Center for Computational Neuroscience Research, Department of Biomedical Engineering, Imam Reza International University, Mashhad, Razavi Khorasan, Iran.
Software Department, Computer Engineering Faculty, Imam Reza International University, Mashhad, Iran.
Comput Methods Programs Biomed. 2019 Aug;177:277-283. doi: 10.1016/j.cmpb.2019.06.006. Epub 2019 Jun 8.
Autism spectrum disorder (ASD) is a disorder in the information flow of the human brain system that can lead to secondary problems for the patient. Only when ASD is diagnosed by clinical methods can the secondary problems be detected. Hence, diagnosis of ASD at an early age and in young children can prevent its secondary effects.
By employing the visibility graph (VG) algorithm, the present study examines the C3 single-channel of EEG signals and presents the differences among the topological features of complex networks resulting from these signals. The average degree (AD) can be a method for the detection of normal and ASD samples.
With an accuracy 81/67%, the ASD class can be discerned.
The current paper demonstrates that only by the usage of EEG signals of the brain's C3 channel and the topological features of its network (AD and related features, such as R and R can ASD and NC target classes be distinguished at an early age.
自闭症谱系障碍（ASD）是一种人类大脑系统信息流的紊乱，可能导致患者出现继发性问题。只有通过临床方法诊断 ASD，才能发现这些继发性问题。因此，早期诊断 ASD 并对幼儿进行诊断，可以预防其继发性影响。
本研究采用可视性图（VG）算法，对 EEG 信号的 C3 单通道进行分析，并呈现出源自这些信号的复杂网络拓扑特征的差异。平均度（AD）可以作为一种检测正常和 ASD 样本的方法。
ASD 组的准确率为 81/67%。
本研究表明，仅通过大脑 C3 通道的 EEG 信号以及网络的拓扑特征（AD 及其相关特征，如 R 和 R），即可在早期区分 ASD 和 NC 目标类别。
