode, stationary and non-stationary, linear and nonlinear interactions. The Holo prefix in HHSA denotes a multiple dimensional representation with both additive and multiplicative capabilities.

scholarly journals Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2912
Author(s):  
Joaquin Luque ◽  
Davide Anguita ◽  
Francisco Pérez ◽  
Robert Denda
Keyword(s):  
Spectral Analysis ◽  
Electricity Demand ◽  
Frequency Resolution ◽  
Electrical Networks ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Sequence Representation ◽  
Electrical Demand ◽  
Computer Resources

The large amount of sensors in modern electrical networks poses a serious challenge in the data processing side. For many years, spectral analysis has been one of the most used approaches to extract physically meaningful information from a sea of data. Fourier Transform (FT) and Wavelet Transform (WT) are by far the most employed tools in this analysis. In this paper we explore the alternative use of Hilbert–Huang Transform (HHT) for electricity demand spectral representation. A sequence of hourly consumptions, spanning 40 months of electrical demand in Spain, has been used as dataset. First, by Empirical Mode Decomposition (EMD), the sequence has been time-represented as an ensemble of 13 Intrinsic Mode Functions (IMFs). Later on, by applying Hilbert Transform (HT) to every IMF, an HHT spectrum has been obtained. Results show smoother spectra with more defined shapes and an excellent frequency resolution. EMD also fosters a deeper analysis of abnormal electricity demand at different timescales. Additionally, EMD permits information compression, which becomes very significant for lossless sequence representation. A 35% reduction has been obtained for the electricity demand sequence. On the negative side, HHT demands more computer resources than conventional spectral analysis techniques.

scholarly journals FINDING BICHROMATIC-BIDIRECTIONAL WAVES WITH ADVs

2018 ◽  
pp. 33
Author(s):  
Mario G. De Souza e Silva ◽  
Nils Kerpen ◽  
Paulo Cesar C. Rosman ◽  
Torsten Schlurmann ◽  
Claudio F. Neves
Keyword(s):  
Physical Model Test ◽  
Good Precision ◽  
Nonlinear Interactions ◽  
Spectral Techniques ◽  
Velocity Amplitude ◽  
Hilbert Huang Transform ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Wave Basin ◽  
Infragravity Wave

The aim of this study is to investigate Bichromatic-Bidirectional waves to characterize the subtractive wave-wave nonlinear interactions, using adaptive techniques rather than traditional spectral techniques. A physical model test in a 3D-wave basin was conducted and measurements were made with two arrays of ultrasonic sensors of free surface and one array of ADVs. The Hilbert-Huang transform, aided by the Multivariate Empirical Mode Decomposition, was applied to the orbital velocity data and the main characteristics of the infragravity wave (velocity amplitude, period and direction) were extracted with a good precision.

SIMULATION OF EARTHQUAKE RECORDS BY MEANS OF EMPIRICAL MODE DECOMPOSITION AND HILBERT SPECTRAL ANALYSIS

2014 ◽  
Vol 08 (01) ◽  
pp. 1450002 ◽  
Author(s):  
ABDOLLAH BAGHERI ◽  
AMIR A. FATEMI ◽  
GHOLAMREZA GHODRATI AMIRI
Keyword(s):  
Spectral Analysis ◽  
Empirical Mode Decomposition ◽  
Time History ◽  
Response Spectra ◽  
Frequency Function ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Earthquake Records ◽  
Mode Decomposition ◽  
Time Histories

One of the most important problems in the design of earthquake resistance structures at sites with no strong ground motion data is the generation and simulation of earthquake records. In this paper, an effective method based on Hilbert–Huang transform for the simulation of earthquake time histories is presented. The Hilbert–Huang transform consists of the empirical mode decomposition and Hilbert spectral analysis. Earthquake time histories decompose via empirical mode decomposition to obtain the intrinsic mode functions of earthquake time history. Any of intrinsic mode functions is simulated based on the proposed method for simulation. The ground frequency function of the presented model is estimated using Hilbert spectral analysis for the simulation of earthquake accelerograms. The proposed method has been applied to three earthquake records to demonstrate the efficiency and reliability of the approach. The obtained results of simulating method by comparison between pseudo-acceleration and pseudo-velocity response spectra of actual and the average of simulated time histories for these three earthquakes reveal that the simulated earthquake time histories well preserve the significant properties and the nonstationary characteristics of the actual earthquake records. The results indicated that there is a good accord between the response spectra of simulated and genuine time histories.

Analysis Methodology of Vortex-Induced Motions (VIM) on a Monocolumn Platform Applying the Hilbert-Huang Transform Method

2010 ◽  
Author(s):  
Rodolfo T. Gonc¸alves ◽  
Guilherme R. Franzini ◽  
Guilherme F. Rosetti ◽  
Andre´ L. C. Fujarra ◽  
Kazuo Nishimoto
Keyword(s):  
Mooring Lines ◽  
Transform Method ◽  
Hilbert Huang Transform ◽  
Analysis Methodology ◽  
Numerical Predictions ◽  
Non Linear ◽  
Stationary Signals ◽  
Induced Motion ◽  
The Difference ◽  
The Fourier Transform

Vortex-Induced Motion (VIM) is a highly non-linear dynamic phenomenon. Usual spectral analysis methods, using the Fourier transform, rely on the hypotheses of linear and stationary dynamics. A method to treat non-stationary signals that emerge from non-linear systems is denoted Hilbert-Huang transform method (HHT). The development of an analysis methodology to study the VIM of a MPSO (Monocolumn Production, Storage and Offloading System) using HHT was presented. The purposes of the analysis methodology are to improve the statistics characteristics of VIM. The results showed to be comparable to results obtained from the traditional analysis (mean of the 10% highest peaks) principally for the motions in the transverse direction, although the difference between the results from the traditional analysis for the motions in the in-line direction showed a difference of around 25%. The results from the HHT analysis are more reliable than the traditional ones, owing to the larger number of points to calculate the statistics characteristics. These results should be used to design the risers and mooring lines, as well as to obtain parameters of the VIM to calibrate numerical predictions.

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