Region-Wise Brain Response Classification of ASD Children Using EEG and BiLSTM RNN.

IF 1.6 4区医学 Q3 CLINICAL NEUROLOGY Clinical EEG and Neuroscience Pub Date : 2023-09-01 DOI:10.1177/15500594211054990

Thanga Aarthy Manoharan, Menaka Radhakrishnan

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引用次数: 6

AbstractAutism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairment in sensory modulation. These sensory modulation deficits would ultimately lead them to difficulties in adaptive behavior and intellectual functioning. The purpose of this study was to observe changes in the nervous system with responses to auditory/visual and only audio stimuli in children with autism and typically developing (TD) through electroencephalography (EEG). In this study, 20 children with ASD and 20 children with TD were considered to investigate the difference in the neural dynamics. The neural dynamics could be understood by non-linear analysis of the EEG signal. In this research to reveal the underlying nonlinear EEG dynamics, recurrence quantification analysis (RQA) is applied. RQA measures were analyzed using various parameter changes in RQA computations. In this research, the cosine distance metric was considered due to its capability of information retrieval and the other distance metrics parameters are compared for identifying the best biomarker. Each computational combination of the RQA measure and the responding channel was analyzed and discussed. To classify ASD and TD, the resulting features from RQA were fed to the designed BiLSTM (bi-long short-term memory) network. The classification accuracy was tested channel-wise for each combination. T3 and T5 channels with neighborhood selection as FAN (fixed amount of nearest neighbors) and distance metric as cosine is considered as the best-suited combination to discriminate between ASD and TD with the classification accuracy of 91.86%, respectively.

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基于脑电和BiLSTM RNN的ASD儿童脑反应区域分类。

摘要自闭症谱系障碍是一种以感觉调节功能障碍为特征的神经发育障碍。这些感觉调节缺陷最终会导致他们在适应行为和智力功能方面出现困难。本研究目的是通过脑电图(EEG)观察自闭症和典型发育(TD)儿童在听觉/视觉和仅听觉刺激下神经系统的变化。本研究以20名ASD患儿和20名TD患儿为研究对象，探讨其神经动力学的差异。通过对脑电信号的非线性分析，可以理解脑电信号的神经动力学。在本研究中，应用递归量化分析(RQA)来揭示潜在的非线性脑电动力学。利用RQA计算中的各种参数变化对RQA度量进行了分析。在本研究中，考虑余弦距离度量，因为它的信息检索能力和其他距离度量参数进行比较，以确定最佳的生物标志物。对RQA度量和响应信道的各种计算组合进行了分析和讨论。为了对ASD和TD进行分类，RQA得到的特征被输入到设计的BiLSTM(双长短期记忆)网络中。对每个组合的分类精度进行了通道测试。以邻域选择为FAN (fixed amount of nearest neighbors)，距离度量为余弦的T3和T5通道被认为是区分ASD和TD的最合适组合，分类准确率分别为91.86%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinical EEG and Neuroscience 医学-临床神经学

CiteScore

5.20

自引率

5.00%

发文量

审稿时长

>12 weeks

期刊介绍： Clinical EEG and Neuroscience conveys clinically relevant research and development in electroencephalography and neuroscience. Original articles on any aspect of clinical neurophysiology or related work in allied fields are invited for publication.