The Potential Application of Multiscale Entropy Analysis of Electroencephalography in Children with Neurological and Neuropsychiatric Disorders

Chu, Yen-Ju; Chang, Chi-Feng; Shieh, Jiann-Shing; Lee, Wang-Tso

doi:10.3390/e19080428

Cited by 18 publications

(16 citation statements)

References 77 publications

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“…Entropy is an optimal concept for the series randomness analysis ( Fast, 1962 ), and is becoming a powerful analysis tool for information process of the EEG activity currently ( Al-Kadi, Reaz & Ali, 2013 ; Chu et al, 2017 ). Entropy implies the degree of irregularity of a system.…”

Section: Discussionmentioning

confidence: 99%

“…In particular, SampEn applies to non-stationary data and is more noise resistant, which overcomes the drawbacks of many spectral methods that are mostly based on linear Fourier transform. Therefore, SampEn has been applied to many biomedical dynamics such as heart rate variability and EEG analysis ( Chu et al, 2017 ; Huang et al, 2013 ; Shalbaf et al, 2012 ). Previous research efforts, including ours, ignored the variability in different anaesthetic levels to some extent.…”

Section: Introductionmentioning

confidence: 99%

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Sample entropy analysis for the estimating depth of anaesthesia through human EEG signal at different levels of unconsciousness during surgeries

Liu

Fan

et al. 2018

PeerJ

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Estimating the depth of anaesthesia (DoA) in operations has always been a challenging issue due to the underlying complexity of the brain mechanisms. Electroencephalogram (EEG) signals are undoubtedly the most widely used signals for measuring DoA. In this paper, a novel EEG-based index is proposed to evaluate DoA for 24 patients receiving general anaesthesia with different levels of unconsciousness. Sample Entropy (SampEn) algorithm was utilised in order to acquire the chaotic features of the signals. After calculating the SampEn from the EEG signals, Random Forest was utilised for developing learning regression models with Bispectral index (BIS) as the target. Correlation coefficient, mean absolute error, and area under the curve (AUC) were used to verify the perioperative performance of the proposed method. Validation comparisons with typical nonstationary signal analysis methods (i.e., recurrence analysis and permutation entropy) and regression methods (i.e., neural network and support vector machine) were conducted. To further verify the accuracy and validity of the proposed methodology, the data is divided into four unconsciousness-level groups on the basis of BIS levels. Subsequently, analysis of variance (ANOVA) was applied to the corresponding index (i.e., regression output). Results indicate that the correlation coefficient improved to 0.72 ± 0.09 after filtering and to 0.90 ± 0.05 after regression from the initial values of 0.51 ± 0.17. Similarly, the final mean absolute error dramatically declined to 5.22 ± 2.12. In addition, the ultimate AUC increased to 0.98 ± 0.02, and the ANOVA analysis indicates that each of the four groups of different anaesthetic levels demonstrated significant difference from the nearest levels. Furthermore, the Random Forest output was extensively linear in relation to BIS, thus with better DoA prediction accuracy. In conclusion, the proposed method provides a concrete basis for monitoring patients’ anaesthetic level during surgeries.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sample entropy analysis for the estimating depth of anaesthesia through human EEG signal at different levels of unconsciousness during surgeries

Liu

Fan

et al. 2018

PeerJ

Self Cite

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“…Individuals with autism-a neurodevelopmental disorder marked by social impairment-show atypical neural development, particularly in frontal and temporal lobes [111]. These differences are thought to be reflected in altered brain signal entropy that occurs in autism [98,112] even before diagnostic behaviors emerge [59,99]. Differences within the oxytocinergic system are also implicated in autism, including increased OXTR methylation in both the brain and blood at site -934 [46].…”

