Automatic quiet sleep detection based on multifractality in preterm neonates: Effects of maturation

“…These dysmaturity features can also be analysed by a variety of quantitative approaches (26,(29)(30)(31). In this study, four sets of features were derived from the EEGs to describe dysmaturity in young infants (see Supplementary Material):…”

Section: Eegmentioning

confidence: 99%

Prediction of Neurodevelopment in Infants With Tuberous Sclerosis Complex Using Early EEG Characteristics

Ridder¹,

Lavanga²,

Verhelle³

et al. 2020

Front. Neurol.

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“…The number of support vectors M that are selected in an iterative way was set equal to 1500. As we expect that the maturational effect will also play a role during sleep stage classification, the observations were divided into three groups according to their PMA: recordings before 31 weeks (N <31 = 2395), in the range from 31 to 37 weeks (N 31−37 = 10,901), and EEGs recorded beyond 37 weeks PMA (N >37 = 4304) [31]. To assure that not all training datapoints were drawn from 1 sleep state, the number of training datapoints selected from a specific sleep state was proportional to its representation in the complete dataset.…”

Section: Sleep Stage Classificationmentioning

confidence: 99%

Complexity Analysis of Neonatal EEG Using Multiscale Entropy: Applications in Brain Maturation and Sleep Stage Classification

Wel

Lavanga

Caicedo

et al. 2017

Entropy

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Automated analysis of the electroencephalographic (EEG) data for the brain monitoring of preterm infants has gained attention in the last decades. In this study, we analyze the complexity of neonatal EEG, quantified using multiscale entropy. The aim of the current work is to investigate how EEG complexity evolves during electrocortical maturation and whether complexity features can be used to classify sleep stages. First , we developed a regression model that estimates the postmenstrual age (PMA) using a combination of complexity features. Then, these features are used to build a sleep stage classifier. The analysis is performed on a database consisting of 97 EEG recordings from 26 prematurely born infants, recorded between 27 and 42 weeks PMA. The results of the regression analysis revealed a significant positive correlation between the EEG complexity and the infant's age. Moreover, the PMA of the neonate could be estimated with a root mean squared error of 1.88 weeks. The sleep stage classifier was able to discriminate quiet sleep from nonquiet sleep with an area under the curve (AUC) of 90%. These results suggest that the complexity of the brain dynamics is a highly useful index for brain maturation quantification and neonatal sleep stage classification.

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“…Serrano and Figliola[22] used the spectral width based on the WLMF method to detect epileptic seizures. Lavanga and co-workers [23] evaluated the spectral width for the detection of quiet sleep by EEG recordings from 25 healthy preterm infants. The results indicated that multifractal analysis based on wavelet leaders could identify quiet sleep stages.…”

Section: Introductionmentioning

confidence: 99%

Wavelet Leader Based Multifractal Analysis of Sleep Electroencephalogram

Li¹,

Tang²,

Liu³

et al. 2020

Preprint

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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.

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Automatic quiet sleep detection based on multifractality in preterm neonates: Effects of maturation

Cited by 8 publications

References 12 publications

Prediction of Neurodevelopment in Infants With Tuberous Sclerosis Complex Using Early EEG Characteristics

Prediction of Neurodevelopment in Infants With Tuberous Sclerosis Complex Using Early EEG Characteristics

Complexity Analysis of Neonatal EEG Using Multiscale Entropy: Applications in Brain Maturation and Sleep Stage Classification

Wavelet Leader Based Multifractal Analysis of Sleep Electroencephalogram

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