Zhao J, Song J, Li X, Kang J. A study on EEG feature extraction and classification in autistic children based on singular spectrum analysis method.
Brain Behav 2020;
10:e01721. [PMID:
33125837 PMCID:
PMC7749618 DOI:
10.1002/brb3.1721]
[Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 04/25/2020] [Accepted: 05/08/2020] [Indexed: 12/14/2022] Open
Abstract
INTRODUCTION
The clinical diagnosis of Autism spectrum disorder (ASD) depends on rating scale evaluation, which introduces subjectivity. Thus, objective indicators of ASD are of great interest to clinicians. In this study, we sought biomarkers from resting-state electroencephalography (EEG) data that could be used to accurately distinguish children with ASD and typically developing (TD) children.
METHODS
We recorded resting-state EEG from 46 children with ASD and 63 age-matched TD children aged 3 to 5 years. We applied singular spectrum analysis (SSA) to the EEG sequences to eliminate noise components and accurately extract the alpha rhythm.
RESULTS
When we used individualized alpha peak frequency (iAPF) and individualized alpha absolute power (iABP) as features for a linear support vector machine, ASD versus TD classification accuracy was 92.7%.
CONCLUSION
This study suggested that our methods have potential to assist in clinical diagnosis.
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