Automated detection of neonate EEG sleep stages

Piryatinska, Alexandra; Terdik, György; Woyczyński, Wojbor A.; Loparo, Kenneth A.; Scher, Mark S.; Zlotnik, Anatoly

doi:10.1016/j.cmpb.2009.01.006

Cited by 64 publications

(60 citation statements)

References 20 publications

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“…al. [7] and available online at: http://stat.case.edu/~ayp2/EEGdat. The data used was obtained from neonates of post-conceptual age of 40 weeks from both preterm neonates (13 recordings) and fullterm neonates (14 recordings).…”

Section: A Subjectsmentioning

confidence: 99%

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Newborn sleep stage identification using multiscale entropy

Fraiwan

Lweesy

2014

2nd Middle East Conference on Biomedical Engineering

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Neonatal sleep stage identification is of great importance as it helps diagnosis of certain possible disabilities in newborns. The sleep stage identification is normally done manually for an entire sleep recording which requires great human resources; therefore a reliable automated sleep stage identification system offers a helpful tool for specialists. This study demonstrated a new method for automated sleep stage scoring in neonates. The automated approach comprises two major steps: feature extraction and classification. This study presented a new approach for feature extraction based on multiscale entropy (MSE), a recently developed method for the analysis of time series and physiological signals. The features were extracted from a single EEG recording where 13 recordings from preterm infants and 14 from full term infants were used. The classification was done using the Weka software with three different classifiers: neural networks, random forests, and classification via regression. The performance of the proposed method was found to be comparable to the methods reported in the literature. The reported accuracy was found to be 0.813 for preterm subjects and 0.864 for fullterm subjects.

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Section: A Subjectsmentioning

confidence: 99%

“…consuming procedure and a difficult task to perform for experts. Several works have been done to perform automatic sleep stage identification for both adults and neonates [5][6][7]. The automatic sleep stage identification is normally done in two steps: feature extraction and classification.…”

Section: Introductionmentioning

confidence: 99%

Newborn sleep stage identification using multiscale entropy

Fraiwan

Lweesy

2014

2nd Middle East Conference on Biomedical Engineering

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“…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%

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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“…Time series derived from motor performance and coordination (Pressing, 1999), psychophysiological variables such as heart rate and skin conductance (Brunsdon & Skinner, 1987;Dean & Bailes, 2015), continuous perceptual responses (Dean & Bailes, 2010;Schubert, 2006), or electroencephalography (EEG; Piryatinska et al, 2009), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) measures are almost always highly autocorrelated, so contravening the principles of independence upon which most common statistical tests rely. For a simple example of this, consider a computer mouse being moved to translate a pointer across a screen, perhaps in a visual search task.…”

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confidence: 99%

Dangers and uses of cross-correlation in analyzing time series in perception, performance, movement, and neuroscience: The importance of constructing transfer function autoregressive models

2015

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Many articles on perception, performance, psychophysiology, and neuroscience seek to relate pairs of time series through assessments of their cross-correlations. Most such series are individually autocorrelated: they do not comprise independent values. Given this situation, an unfounded reliance is often placed on cross-correlation as an indicator of relationships (e.g., referent vs. response, leading vs. following). Such cross-correlations can indicate spurious relationships, because of autocorrelation. Given these dangers, we here simulated how and why such spurious conclusions can arise, to provide an approach to resolving them. We show that when multiple pairs of series are aggregated in several different ways for a cross-correlation analysis, problems remain. Finally, even a genuine cross-correlation function does not answer key motivating questions, such as whether there are likely causal relationships between the series. Thus, we illustrate how to obtain a transfer function describing such relationships, informed by any genuine cross-correlations. We illustrate the confounds and the meaningful transfer functions by two concrete examples, one each in perception and performance, together with key elements of the R software code needed. The approach involves autocorrelation functions, the establishment of stationarity, prewhitening, the determination of cross-correlation functions, the assessment of Granger causality, and autoregressive model development. Autocorrelation also limits the interpretability of other measures of possible relationships between pairs of time series, such as mutual information. We emphasize that further complexity may be required as the appropriate analysis is pursued fully, and that causal intervention experiments will likely also be needed.Keywords Autocorrelation . Autoregression . Cross-correlation . Continuous responses . Time series analysis . Granger causality . Prewhitening . Transfer function modelingThe study of relationships between simultaneous time series, particularly those involving continuous human perceptions and performance, has been ongoing in many fields of psychology for several decades (e.g., Brunsdon & Skinner, 1987;Gregson, 1983;Pressing, 1999). To illustrate the ubiquity of time series data in these fields, consider the categorization provided by Pressing (1999) in his synthesis of "the referential dynamics of cognition and action". Pressing enunciates "referential behavior theory," which is a "general dynamical approach to psychological . . . systems that operate through a control or referencing process" (p. 714)-specifically, processes that operate continuously. The analytical approach to such continuous processes is most commonly discrete, in that the data are samples of the process spaced regularly in time, and this is what concerns us here. The resulting "discrete control equation" is normally an example of a vector (multivariate) autoregression time series analysis model of the paired (or several) continuous response/performance time series, w...

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Automated detection of neonate EEG sleep stages

Cited by 64 publications

References 20 publications

Newborn sleep stage identification using multiscale entropy

Newborn sleep stage identification using multiscale entropy

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

Dangers and uses of cross-correlation in analyzing time series in perception, performance, movement, and neuroscience: The importance of constructing transfer function autoregressive models

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