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Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths

Authors:
Sultan Mohammad Manjur
Sultan Mohammad Manjur
Sultan Mohammad Manjur
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Luis Roberto Mercado Diaz at University of Connecticut
Luis Roberto Mercado Diaz
Irene OLS Lee at University College London
Irene OLS Lee
David Skuse at University College London
David Skuse
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Abstract and Figures

Purpose Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are conditions that similarly alter cognitive functioning ability and challenge the social interaction, attention, and communication skills of affected individuals. Yet these are distinct neurological conditions that can exhibit diverse characteristics which require different management strategies. It is desirable to develop tools to assist with early distinction so that appropriate early interventions and support may be tailored to an individual’s specific requirements. The current diagnostic procedures for ASD and ADHD require a multidisciplinary approach and can be lengthy. This study investigated the potential of electroretinogram (ERG), an eye test measuring retinal responses to light, for rapid screening of ASD and ADHD. Methods: Previous studies identified differences in ERG amplitude between ASD and ADHD, but this study explored time-frequency analysis (TFS) to capture dynamic changes in the signal. ERG data from 286 subjects (146 control, 94 ASD, 46 ADHD) was analyzed using two TFS techniques. Results: Key features were selected, and machine learning models were trained to classify individuals based on their ERG response. The best model achieved 70% overall accuracy in distinguishing control, ASD, and ADHD groups. Conclusion: The ERG to the stronger flash strength provided better separation and the high frequency dynamics (80–300 Hz) were more informative features than lower frequency components. To further improve classification a greater number of different flash strengths may be required along with a discrimination comparison to participants who meet both ASD and ADHD classifications and carry both diagnoses.
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ORIGINAL ARTICLE
Journal of Autism and Developmental Disorders (2025) 55:1365–1378
https://doi.org/10.1007/s10803-024-06290-w
Hugo F. Posada-Quintero
hugo.posada-quintero@uconn.edu
1 Department of Biomedical Engineering, University of
Connecticut, 06269 Storrs, CT, USA
2 Behavioral and Brain Sciences Unit, Population Policy and
Practice Program, UCL Great Ormond Street Institute of
Child Health, University College London, London, UK
3 Tony Kriss Visual Electrophysiology Unit, Clinical and
Academic Department of Ophthalmology, Great Ormond
Street Hospital for Children NHS Foundation Trust, London,
UK
4 UCL Great Ormond Street Institute for Child Health,
University College London, London, UK
5 Centre for Change and Complexity in Learning, University
of South Australia, Adelaide, Australia
6 College of Nursing and Health Sciences, Flinders University,
Caring Futures Institute, Adelaide, Australia
Abstract
Purpose Autism spectrum disorder (ASD) and attention decit hyperactivity disorder (ADHD) are conditions that simi-
larly alter cognitive functioning ability and challenge the social interaction, attention, and communication skills of aected
individuals. Yet these are distinct neurological conditions that can exhibit diverse characteristics which require dierent
management strategies. It is desirable to develop tools to assist with early distinction so that appropriate early interventions
and support may be tailored to an individual’s specic requirements. The current diagnostic procedures for ASD and ADHD
require a multidisciplinary approach and can be lengthy. This study investigated the potential of electroretinogram (ERG),
an eye test measuring retinal responses to light, for rapid screening of ASD and ADHD. Methods: Previous studies identied
dierences in ERG amplitude between ASD and ADHD, but this study explored time-frequency analysis (TFS) to capture
dynamic changes in the signal. ERG data from 286 subjects (146 control, 94 ASD, 46 ADHD) was analyzed using two TFS
techniques. Results: Key features were selected, and machine learning models were trained to classify individuals based
on their ERG response. The best model achieved 70% overall accuracy in distinguishing control, ASD, and ADHD groups.
Conclusion: The ERG to the stronger ash strength provided better separation and the high frequency dynamics (80–300 Hz)
were more informative features than lower frequency components. To further improve classication a greater number of
dierent ash strengths may be required along with a discrimination comparison to participants who meet both ASD and
ADHD classications and carry both diagnoses.
Keywords Electroretinogram · Autism spectrum disorder · Attention decit hyperactivity disorder · Time-frequency
analysis · Machine learning
Accepted: 13 February 2024 / Published online: 23 February 2024
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024
Detecting Autism Spectrum Disorder and Attention Decit
Hyperactivity Disorder Using Multimodal Time-Frequency Analysis
with Machine Learning Using the Electroretinogram from Two Flash
Strengths
Sultan MohammadManjur1· Luis Roberto MercadoDiaz1· Irene OLee2· David HSkuse2· Dorothy A.Thompson3,4·
FernandoMarmolejos-Ramos5· Paul A.Constable6· Hugo F.Posada-Quintero1
1 3
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... However, relying on these markers limits the number of features that ML models can use to improve classification between groups. Therefore, signal analysis in the time-frequency domain is utilized to decompose the signal into multiple frequency-based sub-bands [16,[24][25][26], which expands the number of features available for ML models. The time-frequency analysis of the ERG waveform was developed by Gauvin and colleagues, who decomposed the ERG signal using a discrete wavelet transform (DWT) with a Haar mother wavelet [27][28][29][30]. ...
