Machine learning approaches for electroencephalography and magnetoencephalography analyses in autism spectrum disorder: A systematic review
- PMID: 36574922
- DOI: 10.1016/j.pnpbp.2022.110705
Machine learning approaches for electroencephalography and magnetoencephalography analyses in autism spectrum disorder: A systematic review
Abstract
There are growing application of machine learning models to study the intricacies of non-linear and non-stationary characteristics of electroencephalography (EEG) and magnetoencephalography (MEG) data in neurobiologically complex and heterogeneous conditions such as autism spectrum disorder (ASD). Such tools have potential diagnostic applications, and given the highly heterogeneous presentation of ASD, might prove fruitful in early detection and therefore could facilitate very early intervention. We conducted a systematic review (PROSPERO ID#CRD42021257438) by searching PubMed, EMBASE, and PsychINFO for machine learning approaches for EEG and MEG analyses in ASD. Thirty-nine studies were identified, of which the majority (18) used support vector machines for classification; other successful methods included deep learning. Thirty-seven studies were found to employ EEG and two were found to employ MEG. This systematic review indicate that machine learning methods can be used to classify ASD, predict ASD diagnosis in high-risk infants as early as 3 months of age, predict ASD symptom severity, and classify states of cognition in ASD with high accuracy. Replication studies testing validity, reproducibility and generalizability in tandem with randomized controlled trials in ASD populations will likely benefit the field.
Keywords: Autism; Electroencephalography; Machine learning; Magnetoencephalography; Neurophysiology; Review; Support vector machines; Systematic.
Copyright © 2022 Elsevier Inc. All rights reserved.
Conflict of interest statement
Declaration of Competing Interest No author has any conflict of interest to declare for this manuscript.
Similar articles
-
Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months.J Neurodev Disord. 2021 Nov 30;13(1):57. doi: 10.1186/s11689-021-09405-x. J Neurodev Disord. 2021. PMID: 34847887 Free PMC article.
-
Identification of autism spectrum disorder based on electroencephalography: A systematic review.Comput Biol Med. 2024 Mar;170:108075. doi: 10.1016/j.compbiomed.2024.108075. Epub 2024 Jan 29. Comput Biol Med. 2024. PMID: 38301514 Review.
-
An Improved AlexNet Model and Cepstral Coefficient-Based Classification of Autism Using EEG.Clin EEG Neurosci. 2024 Jan;55(1):43-51. doi: 10.1177/15500594231178274. Epub 2023 May 29. Clin EEG Neurosci. 2024. PMID: 37246419
-
Detecting autism from picture book narratives using deep neural utterance embeddings.Int J Lang Commun Disord. 2022 Sep;57(5):948-962. doi: 10.1111/1460-6984.12731. Epub 2022 May 12. Int J Lang Commun Disord. 2022. PMID: 35555933 Free PMC article.
-
Machine learning based on eye-tracking data to identify Autism Spectrum Disorder: A systematic review and meta-analysis.J Biomed Inform. 2023 Jan;137:104254. doi: 10.1016/j.jbi.2022.104254. Epub 2022 Dec 9. J Biomed Inform. 2023. PMID: 36509416 Review.
Cited by
-
Autism Spectrum Disorder: Brain Areas Involved, Neurobiological Mechanisms, Diagnoses and Therapies.Int J Mol Sci. 2024 Feb 19;25(4):2423. doi: 10.3390/ijms25042423. Int J Mol Sci. 2024. PMID: 38397100 Free PMC article. Review.
-
Classification of autism spectrum disorder using electroencephalography in Chinese children: a cross-sectional retrospective study.Front Neurosci. 2024 Jan 25;18:1330556. doi: 10.3389/fnins.2024.1330556. eCollection 2024. Front Neurosci. 2024. PMID: 38332856 Free PMC article.