Instantaneous EEG Signal Analysis Based on Empirical Mode Decomposition Applied to Burst-Suppression in Propofol Anaesthesia

The human electroencephalogram (EEG) constitutes a nonstationary, nonlinear electrophysiological signal resulting from synchronous firing of neurons in thalamocortical structures of the brain. Due to the complexity of the brain's physiological structures and its rhythmic oscillations, analysis of EEG often utilises spectral analysis methods.Aim: to improve clinical monitoring of neurophysiological signals and to further explain basic principles of functional mechanisms in the brain during anaesthesia.Material and methods. In this paper we used Empirical Mode decomposition (EMD), a novel spectral analysis method especially suited for nonstationary and nonlinear signals. EMD and the related Hilbert-Huang Transform (HHT) decompose signal into constituent Intrinsic Mode Functions (IMFs). In this study we applied EMD to analyse burst-suppression (BS) in the human EEG during induction of general anaesthesia (GA) with propofol. BS is a state characterised by cyclic changes between significant depression of brain activity and hyper-active bursts with variable duration, amplitude, and waveform shape. BS arises after induction into deep general anaesthesia after an intravenous bolus of general anaesthetics. Here we studied the behaviour of BS using the burst-suppression ratio (BSR).Results. Comparing correlations between EEG and IMF BSRs, we determined BSR was driven mainly by alpha activity. BSRs for different spectral components (IMFs 1-4) showed differing rates of return to baseline after the end of BS in EEG, indicating BS might differentially impair neural generators of low-frequency EEG oscillations and thalamocortical functional connectivity.Conclusion. Studying BS using EMD represents a novel form of analysis with the potential to elucidate neurophysiological mechanisms of this state and its impact on post-operative patient prognosis.

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Fault Diagnosis of Rolling Bearing based on an Improved Denoising Technique using Complete Ensemble Empirical Mode Decomposition and Adaptive Thresholding Method

10.21203/rs.3.rs-159429/v1 ◽

2021 ◽

Author(s):

Prashant Kumar Sahu ◽

Rajiv Nandan Rai

Keyword(s):

Empirical Mode Decomposition ◽

Signal Reconstruction ◽

Low Frequency ◽

Rolling Bearing ◽

Ensemble Empirical Mode Decomposition ◽

Adaptive Thresholding ◽

Intrinsic Mode Functions ◽

Vibration Signals ◽

Mode Decomposition ◽

Envelope Spectrum

Abstract The vibration signals for rotating machines are generally polluted by excessive noise and can lose the fault information at the early development phase. In this paper, an improved denoising technique is proposed for early faults diagnosis of rolling bearing based on the complete ensemble empirical mode decomposition (CEEMD) and adaptive thresholding (ATD) method. Firstly, the bearing vibration signals are decomposed into a set of various intrinsic mode functions (IMFs) using CEEMD algorithm. The IMFs grouping and selection are formed based upon the correlation coefficient value. The noise-predominant IMFs are subjected to adaptive thresholding for denoising and then added to the low-frequency IMFs for signal reconstruction. The effectiveness of the proposed method denoised signals are measured based on kurtosis value and the envelope spectrum analysis. The presented method results on experimental datasets illustrate that the proposed approach is an effective denoising technique for early fault detection in the rolling bearing.

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

10.1101/2020.06.06.138065 ◽

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.

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Random Noise Reduction Based on Ensemble Empirical Mode Decomposition and Wavelet Threshold Filtering

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.518-523.3887 ◽

2012 ◽

Vol 518-523 ◽

pp. 3887-3890 ◽

Cited By ~ 1

Author(s):

Wei Chen ◽

Shang Xu Wang ◽

Xiao Yu Chuai ◽

Zhen Zhang

Keyword(s):

Noise Reduction ◽

Empirical Mode Decomposition ◽

High Frequency ◽

Reduction Method ◽

Random Noise ◽

Low Frequency ◽

Ensemble Empirical Mode Decomposition ◽

Intrinsic Mode Functions ◽

Mode Decomposition ◽

Eemd Method

This paper presents a random noise reduction method based on ensemble empirical mode decomposition (EEMD) and wavelet threshold filtering. Firstly, we have conducted spectrum analysis and analyzed the frequency band range of effective signals and noise. Secondly, we make use of EEMD method on seismic signals to obtain intrinsic mode functions (IMFs) of each trace. Then, wavelet threshold noise reduction method is used on the high frequency IMFs of each trace to obtain new high frequency IMFs. Finally, reconstruct the desired signal by adding the new high frequency IMFs on the low frequency IMFs and the trend item together. When applying our method on synthetic seismic record and field data we can get good results.

