Nonlinear dynamical analysis of the neonatal EEG time series: The relationship between sleep state and complexity

Janjarasjitt, Suparerk; Scher, Mark S.; Loparo, Kenneth A.

doi:10.1016/j.clinph.2008.03.024

Cited by 65 publications

(42 citation statements)

References 31 publications

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“…The best estimate of the PMA has a √ MSE of 1.88 weeks, which is comparable to the results reported in [6,32]. In accordance with literature, we found that the neural complexity is dependent on the sleep stage [13,14]. By developing a classification model that is able to identify neonatal sleep stages, the present study contributes additional evidence suggesting that brain dynamics are different in quiet sleep compared to nonquiet sleep.…”

Section: Discussionsupporting

confidence: 80%

“…This increase in EEG complexity can be attributed to changes in the dynamics of the underlying neural networks during cortical maturation [13]. In this paper a regression model, relying solely on complexity features, is used to predict the patient's PMA.…”

Section: Discussionmentioning

confidence: 99%

“…After quantifying the multiscale entropy of each EEG channel, four features were extracted from the multiscale entropy curve: (1) the area under the multiscale curve (this will be referred to as the complexity index); (2) the average slope of the multiscale entropy curve in the small scales (scale 1-5); (3) the average slope of the curve in the large scales (scale [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]; and (4) the maximum value of the multiscale entropy curve. Thus, in total, a set of 32 (8 channels × 4) features are extracted.…”

Section: Feature Extractionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Discussionsupporting

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

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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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“…Nonlinear dynamical analysis can provide complementary information about the dynamics under physiological or psychological states compared with classical linear time series analysis methods such as Fourier or spectral analysis [9], [10]. Nonlinear dynamical analysis techniques derived from the theory of nonlinear dynamical systems such as the correlation integral, Lyapunov exponents, and correlation dimension have been recently used in a number of fields of application.…”

mentioning

confidence: 99%

Automated Detection of Driver Fatigue Based on Entropy and Complexity Measures

Zhang

Wang

2014

IEEE Trans. Intell. Transport. Syst.

192

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Abstract-This paper presents a real-time method based on various entropy and complexity measures for detection and identification of driving fatigue from recorded electroencephalogram (EEG), electromyogram, and electrooculogram signals. The complexity features were used to distinguish whether the subjects are experienced drivers by calculating the Lempel-Ziv complexity of EEG approximate entropy (ApEn). Different threshold values can be set for the two kinds of drivers individually. The entropy-based features, namely, the wavelet entropy (WE), the peak-to-peak value of ApEn (PP-ApEn), and the peak-to-peak value of sample entropy (PP-SampEn), were extracted from the collected signals to estimate the driving fatigue stages. We proposed WE in a sliding window (WES), PP-ApEn in a sliding window (PP-ApEnS), and PP-SampEn in a sliding window (PP-SampEnS) for real-time analysis of driver fatigue. The real-time features obtained by WE, PP-ApEn, and PP-SampEn with sliding window were applied to artificial neural network for training and testing the system, which gives four situations for the fatigue level of the subjects, namely, normal state, mild fatigue, mood swing, and excessive fatigue. Then, the driver fatigue level can be determined in real time. The accuracy of estimation is about 96.5%-99.5%. Receiver operating characteristic (ROC) curve was used to present the performance of the neural network classifier. The area under the ROC curve is 0.9931. The results show that the developed method is valuable for the application of avoiding some traffic accidents caused by driver's fatigue.Index Terms-Driver fatigue, electroencephalogram (EEG), electromyogram (EMG), electrooculogram (EOG), entropy, neural network.

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“…It has been reported that the CD of human EEG changes as the sleep stages change, particularly in the wake and REM stages, the cortex is more active in these two stages than in other stages 13,14 . By analysing the relationship between sleep stages and CD, Ehlers et al indicated that the CDs of the wake and REM stages are significantly higher than those of the other stages 15,16 . Rajendra et al studied the six different types of sleep signals using the CD, and the results denoted that nonlinear parameters can be used to quantify the cortical function at different sleep stages 17 .…”

Section: Introductionmentioning

confidence: 99%

Improved GP algorithm for the analysis of sleep stages based on grey model

Li¹,

Xie²,

Zhao³

et al. 2017

ScienceAsia

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Correlation dimension analysis of EEG signals is widely used to access sleep stages. However, the standard Grassberger-Procaccia (GP) algorithm used to calculate the correlation dimension is very time consuming. To overcome this problem, an algorithm that combines the grey model and GP algorithm (GM-GP) is proposed. The results show that the correlation dimensions computed from GP and GM-GP are highly correlated, and the significance between the CDs in different stages of GM-GP is similar to GP. Furthermore, the computation time of the proposed method is at most 5% of that of the GP. The proposed algorithm is suitable for the real-time monitoring of sleep stages, which can provide a deeper understanding of brain function.

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Nonlinear dynamical analysis of the neonatal EEG time series: The relationship between sleep state and complexity

Cited by 65 publications

References 31 publications

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

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

Automated Detection of Driver Fatigue Based on Entropy and Complexity Measures

Improved GP algorithm for the analysis of sleep stages based on grey model

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