Identification of autism spectrum disorder based on electroencephalography: A systematic review

• Published in: Computers in biology and medicine Vol. 170; p. 108075
• Main Authors: Li, Jing, Kong, Xiaoli, Sun, Linlin, Chen, Xu, Ouyang, Gaoxiang, Li, Xiaoli, Chen, Shengyong
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.03.2024; Elsevier Limited
• Subjects: Algorithms; Autism; Autism spectrum disorder; Autism Spectrum Disorder - diagnosis; Autism Spectrum Disorder - physiopathology; Behavior; Brain - physiopathology; Brain research; Child; Children; Deep leaning; Deep Learning; Diagnosis; Diagnostic systems; EEG; Electroencephalography; Electroencephalography - methods; Eye movements; Humans; Identification; Internal Medicine; Intervention; Learning algorithms; Machine Learning; Multi-model fusion; Neural networks; Neurodevelopmental disorders; Other; Signal Processing, Computer-Assisted; Stereotyped behavior; Support vector machines; Electroencephalography; Autism spectrum disorder; Multi-model fusion; Machine learning; Deep leaning
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108075

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by difficulties in social communication and repetitive and stereotyped behaviors. According to the World Health Organization, about 1 in 100 children worldwide has autism. With the global prevalence of ASD, timely and accurate diagnosis has been essential in enhancing the intervention effectiveness for ASD children. Traditional ASD diagnostic methods rely on clinical observations and behavioral assessment, with the disadvantages of time-consuming and lack of objective biological indicators. Therefore, automated diagnostic methods based on machine learning and deep learning technologies have emerged and become significant since they can achieve more objective, efficient, and accurate ASD diagnosis. Electroencephalography (EEG) is an electrophysiological monitoring method that records changes in brain spontaneous potential activity, which is of great significance for identifying ASD children. By analyzing EEG data, it is possible to detect abnormal synchronous neuronal activity of ASD children. This paper gives a comprehensive review of the EEG-based ASD identification using traditional machine learning methods and deep learning approaches, including their merits and potential pitfalls. Additionally, it highlights the challenges and the opportunities ahead in search of more effective and efficient methods to automatically diagnose autism based on EEG signals, which aims to facilitate automated ASD identification. •A review of electroencephalography-based autism identification methods are presented.•Data preprocessing, feature extraction and classification methods are summarized.•A comprehensive overview of the construction of deep learning models are provided.•Methods for autism identification using multi-modal data are summarized.•Challenges and directions in autism diagnosis are discussed.