Sleep stages classification from EEG signal based on Stockwell transform

Ghasemzadeh, Peyman; Kalbkhani, Hashem; Shayesteh, Mahrokh G.

doi:10.1049/iet-spr.2018.5032

Cited by 27 publications

(14 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lee et al [34] have suggested the Pz-Oz EEG for feature extraction behaved better than the Fpz-Cz in fatigue detection, which provided approximately 69% of the total features. Ghasemzadeh et al [35] have used the Fpz-Cz and Pz-Oz EEGs for sleep staging, and their result indicates that the accuracy of the Fpz-Cz EEG is higher than the Pz-Oz. In this study, we found no significant difference in the 2 stages between the Fpz-Cz group and the Pz-Oz group by the WLMF method.…”

Section: Discussionmentioning

confidence: 99%

Wavelet Leader Based Multifractal Analysis of Sleep Electroencephalogram

Li¹,

Tang²,

Liu³

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Conventional manual sleep stage classiﬁcation is time-consuming and relies on the knowledge and experience of the specialists. The emergence of automatic sleep stage classiﬁcation greatly improves the classiﬁcation eﬃciency. The feature extraction in automatic sleep stage classiﬁcation is particularly important, which usually uses the linear methods based on techniques in the time domain, frequency domain, or time-frequency domain. Electroencephalograms (EEGs) contain a wealth of physiological information, have been widely used for the classiﬁcation of sleep stage. Due to the nonlinear, non-stationary, and multifractal characteristics of EEGs, some nonlinear methods have been used to extract features of sleep stages in recent years, such as complexity, multifractal theory, and chaos theory. The Wavelet Leader Multifractal Formalism (WLMF) of the multifractal theory is widely applied to diﬀerent physiological signals. The current researches focus on discussing the mean H¨older exponent (h0) and the width of the multifractal singularity spectrum (WD(h)) estimated by the WLMF method. However, in the ﬁeld of sleep staging, a number of researches focused on h0, but few studies on WD(h). Results: This paper aims to assess the multifractal characteristic for sleep EEG time series from the Sleep-EDF Expanded Database by the WLMF method. In the young group, the mean h0 increased from the Wake stage to the S3 stage (p<0.01). So did the elderly group (p<0.001). WD(h) of the Wake stage was less than that of the S3 stage for the young group, and this relationship was reversed for the elderly group(χ2=13.769, df=1, p<0.001). Gender did not aﬀect, with statistical signiﬁcance, WD(h) of the Wake stage and the S3 stage (χ2=0.085, df=1, p=0.608), nor did the brain region (χ2=3.137, df=1, p=0.078). Conclusions: The result shows that WD(h) was inﬂuenced by aging. The gender and location of brain regions did not show signiﬁcant inﬂuence on the multifractal characteristics of wakefulness and sleeping. This ﬁnding extends the application of the multifractal singularity spectrum on sleep staging, and raises a fundamental question on what might be the underlying mechanisms of the WD(h) reversion.

show abstract

Section: Discussionmentioning

confidence: 99%

Wavelet Leader Based Multifractal Analysis of Sleep Electroencephalogram

Li¹,

Tang²,

Liu³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Several studies on the division of EEG frequency bands have been performed [28][29][30][31][32]. Additionally to the common five sub-band division methods of delta, theta, alpha, beta and gamma, according to previous studies, the alpha band can be divided into two parts (8-10 Hz and 10-13 Hz) [28], the beta band can be divided into two parts (13-18 Hz and 18-30 Hz) [29] [30]or three parts (13-18 Hz,18-25 Hz and 18-30 Hz) [31] and the gamma band can be divided into two parts (30-40 Hz and 40-49.5 Hz) [30] or four parts (30)(31)(32)(33)(34)(35)(36)(36)(37)(38)(39)(40) Hz, 40-46 Hz and 46-49.5 Hz) [32]. In this study, according to the above frequency band division methods, different frequency partitioning schemes for EEG signals were used and 5, 7, 9 and 11 frequency sub-bands were obtained, respectively.…”

