Fast and Adaptive Empirical Mode Decomposition for Multidimensional, Multivariate Signals

Thirumalaisamy, Mruthun R.; Ansell, Phillip J.

doi:10.1109/lsp.2018.2867335

Cited by 44 publications

(13 citation statements)

References 18 publications

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“…The factor m is the total number of channels. In MFAEMD, the performance of the projection of a multi-channel signal

is based on the weighted sum of all channel signals [ 30 ]. For taking the projection, a point set for sampling on the

-dimensional unit sphere is considered [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

“…The

-dimensional unit sphere contains the set of points (

, …,

) which satisfy the condition of

in the Euclidean space. The vector representation of this point on

-dimension or channel unit sphere is given as

, where

………

are the unit vectors of different channels [ 30 ]. In this study, we have considered the value of points such as (

, …,

) as the direction cosines for all channels and they are given by

.…”

Section: Methodsmentioning

confidence: 99%

“…In this study, we have considered the value of points such as (

, …,

) as the direction cosines for all channels and they are given by

. This unit vector used in this work is given as follows [ 29 , 30 ]:

Similarly, the parameter N corresponds to the number of samples present in each channel of multi-channel EEG signal. The projected EEG signal is computed as follows:

where

, …,

are the EEG signals for different channels.…”

Section: Methodsmentioning

confidence: 99%

“…The projected EEG signal evaluated from the multi-channel EEG is depicted in Figure 2 e. The detected frequency points for the design of the MPFBEWT filter bank in the spectrum of the projected EEG signal are shown in Figure 2 e. The MPFBEWT filter bank was computed using empirical scaling and wavelet functions that are depicted in Figure 2 f. The purpose of considering the spectrum of the projected EEG signal for deriving an empirical wavelet filter bank is given as follows. In multi-channel signal decomposition approaches like MEMD and MFAEMD, the composite signal is evaluated at the initial step by considering the information of all channel signals [ 29 , 30 ]. The mean envelope is computed from the composite signal using maxima–minima detection and the evaluation of upper and lower envelopes [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

“…The modes are evaluated using the designed wavelet filters and DFT of each channel EEG signal. The projection-based approach has been used in multivariate EMD (MEMD) and multivariate Fast and adaptive EMD (MFAEMD) to evaluate the composite signal from the multi-channel signal [ 29 , 30 ]. The advantage of the projection-based approach is that all channels are used to generate the composite signal.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Development of Automated Sleep Stage Classification System Using Multivariate Projection-Based Fixed Boundary Empirical Wavelet Transform and Entropy Features Extracted from Multichannel EEG Signals

Tripathy

Ghosh

Gajbhiye

et al. 2020

Entropy

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The categorization of sleep stages helps to diagnose different sleep-related ailments. In this paper, an entropy-based information–theoretic approach is introduced for the automated categorization of sleep stages using multi-channel electroencephalogram (EEG) signals. This approach comprises of three stages. First, the decomposition of multi-channel EEG signals into sub-band signals or modes is performed using a novel multivariate projection-based fixed boundary empirical wavelet transform (MPFBEWT) filter bank. Second, entropy features such as bubble and dispersion entropies are computed from the modes of multi-channel EEG signals. Third, a hybrid learning classifier based on class-specific residuals using sparse representation and distances from nearest neighbors is used to categorize sleep stages automatically using entropy-based features computed from MPFBEWT domain modes of multi-channel EEG signals. The proposed approach is evaluated using the multi-channel EEG signals obtained from the cyclic alternating pattern (CAP) sleep database. Our results reveal that the proposed sleep staging approach has obtained accuracies of 91.77%, 88.14%, 80.13%, and 73.88% for the automated categorization of wake vs. sleep, wake vs. rapid eye movement (REM) vs. Non-REM, wake vs. light sleep vs. deep sleep vs. REM sleep, and wake vs. S1-sleep vs. S2-sleep vs. S3-sleep vs. REM sleep schemes, respectively. The developed method has obtained the highest overall accuracy compared to the state-of-art approaches and is ready to be tested with more subjects before clinical application.