... The time-frequency analysis of the ERG waveform was developed by Gauvin and colleagues, who decomposed the ERG signal using a discrete wavelet transform (DWT) with a Haar mother wavelet [27][28][29][30]. An alternative signal analytical approach using variable-frequency complex demodulation (VFCDM) [31] was applied to the ERG waveforms and compared to DWT with the introduction of ML for group classification, with the model achieving a sensitivity of 0.85 and specificity of 0.78 [24,26]. ...
... Three analyses of the ERG waveform 'signal', in both the time and time-frequency domains, were conducted with the aim of identifying the best set of features to differentiate the control, ASD, ADHD, and the ASD + ADHD groups. Further details of the signal analysis methods are provided in Appendix B (following the discussion section) and were previously described in detail using discrete wavelet transform (DWT) [16,[27][28][29][30] and variable-frequency complex demodulation (VFCDM) [24,25,31,45]. ...
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... Still, these early findings require more extensive studies to replicate in younger clinical populations. Whilst developments in this field continue with the use of signal analysis of the ERG using variable frequency complex demodulation [24] showing potential to not only classify ASD but also to differentiate between ASD and ADHD [25], [26]. Other methods using aspects of Functional Data Analysis of the b-wave have also recently been reported [27] that may provide additional features that may contribute to the classification of retinal disorders [28]. ...
... Manjur et al. [25] applied variable frequency complex demodulation and ML for ASD and ADHD classification, achieving 0.84 accuracy with gradient boosting. and more recently achieved a 70% overall accuracy for differentiating between ASD, ADHD and controsl [26]. Further studies may explore sensitivity and specificity with controls meeting both ASD and ADHD classifications [25]. ...
... However, one limitation in such studies is the heterogeneity of the ASD population despite standardization of clinical assessments such as the Autism Diagnostic Observational schedule (ADOS) [43]. Heterogeneity in ASD may manifest in the severity and co-occurrence of additional conditions [29] Obesity diseases 47 / Not Specified ERG DA and LA Artificial Neural Network (ANN) Zhdanov, 2022 [31] Retinal Dystrophy Not Specified / 425 ERG DA and LA Decision Tree (DT) Glinton, 2022 [32] ABCA4-related retinopathy 597 / Not Specified ERG DA and LA SVM Gajendran, 2022 [33] Early-Stage Glaucoma 60 / 540 ERG DA and LA Feature Engineering, RFC * Manjur, 2022 [34] Autism Spectrum Disorder (ASD) 143 / Not Specified ERG LA RFC, GB ** , DT, SVM Kulyabin, 2023 [35] Retinal Dystrophy Not Specified / 542 ERG DA and LA VGG, ResNet, DensNet, ResNext, ViT Posada-Quintero,2023 [24] ASD 217 / Not Specified ERG LA RFC, GB, SVM Manjur, 2023 [25] ASD 143 / Not Specified ERG LA GradBoost, XGBoost Manjur, 2024 [26] ASD, ADHD *** 286 / Not Specified ERG LA RFC, XGBoost * Random Forest Classifier ** Gradient Boosting *** Attention Deficit Hyperactivity Disorder such as ADHD with phenotypic overlap [44]. ML approaches may help to classify neurodevelopmental disorders based on a combination of phenotypic and biological markers [45], [46]. ...
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... However, test conditions such as the electrode position and type and subject parameters such as sex assigned at birth, age, and iris color may also infuence the reference ERG parameters [6][7][8]. Having the ability to increase representative waveforms based on exemplars may help bolster the sample size of not only normative but also of case series in challenging to recruit or under-represented populations in clinical research, such as autism spectrum disorder (ASD) [9][10][11], rare inherited retinal dystrophies (IRDs) [12], Parkinson's disease [13], glaucoma [14] and attention-defcit/hyperactivity disorder (ADHD) [15]. ...
... Additional signal analytical methods using variable frequency complex demodulation (VFCDM) employs a series of bandpass flters to give a greater time-frequency resolution than DWT, but at the expense of detailed information concerning the cellular origins of the extracted signals [23]. Nonetheless, VFCDM has been applied to the ERG for the classifcation of ASD and ADHD based on the LA-ERGs [15,[23][24][25]. In addition to these analytical methods, other developments based on functional data analysis that identify features of the waveform shape to classify groups have also been described for the ascending portion of the b-wave [26]. ...
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... Typically, the clinical ERG is used to support the diagnosis of inherited or acquired retinal disease using time-domain parameters based on the amplitude and timing of the waveform peaks [31]. However, in conditions where subtle changes in the ERG may not be apparent through analysis of timedomain parameters, such as in heterogeneous conditions, that may co-occur, such as autism spectrum disorder (ASD) [37] and attention deficit hyperactivity disorder (ADHD) [2] then alternative approaches may be required using signal analysis of the ERG to identify features for better classification [9,26]. One way of addressing the difficulty of heterogeneity is by using large datasets and artificial intelligence (AI) to improve classification models. ...
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