Altered electroencephalogram complexity in autistic children shown by the multiscale entropy approach

Liu, Tian; Chen, Yanni; Chen, Desheng; Li, Chenxi; Qiu, Yusheng; Wang, Jue

doi:10.1097/wnr.0000000000000724

Cited by 41 publications

(23 citation statements)

References 26 publications

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“…Decreased EEG complexity in individuals with ASD was also found during observation and imitation tasks over central, parietal, occipital, and right temporal areas. These areas were compatible with the areas activated during imitation tasks in fMRI studies and may indicating a deficit in the "mirror neuron system" in ASD [16,56]. Treatments of ASD-related symptoms may also affect EEG complexity.…”

Section: Autism Spectrum Disordersupporting

confidence: 65%

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

Chu

Chang

Shieh

et al. 2017

Entropy

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Electroencephalography (EEG) is frequently used in functional neurological assessment of children with neurological and neuropsychiatric disorders. Multiscale entropy (MSE) can reveal complexity in both short and long time scales and is more feasible in the analysis of EEG. Entropy-based estimation of EEG complexity is a powerful tool in investigating the underlying disturbances of neural networks of the brain. Most neurological and neuropsychiatric disorders in childhood affect the early stage of brain development. The analysis of EEG complexity may show the influences of different neurological and neuropsychiatric disorders on different regions of the brain during development. This article aims to give a brief summary of current concepts of MSE analysis in pediatric neurological and neuropsychiatric disorders. Studies utilizing MSE or its modifications for investigating neurological and neuropsychiatric disorders in children were reviewed. Abnormal EEG complexity was shown in a variety of childhood neurological and neuropsychiatric diseases, including autism, attention deficit/hyperactivity disorder, Tourette syndrome, and epilepsy in infancy and childhood. MSE has been shown to be a powerful method for analyzing the non-linear anomaly of EEG in childhood neurological diseases. Further studies are needed to show its clinical implications on diagnosis, treatment, and outcome prediction.

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Section: Autism Spectrum Disordersupporting

confidence: 65%

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

Chu

Chang

Shieh

et al. 2017

Entropy

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“…Multi-domain features are captured to highlight the characteristics of EEG signals, e.g., information entropy, timefrequency analysis, and synchronization analysis. Specifically, information entropy features are first obtained by measuring the complexity of the original EEG signal individually along each channel, e.g., approximate entropy (ApprEn, Meedeniya et al, 2019), sample entropy (SampEn, Liu et al, 2017), and permutation entropy (PermEn, Kang et al, 2019). Then, the original EEG is processed by short-time Fourier transforms (STFT) to capture the time-frequency representation, e.g., entropy feature.…”

Section: Feature Extractionmentioning

confidence: 99%

Children ASD Evaluation Through Joint Analysis of EEG and Eye-Tracking Recordings With Graph Convolution Network

Zhang

Chen

Tang

et al. 2021

Front. Hum. Neurosci.

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Recent advances in neuroscience indicate that analysis of bio-signals such as rest state electroencephalogram (EEG) and eye-tracking data can provide more reliable evaluation of children autism spectrum disorder (ASD) than traditional methods of behavior measurement relying on scales do. However, the effectiveness of the new approaches still lags behind the increasing requirement in clinical or educational practices as the “bio-marker” information carried by the bio-signal of a single-modality is likely insufficient or distorted. This study proposes an approach to joint analysis of EEG and eye-tracking for children ASD evaluation. The approach focuses on deep fusion of the features in two modalities as no explicit correlations between the original bio-signals are available, which also limits the performance of existing methods along this direction. First, the synchronization measures, information entropy, and time-frequency features of the multi-channel EEG are derived. Then a random forest applies to the eye-tracking recordings of the same subjects to single out the most significant features. A graph convolutional network (GCN) model then naturally fuses the two group of features to differentiate the children with ASD from the typically developed (TD) subjects. Experiments have been carried out on the two types of the bio-signals collected from 42 children (21 ASD and 21 TD subjects, 3–6 years old). The results indicate that (1) the proposed approach can achieve an accuracy of 95% in ASD detection, and (2) strong correlations exist between the two bio-signals collected even asynchronously, in particular the EEG synchronization against the face related/joint attentions in terms of covariance.

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“…The primary disruption is likely to be in these systems themselves, and the measured action kinematics a manifestation of those neural disruptions. Therefore, kinematic studies that address fundamental motor control theory, such as feed-forward and feed-back systems in motor control (Nazarali et al, 2009), prospective motor control such as shown for infant movement (Delafield-Butt et al, 2018;Gottwald et al, 2016;Lee, 2009), predictive coding in motor systems (Gonzalez-Gadea et al, 2015;Van de Cruys et al, 2014) or entropy analyses (Liu et al, 2017) will begin to afford insight into disruptions of those neural systems in autism. The advantage of the methodology we employ here is rapid, nonintrusive collection of precise laboratory-grade kinematic data within an ecologically valid, fun, and attractive serious game format.…”

Section: Discussionmentioning

confidence: 99%

Swipe kinematic differences in young children with autism spectrum disorders are task- and age-dependent: A smart tablet game approach

Lu¹,

Rowe²,

Tachtatzis³

et al. 2020

Preprint

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The motor system is becoming increasingly recognized as an important site of disruptions in autism spectrum disorders (ASD). However, the precise nature of this motor disruption remains unclear with some conflicted reports between papers. A bottleneck in kinematic studies of children with ASD has been the use of laboratory-based motion tracking systems and experimental paradigms that require didactic instructions. Thus, we employed an attractive smart tablet gameplay methodology to engage children’s interest without complicated verbal instruction. Children’s movements on the touch screen were recorded as they engaged in gameplay of their own volition, enabling improved ecological validity in data capture. Swipe kinematics were computed from two games that afforded goal-directed and free-style scribbling, respectively. 82 children aged 2-6 years were tested, including 37 children with ASD and 45 typically developing (TD) children. Kinematic analyses revealed significant age, group, and task differences. In comparison to aged-matched TD children, children with ASD under 5 years old performed faster goal-directed movements, whereas those over 5 years old performed slower goal-directed movements. In contrast, children with ASD moved faster than TD children irrespective of age during creative scribbling. Interestingly, movement speed increased with age in TD children in both game contexts, likely due to motor skill and confidence development, but this was not true for children with ASD, who moved slower in the case of goal-directed swipes or with no difference in the case of creative scribbling. These findings support emerging evidence for differences in maturation of visuomotor integration in preschool children with ASD.

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Altered electroencephalogram complexity in autistic children shown by the multiscale entropy approach

Cited by 41 publications

References 26 publications

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

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

Children ASD Evaluation Through Joint Analysis of EEG and Eye-Tracking Recordings With Graph Convolution Network

Swipe kinematic differences in young children with autism spectrum disorders are task- and age-dependent: A smart tablet game approach

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