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The Measurement and Elimination of Mode Splitting: From the Perspective of the Partly Ensemble Empirical Mode Decomposition

Complexity ◽

10.1155/2018/4230649 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Bin Liu ◽

Peng Zheng ◽

Qilin Dai ◽

Zhongli Zhou

Keyword(s):

Spectral Analysis ◽

White Noise ◽

Empirical Mode Decomposition ◽

Ensemble Empirical Mode Decomposition ◽

Permutation Entropy ◽

Intrinsic Mode Functions ◽

Mode Splitting ◽

Mode Decomposition ◽

Microseismic Signal ◽

Mode Mixing

The problems of mode mixing, mode splitting, and pseudocomponents caused by intermittence or white noise signals during empirical mode decomposition (EMD) are difficult to resolve. The partly ensemble EMD (PEEMD) method is introduced first. The PEEMD method can eliminate mode mixing via the permutation entropy (PE) of the intrinsic mode functions (IMFs). Then, bilateral permutation entropy (BPE) of the IMFs is proposed as a means to detect and eliminate mode splitting by means of the reconstructed signals in the PEEMD. Moreover, known ingredient component signals are comparatively designed to verify that the PEEMD method can effectively detect and progressively address the problem of mode splitting to some degree and generate IMFs with better performance. The microseismic signal is applied to prove, by means of spectral analysis, that this method is effective.

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A fast online bandwidth empirical mode decomposition scheme for avoidance of the mode mixing problem

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406217741515 ◽

2017 ◽

Vol 232 (20) ◽

pp. 3652-3674 ◽

Cited By ~ 1

Author(s):

SH Momeni Massouleh ◽

Seyed Ali Hosseini Kordkheili ◽

H Mohammad Navazi

Keyword(s):

Empirical Mode Decomposition ◽

Low Frequency ◽

Dominant Frequency ◽

Auxiliary Function ◽

Intrinsic Mode Functions ◽

Online Data ◽

Decomposition Scheme ◽

Mode Decomposition ◽

Mixing Problem ◽

Mode Mixing

The main objective of this work is to propose a scheme to extract intrinsic mode functions of online data with an acceptable speed as well as accuracy. For this purpose, an individual block framework method is firstly employed to extract the intrinsic mode functions. In this method, buffers are selected such that they overlap with their neighbors to prevent the end effect errors with no need for the averaging process. And in order to avoid the mode mixing problem, a bandwidth empirical mode decomposition scheme is developed to effectively improve the results. Through this scheme, an auxiliary function made of both high- and low-frequency components corresponding to noise and dominant frequency is added to data for the strengthening of the components for the better extraction of intrinsic mode functions during sifting process. An index criterion as well as a threshold limit is also introduced to separate high- and low-frequency parts of data at desired frequency range. Advantages of the proposed scheme are assessed and comparisons with the available methods are presented. Solution of different types of examples and experimentally generated data for two faulty ball bearings reveals that the present easily implemented scheme achieves results with lower computational efforts and accuracy.

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

Journal of Earthquake and Tsunami ◽

10.1142/s179343111450002x ◽

2014 ◽

Vol 08 (01) ◽

pp. 1450002 ◽

Cited By ~ 1

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.

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ASSESSING DISCONTINUOUS DATA USING ENSEMBLE EMPIRICAL MODE DECOMPOSITION

Advances in Adaptive Data Analysis ◽

10.1142/s179353691100091x ◽

2011 ◽

Vol 03 (04) ◽

pp. 483-491 ◽

Cited By ~ 7

Author(s):

BRADLEY LEE BARNHART ◽

HONDA KAHINDO WA NANDAGE ◽

WILLIAM EICHINGER

Keyword(s):

Empirical Mode Decomposition ◽

High Frequency ◽

Low Frequency ◽

Ensemble Empirical Mode Decomposition ◽

Data Sets ◽

Intrinsic Mode Functions ◽

Mode Decomposition ◽

Data Gaps ◽

Discontinuous Data ◽

Periodic Components

This investigation presents an improved ensemble empirical mode decomposition (EEMD) algorithm that can be applied to discontinuous data. The quality of the algorithm is assessed by creating artificial data gaps in continuous data, then comparing the extracted intrinsic mode functions (IMFs) from both data sets. The results show that errors increase as the gap length increases. In addition, errors in the high-frequency IMFs are less than the low-frequency IMFs. The majority of the errors in the high-frequency IMFs are due to end-effect errors associated with under-defined interpolation functions near the gap endpoints. A method that utilizes a mirroring technique is presented to reduce the errors in the discontinuous decomposition. The improved algorithm provides a more locally accurate decomposition of the data amidst data gaps. Overall, this simple but powerful algorithm expands EEMD's ability to locally extract periodic components from discontinuous data.