Section: Discussionmentioning

confidence: 99%

Epigenetic tuning of brain signal entropy in emergent human social behavior

Puglia

Krol

Missana

et al. 2020

BMC Med

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Background How the brain develops accurate models of the external world and generates appropriate behavioral responses is a vital question of widespread multidisciplinary interest. It is increasingly understood that brain signal variability—posited to enhance perception, facilitate flexible cognitive representations, and improve behavioral outcomes—plays an important role in neural and cognitive development. The ability to perceive, interpret, and respond to complex and dynamic social information is particularly critical for the development of adaptive learning and behavior. Social perception relies on oxytocin-regulated neural networks that emerge early in development. Methods We tested the hypothesis that individual differences in the endogenous oxytocinergic system early in life may influence social behavioral outcomes by regulating variability in brain signaling during social perception. In study 1, 55 infants provided a saliva sample at 5 months of age for analysis of individual differences in the oxytocinergic system and underwent electroencephalography (EEG) while listening to human vocalizations at 8 months of age for the assessment of brain signal variability. Infant behavior was assessed via parental report. In study 2, 60 infants provided a saliva sample and underwent EEG while viewing faces and objects and listening to human speech and water sounds at 4 months of age. Infant behavior was assessed via parental report and eye tracking. Results We show in two independent infant samples that increased brain signal entropy during social perception is in part explained by an epigenetic modification to the oxytocin receptor gene (OXTR) and accounts for significant individual differences in social behavior in the first year of life. These results are measure-, context-, and modality-specific: entropy, not standard deviation, links OXTR methylation and infant behavior; entropy evoked during social perception specifically explains social behavior only; and only entropy evoked during social auditory perception predicts infant vocalization behavior. Conclusions Demonstrating these associations in infancy is critical for elucidating the neurobiological mechanisms accounting for individual differences in cognition and behavior relevant to neurodevelopmental disorders. Our results suggest that an epigenetic modification to the oxytocin receptor gene and brain signal entropy are useful indicators of social development and may hold potential diagnostic, therapeutic, and prognostic value.

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“…Therefore, MEMD cannot be applied to real-time situations currently. This Entropy is an optimal concept for the series randomness analysis QFast, 1962), and is becoming a powerful analysis tool for information process of the EEG activity currently QAl-Kadi et al, 2013;Chu et al, 2017). Entropy implies the degree of irregularity of a system.…”

Section: Discussionmentioning

confidence: 99%

Peer Review #1 of "Sample entropy analysis for the estimating depth of anaesthesia through human EEG signal at different levels of unconsciousness during surgeries (v0.1)"

2018

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Estimating of the depth of anaesthesia (DoA) in operations has always been a challenging issue due to the underlying complexity of the brain mechanisms. Electroencephalogram (EEG) signals are undoubtedly the most widely used signals for measuring the DoA. In this paper, a novel EEG-based index is proposed to evaluate the DoA for 24 patients receiving general anaesthesia with different levels of unconsciousness. Sample Entropy (SampEn) algorithm was in order to acquire the chaotic features of the signals. After calculating the SampEn from the EEG signals, random forest was utilised for developing learning regression models with Bispectral index (BIS) as the target. Correlation coefficient, mean absolute error, and area under the curve (AUC) were used to verify the perioperative performance of the proposed method. Validation comparisons with typical nonstationary signal analysis methods (i.e., recurrence analysis and permutation entropy) and regression methods (i.e., neural network and support vector machine) were conducted. To further verify the accuracy and validity of the proposed methodology, the data is divided into four unconsciousness-level groups on the basis of BIS levels. Subsequently, analysis of variance (ANOVA) was applied to the corresponding index (i.e., regression output). Results indicate that the correlation coefficient has improved to 0.72 ± 0.09 after filtering and to 0.90 ± 0.05 after regression from the initial values of 0.51 ± 0.17. Similarly, the final mean absolute error has dramatically declined to 5.22 ± 2.12. In addition, the ultimate AUC has increased to 0.98 ± 0.02, and the ANOVA analysis indicates that each of the four groups of different anaesthetic levels demonstrated significant difference from the nearest levels.Furthermore, the random forest output was extensively linear in relation to BIS, thus better DoA prediction accuracy. In conclusion, the proposed method provides a concrete basis for monitoring patients' anaesthetic level during surgeries.

show abstract

The Potential Application of Multiscale Entropy Analysis of Electroencephalography in Children with Neurological and Neuropsychiatric Disorders

Cited by 18 publications

References 77 publications

Sample entropy analysis for the estimating depth of anaesthesia through human EEG signal at different levels of unconsciousness during surgeries

Sample entropy analysis for the estimating depth of anaesthesia through human EEG signal at different levels of unconsciousness during surgeries

Epigenetic tuning of brain signal entropy in emergent human social behavior

Peer Review #1 of "Sample entropy analysis for the estimating depth of anaesthesia through human EEG signal at different levels of unconsciousness during surgeries (v0.1)"

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