ArticleSensors (Basel, Switzerland)2023
Energy-Efficient EEG-Based Scheme for Autism Spectrum Disorder Detection Using Wearable Sensors.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed, 33 citations in OpenAlex.
- A Novel EEG-Based Topographic Brain Map-Driven Deep Learning Method for Autism Spectrum Disorder Detection in Children.Brain topography · 2026Article
- Wearable Technologies for Clinical Assessment and Monitoring in Pediatric Neurodevelopmental Disorders : A Narrative Review.Journal of Korean Neurosurgical Society · 2026Article
- Transcranial photobiomodulation for reducing symptoms of autism spectrum disorder and modulating brain electrophysiology in children aged 2-7: an open label study.Frontiers in child and adolescent psychiatry · 2025Article
- Exploratory quantitative EEG characteristics in children with autism spectrum disorder.Frontiers in psychiatry · 2025Article
- Commercial Wearables for the Management of People with Autism Spectrum Disorder: A Review.Biosensors · 2024Review
- Trends in South Korean Medical Device Development for Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder: Narrative Review.JMIR biomedical engineering · 2024Review
- Detection of autism spectrum disorder (ASD) in children and adults using machine learning.Scientific reports · 2023Article
- From brain scans to classifiers: A systematic review of ML-based autism diagnostic frameworks.Digital healthReview
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The deployment of wearable wireless systems that collect physiological indicators to aid in diagnosing neurological disorders represents a potential solution for the new generation of e-health systems. Electroencephalography (EEG), a recording of the brain's electrical activity, is a promising physiological test for the diagnosis of autism spectrum disorders. It can identify the abnormalities of the neural system that are associated with autism spectrum disorders. However, streaming EEG samples remotely for classification can reduce the wireless sensor's lifespan and creates doubt regarding the application's feasibility. Therefore, decreasing data transmission may conserve sensor energy and extend the lifespan of wireless sensor networks. This paper suggests the development of a sensor-based scheme for early age autism detection. The proposed scheme implements an energy-efficient method for signal transformation allowing relevant feature extraction for accurate classification using machine learning algorithms. The experimental results indicate an accuracy of 96%, a sensitivity of 100%, and around 95% of F1 score for all used machine learning models. The results also show that our scheme energy consumption is 97% lower than streaming the raw EEG samples.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.