Detecting Autism Spectrum Disorder Using Spectral Analysis of Electroretinogram and Machine Learning: Preliminary results

Sultan Mohammad Manjur, Md Billal Hossain, Paul A. Constable, Dorothy A. Thompson, Fernando Marmolejo-Ramos, Irene O. Lee, David H. Skuse, Hugo F. Posada-Quintero

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Citations (Scopus)

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental condition that impacts language, communication and social interactions. The current diagnostic process for ASD is based upon a detailed multidisciplinary assessment. Currently no clinical biomarker exists to help in the diagnosis and monitoring of this condition that has a prevalence of approximately 1%. The electroretinogram (ERG), is a clinical test that records the electrical response of the retina to light. The ERG is a promising way to study different neurodevelopmental and neurodegenerative disorders, including ASD. In this study, we have proposed a machine learning based method to detect ASD from control subjects using the ERG waveform. We collected ERG signals from 47 control (CO) and 96 ASD individuals. We analyzed ERG signals both in the time and the spectral domain to gain insight into the statistically significant discriminating features between CO and ASD individuals. We evaluated the machine learning (ML) models using a subject independent cross validation-based approach. Time-domain features were able to detect ASD with a maximum 65% accuracy. The classification accuracy of our best ML model using time-domain and spectral features was 86%, with 98% sensitivity. Our preliminary results indicate that spectral analysis of ERG provides helpful information for the classification of ASD.

Original languageEnglish
Title of host publication44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society
PublisherInstitute of Electrical and Electronics Engineers
Pages3435-3438
Number of pages4
Volume2022
ISBN (Electronic)9781728127828
DOIs
Publication statusPublished - 8 Sept 2022
Event44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Biomedical Engineering transforming the provision of healthcare: promoting wellness through personalized & predictable provision at the point of care - Glasgow, United Kingdom
Duration: 11 Jul 202215 Jul 2022
Conference number: 44th
https://embc.embs.org/2022/ (Conference website)

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology Society
PublisherInstitute of Electrical and Electronics Engineers
Volume2022
ISSN (Print)2375-7477
ISSN (Electronic)2694-0604

Conference

Conference44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Abbreviated titleEMBS 2022
Country/TerritoryUnited Kingdom
CityGlasgow
Period11/07/2215/07/22
OtherThis conference is themed, “Biomedical Engineering transforming the provision of healthcare: promoting wellness through personalized & predictable provision at the point of care”. The scientific tracks will cover the standard topics of the EMBS technical committees with an additional topic, consistent with the conference theme. Alongside the scientific sessions, there will be an exhibition comprising biomedical engineering companies, publishers, SMEs, start-ups, funded biomedical research, and Biomedical Engineering programs, Institutes, and Universities. The conference will provide networking opportunities for engineers, clinicians, scientists, and entrepreneurs, as well as for students and young professionals. The conference program consists of mini-symposia, workshops, invited sessions, oral and e-poster sessions, sessions for students and young professionals, and sessions for clinicians and entrepreneurs. Glasgow and Scotland, within which it sits, have been the center of innovation for scientific, medical and technical research for centuries – for example, John Macintyre established the world’s first hospital radiology department at Glasgow Royal Infirmary in 1896, and obstetric ultrasound was launched at the Queen Mother’s Hospital, Glasgow in the late 1950s. Many more technical and medical innovations are linked to Scotland and the wider UK – the seat of many scientific discoveries over many centuries.
Internet address

Keywords

  • Autism
  • Sensitivity
  • Biological system modeling
  • Machine learning
  • Retina
  • Feature extraction
  • Time-domain analysis

Fingerprint

Dive into the research topics of 'Detecting Autism Spectrum Disorder Using Spectral Analysis of Electroretinogram and Machine Learning: Preliminary results'. Together they form a unique fingerprint.

Cite this