Autism Diagnosis using Linear and Nonlinear Analysis of Resting-State EEG and Self-Organizing Map

The prevalence of autism has increased dramatically in recent years and many people around the world are facing this difficult condition. There is a need to develop an objective method to diagnose autism. Various analysis methods have been used to classify the EEG signals of people with autism, from linear methods in the time and frequency domain to nonlinear methods based on chaos theory. However, there is still no consensus on which method of EEG signal analysis can provide us with the best diagnostic accuracy and valid biomarkers for autism diagnosis. Therefore, in this study, we evaluate different feature extraction methods from EEG signals to diagnose autism from healthy individuals. For this purpose, EEG analysis was performed in time, time-frequency, frequency and nonlinear domains. Furthermore, the self-organizing map (SOM) method was used to classify features extracted from autistic and normal EEG. The data used in this study were recorded by the research team from 24 children with autism and 24 normal children. The accuracies of 92.31, 93.57, 95.63 and 97.10% were achieved through time and morphological, frequency, time-frequency and nonlinear analyzes, respectively. Indeed, the findings showed that nonlinear analysis could yield the best classification results (accuracy = 97.10%, sensitivity = 98.80% and specificity = 97.02%) in the EEG discrimination of autistic children from typical children through the SOM neural network.

@article{Xu2023,
  title     = {Autism Diagnosis using Linear and Nonlinear Analysis of Resting-State EEG and Self-Organizing Map},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {9},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Jie Xu and Wenxiao Yang},
  doi       = {10.14569/IJACSA.2023.01409123},
  url       = {https://doi.org/10.14569/IJACSA.2023.01409123}
}