Section: Conflict Of Interestmentioning

confidence: 99%

An Automatic Classification Method of Sleep Apnea Events Based on EEG Frequency Sub-band Division

Wang

et al. 2021

Preprint

View full text Add to dashboard Cite

Sleep apnea is a kind of sleep disorder with a high prevalence rate. It is manifested as the abnormal stop of breathing during sleep and is highly dangerous to human health. The purpose of this research is to find a simple, and effective feature extraction method that can able to distinguish obstructive apnea events, central apnea events, and normal breathing events. Unlike conventional methods, the method illustrated in this study used the Infinite Impulse Response Butterworth Band pass filter to divide the Electroencephalogram (EEG) signal into 5, 7, 9 or 11 frequency sub-bands and then used the Welch method to extract the power features of these frequency sub-band signals, which were subsequently used as classifier input. Random forest, K-nearest neighbors and bagging classifiers were investigated. The results showed that in several different frequency sub-band division methods of EEG signals, the features extracted from the EEG signal that was divided into 11 frequency sub-bands were more conducive to the classification of sleep apnea events. The random forest classifier achieved the highest average accuracy, macro F1 and kappa coefficient in three types of events, which were 90.43%, 90.38% and 0.88, respectively. Compared with existing methods, the method used in the present study has higher classification performance.

show abstract

“…where t and f are, respectively, the spectral localization time and Fourier frequency, and g(t) represents the window function. The following Gaussian function (g(t)) is used in the above equation to evaluate the Stockwell Transform-based decomposition of i(t) [30].…”

Section: Stockwell Transform-based Median and Summation Indexmentioning

confidence: 99%

An Algorithm for Recognition of Fault Conditions in the Utility Grid with Renewable Energy Penetration

Yogee¹,

Mahela²,

Kansal³

et al. 2020

Energies

View full text Add to dashboard Cite

Penetration level of renewable energy (RE) in the utility grid is continuously increasing to minimize the environmental concerns, risk of energy security, and depletion of fossil fuels. The uncertain nature and availability of RE power for a short duration have created problems related to the protection, grid security, power reliability, and power quality. Further, integration of RE sources near the load centers has also pronounced the protection issues, such as false tripping, delayed tripping, etc. Hence, this paper introduces a hybrid grid protection scheme (HGPS) for the protection of the grid with RE integration. This combines the merits of the Stockwell Transform, Hilbert Transform, and Alienation Coefficient to improve performance of the protection scheme. The Stockwell Transform-based Median and Summation Index (SMSI) utilizing current signals, Hilbert Transform-based derivative index (HDI) utilizing voltage signals, and Alienation Coefficient index (ACI) utilizing voltage signals were used to compute a proposed Stockwell Transform-, Hilbert Transform-, and Alienation-based fault index (SAHFI). This SAHFI was used to recognize the fault conditions. The fault conditions were categorized using the number of faulty phases and the proposed Stockwell Transform and Hilbert Transform-based ground fault index (SHGFI) utilizing zero sequence currents. The fault conditions, such as phase and ground (PGF), any two phases (TPF), any two phases and ground (TPGF), all three phases (ATPF), and all three phases and ground (ATPGF), were recognized effectively, using the proposed SAHFI. The proposed method has the following merits: performance is least affected by the noise, it is effective in recognizing fault conditions in minimum time, and it is also effective in recognizing the fault conditions in different scenarios of the grid. Performance of the proposed approach was found to be superior compared to the discrete wavelet transform (DWT)-based method reported in the literature. The study was performed using the hybrid grid test system realized by integrating wind and solar photovoltaic (PV) plants to the IEEE-13 nodes network in MATLAB software.

show abstract

Sleep stages classification from EEG signal based on Stockwell transform

Cited by 27 publications

References 51 publications

Wavelet Leader Based Multifractal Analysis of Sleep Electroencephalogram

Wavelet Leader Based Multifractal Analysis of Sleep Electroencephalogram

An Automatic Classification Method of Sleep Apnea Events Based on EEG Frequency Sub-band Division

An Algorithm for Recognition of Fault Conditions in the Utility Grid with Renewable Energy Penetration

Contact Info

Product

Resources

About