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A New Spectral Representation of Earthquake Recordings Using an Improved Hilbert-Huang Transform

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.255-260.1671 ◽

2011 ◽

Vol 255-260 ◽

pp. 1671-1675

Author(s):

Tian Li Huang ◽

Wei Xin Ren ◽

Meng Lin Lou

Keyword(s):

Empirical Mode Decomposition ◽

Spectral Representation ◽

Low Frequency ◽

Accurate Information ◽

Intrinsic Mode Functions ◽

Hilbert Spectrum ◽

Hilbert Huang Transform ◽

Time Frequency ◽

Mode Decomposition ◽

Marginal Spectrum

A new spectral representation method of earthquake recordings using an improved Hilbert-Huang transform (HHT) is proposed in the paper. Firstly, the problem that the intrinsic mode functions (IMFs) decomposed by the empirical mode decomposition (EMD) in HHT is not exactly orthogonal is pointed out and improved through the Gram-Schmidt orthogonalization method which is referred as the orthogonal empirical mode decomposition (OEMD). Combined the OEMD and the Hilbert transform (HT) which is referred as the improved Hilbert-Huang transform (IHHT), the orthogonal intrinsic mode functions (OIMFs) and the orthogonal Hilbert spectrum (OHS) and the orthogonal Hilbert marginal spectrum (OHMS) are obtained. Then, the IHHT has been applied for the analysis of the El Centro earthquake recording. The obtained spectral representation result shows that the OHS gives more detailed and accurate information in a time–frequency–energy presentation than the Hilbert spectrum (HS) and the OHMS gives more faithful low-frequency energy presentation than the Fourier spectrum (FS) and the Hilbert marginal spectrum (HMS).

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A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition

Entropy ◽

10.3390/e23091170 ◽

2021 ◽

Vol 23 (9) ◽

pp. 1170

Author(s):

Yangyang Dai ◽

Feng Duan ◽

Fan Feng ◽

Zhe Sun ◽

Yu Zhang ◽

...

Keyword(s):

Real Time ◽

Empirical Mode Decomposition ◽

Mean Squared Error ◽

Simulated Data ◽

Ensemble Empirical Mode Decomposition ◽

Intrinsic Mode Functions ◽

Eeg Signals ◽

Mode Decomposition ◽

Significant Difference ◽

The Brain

An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain–computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics. Therefore, EEG denoising is an essential preprocessing step in any BCI system. Previous studies have confirmed that the combination of ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA) can effectively suppress EMG artifacts. However, the time-consuming iterative process of EEMD may limit the application of the EEMD-CCA method in real-time monitoring of BCI. Compared with the existing EEMD, the recently proposed signal serialization based EEMD (sEEMD) is a good choice to provide effective signal analysis and fast mode decomposition. In this study, an EMG denoising method based on sEEMD and CCA is discussed. All of the analyses are carried out on semi-simulated data. The results show that, in terms of frequency and amplitude, the intrinsic mode functions (IMFs) decomposed by sEEMD are consistent with the IMFs obtained by EEMD. There is no significant difference in the ability to separate EMG artifacts from EEG signals between the sEEMD-CCA method and the EEMD-CCA method (p > 0.05). Even in the case of heavy contamination (signal-to-noise ratio is less than 2 dB), the relative root mean squared error is about 0.3, and the average correlation coefficient remains above 0.9. The running speed of the sEEMD-CCA method to remove EMG artifacts is significantly improved in comparison with that of EEMD-CCA method (p < 0.05). The running time of the sEEMD-CCA method for three lengths of semi-simulated data is shortened by more than 50%. This indicates that sEEMD-CCA is a promising tool for EMG artifact removal in real-time BCI systems.

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Drivers of Stock Prices in Ghana: An Empirical Mode Decomposition Approach

Mathematical Problems in Engineering ◽

10.1155/2021/2321042 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Emmanuel. N. Gyamfi ◽

Frederick A. A. Sarpong ◽

Anokye M. Adam

Keyword(s):

Empirical Mode Decomposition ◽

Stock Prices ◽

Low Frequency ◽

Rank Correlation ◽

Stock Index ◽

Intrinsic Mode Functions ◽

Decomposition Approach ◽

Statistical Measures ◽

Mode Decomposition ◽

Kendall Rank Correlation

This study utilized the empirical mode decomposition (EMD) technique and examined which group of investors based on their trading frequencies influence stock prices in Ghana. We applied this technique to a dataset of daily closing prices of GSE Financial Stock Index for the period 04/01/2011 to 28/08/2015. The daily closing prices were decomposed into six intrinsic mode functions (IMFs) and a residue. We used the hierarchical clustering method to reconstruct the IMFs into high frequency, low frequency, and trend components. Using statistical measures such as Pearson product moment correlation coefficient and the Kendall rank correlation, we found that the low frequency and trend components of stock prices are the main drivers of the GSE stock index. These low-frequency traders are the institutional investors. Therefore, stock prices on the GSE are affected by real economic growth but not short-lived market fluctuations.

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