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Related Experiment Video

Updated: Mar 24, 2026

EEG Mu Rhythm in Typical and Atypical Development

EEG Mu Rhythm in Typical and Atypical Development

Published on: April 9, 2014

REM Sleep Abnormalities in Children With Autism Spectrum Disorder.

Jiasen Ma^1,2, Junjun Qin², Hongfang Jiang¹

¹Perioperative and Systems Medicine Laboratory, Department of Rehabilitation and Department of Anesthesiology, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Children and Adolescent's Health and Diseases, Hangzhou, China.

Autism Research : Official Journal of the International Society for Autism Research

|March 23, 2026

Summary

This summary is machine-generated.

Children with autism spectrum disorder (ASD) experience altered rapid eye movement (REM) sleep patterns, including reduced REM duration and prolonged REM latency. These REM sleep changes can help identify ASD in pediatric populations.

Keywords:

SHapley additive explanation (SHAP)autism spectrum disorder (ASD)machine learning rapid eye movement sleep (REM)

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Area of Science:

Neuroscience
Pediatric Sleep Medicine
Autism Spectrum Disorder Research

Background:

Sleep disturbances are prevalent in children diagnosed with autism spectrum disorder (ASD).
Specific alterations in rapid eye movement (REM) sleep patterns in pediatric ASD populations have not been well-defined.

Purpose of the Study:

To investigate and characterize the specific changes in REM sleep architecture in children with ASD compared to neurotypical peers.
To identify potential REM sleep biomarkers for ASD using machine learning models.

Main Methods:

Utilized polysomnography (PSG) data from 193 children with ASD and 193 matched non-ASD controls.
Analyzed REM sleep parameters including proportion, duration, latency, and first REM stage characteristics.
Employed XGBoost machine learning and SHAP analysis to identify discriminative features for ASD.

Main Results:

Children with ASD exhibited significantly reduced REM sleep proportion and duration.
Prolonged REM latency and increased first REM stage duration and proportion were observed in the ASD group.
The machine learning model effectively identified ASD based on these REM sleep abnormalities, particularly in younger children (3-8 years).

Conclusions:

REM sleep abnormalities are a common feature in young children with ASD.
Specific REM sleep metrics like reduced duration, prolonged latency, and altered first REM stage characteristics serve as potential discriminators for ASD.
Further research is warranted to explore the role of these REM sleep alterations in ASD pathogenesis and severity.