Diagnosis of autism spectrum disorder based on complex network features

Comput Methods Programs Biomed. 2019 Aug;177:277-283. doi: 10.1016/j.cmpb.2019.06.006. Epub 2019 Jun 8.


Background and objectives: 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.

Methods: 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.

Results: With an accuracy 81/67%, the ASD class can be discerned.

Conclusions: 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 RADACC and RADMPL) can ASD and NC target classes be distinguished at an early age.

Keywords: ASD; Average degree; Complex networks; EEG; KNN classification; Visibility graph.

MeSH terms

  • Algorithms
  • Autism Spectrum Disorder / diagnosis*
  • Brain / diagnostic imaging*
  • Child
  • Child, Preschool
  • Cluster Analysis
  • Diagnosis, Computer-Assisted / methods*
  • Electroencephalography*
  • Female
  • Humans
  • Male
  • Reproducibility of Results
  • Signal Processing, Computer-Assisted