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“…The factor m is the total number of channels. In MFAEMD, the performance of the projection of a multi-channel signal

is based on the weighted sum of all channel signals [ 30 ]. For taking the projection, a point set for sampling on the

-dimensional unit sphere is considered [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

“…The

-dimensional unit sphere contains the set of points (

, …,

) which satisfy the condition of

in the Euclidean space. The vector representation of this point on

-dimension or channel unit sphere is given as

, where

………

are the unit vectors of different channels [ 30 ]. In this study, we have considered the value of points such as (

, …,

) as the direction cosines for all channels and they are given by

.…”

Section: Methodsmentioning

confidence: 99%

“…In this study, we have considered the value of points such as (

, …,

) as the direction cosines for all channels and they are given by

. This unit vector used in this work is given as follows [ 29 , 30 ]:

Similarly, the parameter N corresponds to the number of samples present in each channel of multi-channel EEG signal. The projected EEG signal is computed as follows:

where

, …,

are the EEG signals for different channels.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Development of Automated Sleep Stage Classification System Using Multivariate Projection-Based Fixed Boundary Empirical Wavelet Transform and Entropy Features Extracted from Multichannel EEG Signals

Tripathy

Ghosh

Gajbhiye

et al. 2020

Entropy

View full text Add to dashboard Cite

show abstract

Seismic structural health monitoring using the modal assurance distribution

Quqa

Landi

Diotallevi

2021

Earthq Engng Struct Dyn

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Thanks to emerging techniques in the field of signal processing and due to improvements in smart sensing systems which enable the event‐triggered acquisition of high‐fidelity data at the occurrence of strong ground motion events, seismic structural health monitoring has grown considerably in the last few decades. In this paper, the modal assurance distribution, an alternative time‐frequency representation of the modal features of multivariate and multicomponent signals, is extended for application to short‐term nonstationary vibrational structural responses in which the system may manifest its nonlinear behavior. A general procedure for the extraction of the decoupled normal modes is presented, which allows the identification of instantaneous modal parameters in order to investigate in detail the structural behavior during earthquakes. Valuable information that cannot be recovered by means of traditional criteria can thus be exploited for accurate damage assessment. The results obtained for two case studies consisting of a numerical model with softening nonlinear behavior and a full‐scale experimental reinforced concrete benchmark show the potential and applicability of the method proposed for the integrity assessment of civil structures.

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Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works

Moridian

Shoeibi

Khodatars

et al. 2022

WIREs Data Min & Knowl

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Apnea is a sleep disorder that stops or reduces airflow for a short time during sleep. Sleep apnea may last for a few seconds and happen for many while sleeping. This reduction in breathing is associated with loud snoring, which may awaken the person with a feeling of suffocation. So far, a variety of methods have been introduced by researchers to diagnose sleep apnea, among which the polysomnography (PSG) method is known to be the best. Analysis of PSG signals is very complicated. Many studies have been conducted on the automatic diagnosis of sleep apnea from biological signals using artificial intelligence (AI), including machine learning (ML) and deep learning (DL) methods. This research reviews and investigates the studies on the diagnosis of sleep apnea using AI methods. First, computer aided diagnosis system (CADS) for sleep apnea using ML and DL techniques along with its parts including dataset, preprocessing, and ML and DL methods are introduced. This research also summarizes the important specifications of the studies on the diagnosis of sleep apnea using ML and DL methods in a table. In the following, a comprehensive discussion is made on the studies carried out in this field. The challenges in the diagnosis of sleep apnea using AI methods are of paramount importance for researchers. Accordingly, these obstacles are elaborately addressed. In another section, the most important future works for studies on sleep apnea detection from PSG signals and AI techniques are presented. Ultimately, the essential findings of this study are provided in the conclusion section. This article is categorized under: Technologies > Artificial Intelligence Application Areas > Data Mining Software Tools Algorithmic Development > Biological Data Mining

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Fast and Adaptive Empirical Mode Decomposition for Multidimensional, Multivariate Signals

Cited by 44 publications

References 18 publications

Development of Automated Sleep Stage Classification System Using Multivariate Projection-Based Fixed Boundary Empirical Wavelet Transform and Entropy Features Extracted from Multichannel EEG Signals

Development of Automated Sleep Stage Classification System Using Multivariate Projection-Based Fixed Boundary Empirical Wavelet Transform and Entropy Features Extracted from Multichannel EEG Signals

Seismic structural health monitoring using the modal assurance distribution

Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works

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