EEG entropy analysis in autistic children

Kang, Jiannan; Chen, Huimin; Li, Xin; Li, Xiaoli

doi:10.1016/j.jocn.2018.11.027

Cited by 73 publications

(42 citation statements)

References 34 publications

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“…Sample entropy as a feature of the response EEG signals complexity has been widely used in EEG analysis [36,37]. Through analyzing the SampEn of the EEG signals found that the correlation coefficient between SampEn of the EEG signals and BIS value is as high as 0.8.…”

Section: Results Analyzingmentioning

confidence: 99%

Monitoring the depth of anesthesia using Autoregressive model and Sample entropy

Luo

et al. 2019

Preprint

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Anesthesia is an important part in modern surgery, and the way how to effectively monitor the depth of anesthesia (DOA) is core issue in the anesthesia work. Since anesthetics mainly affected the brain of patients, it is very effective to monitor DOA by electroencephalogram (EEG). This paper proposes a method for monitoring DOA using EEG. First, the sample entropy (SampEn) of EEG were calculated as a feature vector. Simultaneously, the Burg recursive algorithm was used to solve the autoregressive model (AR model) and AR coefficients were extracted as feature vectors. Later, according to the characteristics of uneven distribution of sample points, the weighted k-nearest neighbor (WKNN) classifier was selected. The Anesthesia was divided into awake, mild, moderate and deep by WKNN classifier. According to the results, the correlation coefficient between the SampEn of the EEG and Bispectral Index (BIS) is above 0.8. There is a correlation between the first five orders of AR coefficient and the BIS index, and the correlation of the second order reaches 0.8. Through the validation of 30 patients, this method can assessment of DOA effectively and accurately.

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Section: Results Analyzingmentioning

confidence: 99%

Monitoring the depth of anesthesia using Autoregressive model and Sample entropy

Luo

et al. 2019

Preprint

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“…We would like to note that DR can be considered as a measure of dynamic variability of neural activity (specifically focused on the synchronized dynamics). Other measures of dynamics variability of neural activity (methods based on the different types of entropy of the time-series) have been applied to EEG signals yielding differences between ASD and TD subjects (Kang et al, 2019). In that respect, DR is focused on the dynamical aspects of variability of specific (oscillatory) mechanisms of neural activity, not just general differences in complexity.…”

Section: Methodsmentioning

confidence: 99%

“…EEG data also point to changes in brain network organization developmentally: e.g. between 3 and 11 years of age decreased synchronization among brain regions persists, but in addition to it, increases in within-regional synchronization are observed (Han et al, 2017;Kang et al, 2019). The power of the signal in ASD participants appears to be enhanced in both low-and high-frequency bands (what Wang et al, 2013 called a U-shaped function), particularly in the left hemisphere.…”

Section: Introductionmentioning

confidence: 99%

Dysregulation of temporal dynamics of synchronous neural activity in adolescents on autism spectrum

2019

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Autism spectrum disorder is increasingly understood to be based on atypical signal transfer among multiple interconnected networks in the brain. Relative temporal patterns of neural activity have been shown to underlie both the altered neurophysiology and the altered behaviors in a variety of neurogenic disorders. We assessed brain network dynamics variability in Autism Spectrum Disorders (ASD) using measures of synchronization (phase-locking) strength, and timing of synchronization and desynchronization of neural activity (desynchronization ratio) across frequency bands of resting state EEG. Our analysis indicated that fronto-parietal synchronization is higher in ASD, but with more short periods of desynchronization. It also indicates that the relationship between the properties of neural synchronization and behavior is different in ASD and typically developing populations. Recent theoretical studies suggest that neural networks with high desynchronization ratio have increased sensitivity to inputs. Our results point to the potential significance of this phenomenon to autistic brain. This sensitivity may disrupt production of an appropriate neural and behavioral responses to external stimuli. Cognitive processes dependent on integration of activity from multiple networks may be, as a result, particularly vulnerable to disruption. Lay Summary Parts of the brain can work together by synchronizing activity of the neurons. We recorded electrical activity of the brain in adolescents with autism spectrum disorder, and then compared the recording to that of their peers without the diagnosis. We found that in participants with autism, there were a lot of very short time periods of non-synchronized activity between frontal and parietal parts of the brain. Mathematical models show that the brain system with this kind of activity is very sensitive to external events.

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“…Social interaction and communication impairments lead to autism spectral disorder (ASD). Typical development (TD) and ASD to be differentiated 50 for which region specifically and entropy specifically central and front regions are studied where as Renyi wavelet entropy are calculated for frontal and occipital regions.…”

Section: Related Workmentioning

confidence: 99%

Artifact cleaning of motor imagery EEG by statistical features extraction using wavelet families

Nagabushanam

George

Dolly

et al. 2020

Circuit Theory & Apps

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Electroencephalogram (EEG) and its sub-bands represent electrical pattern of human brain. EEG signal contains transient components, spikes, and different types of artifacts due to eye blinking, movement of the person, anxiety, and so forth, during EEG capture. Wavelet transforms are powerful mathematical tool for sampling approximation to get clean EEG. It also helps in filtering, sampling, interpolation, noise reduction, signal approximation and signal enhancement, and feature extraction. In this paper, we have analyzed artifact cleaning via PSD graphs and statistical features extracted from motor imagery EEG-like standard deviation variance. For this, we considered 19 channels EEG signal and applied orthogonal Daubechies wavelet, bi-orthogonal rbio wavelet and Coifman wavelets to check the better performance of different wavelets. Coifman wavelet uses both scaling function and vanishing moments for sampling approximation and hence give smooth sampling compared to rbio and Daubechies wavelet transforms. Coif is a compactly supported wavelet system which also helps in smooth sampling approximations than other wavelets in the state of arts. The detailed coefficients and approximate coefficients can be further used for extracting features from EEG and classification purposes. Artifacts cleaning is thus observed better in coif wavelet analysis compared to other wavelets from the power distributions as power spectral density (PSD) graphs, standard deviation and variance obtained. Matlab R2013b is used for filtering and sampling EEG. Python 2.7 is used for statistical features extraction.

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EEG entropy analysis in autistic children

Cited by 73 publications

References 34 publications

Monitoring the depth of anesthesia using Autoregressive model and Sample entropy

Monitoring the depth of anesthesia using Autoregressive model and Sample entropy

Dysregulation of temporal dynamics of synchronous neural activity in adolescents on autism spectrum

Artifact cleaning of motor imagery EEG by statistical features extraction using wavelet families

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