Autism spectrum disorder detection using variable frequency complex demodulation of the electroretinogram / Sultan Mohammad MANJUR ; Md Billal HOSSAIN ; Fernando MARMOLEJO-RAMOS ; Irene O. LEE ; David H. SKUSE ; Dorothy A. THOMPSON ; Paul A. CONSTABLE in Research in Autism Spectrum Disorders, 109 (November 2023)  

Public ISBD [article]  inResearch in Autism Spectrum Disorders > 109 (November 2023) . - 102258 Titre : Autism spectrum disorder detection using variable frequency complex demodulation of the electroretinogram Type de document : Texte imprimé et/ou numérique Auteurs : Sultan Mohammad MANJUR, Auteur ; Md Billal HOSSAIN, Auteur ; Fernando MARMOLEJO-RAMOS, Auteur ; Irene O. LEE, Auteur ; David H. SKUSE, Auteur ; Dorothy A. THOMPSON, Auteur ; Paul A. CONSTABLE, Auteur Article en page(s) : 102258 Langues : Anglais (eng) Mots-clés : Electroretinogram  Signal analysis  Autism spectrum disorder  Machine learning Index. décimale : PER Périodiques Résumé : The early diagnosis of neurodevelopmental conditions such as autism spectrum disorder (ASD), is an unmet need. One difficulty is the identification of a biological signal that relates to the ASD phenotype. The electroretinogram (ERG) waveform has been identified as a possible signal that could categorize neurological conditions such as ASD. The ERG waveform is derived from the electrical activity of photoreceptors and retinal neurons in response to a brief flash of light and provides an indirect 'window' into the central nervous system. Traditionally, the waveform is analyzed in the time-domain, but more recently time-frequency spectrum (TFS) analysis of ERG has been successfully carried out using discrete wavelet transformation (DWT) to characterize the morphological features of the signal. In this study, we propose the use of a high resolution TFS technique, namely variable frequency complex demodulation (VFCDM), to decompose the ERG waveform based on two signal flash strengths to build machine learning (ML) models to categorize ASD. ERG waveforms from N = 217 subjects (71 ASD, 146 control), at two different flash strengths, 446 and 113 Troland seconds (Td.s), from both right and left eyes were included. We analyzed the raw ERG waveforms using DWT and VFCDM. We computed features from the TFSs and trained ML models such as Random Forest, Gradient Boosting, Support Vector Machine to classify ASD from controls. ML models were validated using a subject independent validation strategy, and we found that the ML models with VFCDM features outperformed models using the DWT, achieving an area under the receiver operating characteristics curve of 0.90 (accuracy = 0.81, sensitivity = 0.85, specificity = 0.78). We found that the higher frequency range (80-300 Hz) included more relevant information for classifying ASD compared to the lower frequencies. We also found that the stronger flash strength of 446 Td.s in the right eye provided the best classification result which supports VFCDM analysis of the ERG waveform as a potential tool to aid in the identification of the ASD phenotype. En ligne : https://doi.org/10.1016/j.rasd.2023.102258 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=517 [article] Autism spectrum disorder detection using variable frequency complex demodulation of the electroretinogram [Texte imprimé et/ou numérique] / Sultan Mohammad MANJUR, Auteur ; Md Billal HOSSAIN, Auteur ; Fernando MARMOLEJO-RAMOS, Auteur ; Irene O. LEE, Auteur ; David H. SKUSE, Auteur ; Dorothy A. THOMPSON, Auteur ; Paul A. CONSTABLE, Auteur . - 102258. Langues : Anglais (eng) in Research in Autism Spectrum Disorders > 109 (November 2023) . - 102258 Mots-clés : Electroretinogram  Signal analysis  Autism spectrum disorder  Machine learning Index. décimale : PER Périodiques Résumé : The early diagnosis of neurodevelopmental conditions such as autism spectrum disorder (ASD), is an unmet need. One difficulty is the identification of a biological signal that relates to the ASD phenotype. The electroretinogram (ERG) waveform has been identified as a possible signal that could categorize neurological conditions such as ASD. The ERG waveform is derived from the electrical activity of photoreceptors and retinal neurons in response to a brief flash of light and provides an indirect 'window' into the central nervous system. Traditionally, the waveform is analyzed in the time-domain, but more recently time-frequency spectrum (TFS) analysis of ERG has been successfully carried out using discrete wavelet transformation (DWT) to characterize the morphological features of the signal. In this study, we propose the use of a high resolution TFS technique, namely variable frequency complex demodulation (VFCDM), to decompose the ERG waveform based on two signal flash strengths to build machine learning (ML) models to categorize ASD. ERG waveforms from N = 217 subjects (71 ASD, 146 control), at two different flash strengths, 446 and 113 Troland seconds (Td.s), from both right and left eyes were included. We analyzed the raw ERG waveforms using DWT and VFCDM. We computed features from the TFSs and trained ML models such as Random Forest, Gradient Boosting, Support Vector Machine to classify ASD from controls. ML models were validated using a subject independent validation strategy, and we found that the ML models with VFCDM features outperformed models using the DWT, achieving an area under the receiver operating characteristics curve of 0.90 (accuracy = 0.81, sensitivity = 0.85, specificity = 0.78). We found that the higher frequency range (80-300 Hz) included more relevant information for classifying ASD compared to the lower frequencies. We also found that the stronger flash strength of 446 Td.s in the right eye provided the best classification result which supports VFCDM analysis of the ERG waveform as a potential tool to aid in the identification of the ASD phenotype. En ligne : https://doi.org/10.1016/j.rasd.2023.102258 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=517

Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths / Sultan Mohammad MANJUR in Journal of Autism and Developmental Disorders, 55-4 (April 2024)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 55-4 (April 2024) . - p.1365-1378 Titre : Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths Type de document : Texte imprimé et/ou numérique Auteurs : Sultan Mohammad MANJUR, Auteur ; Luis Roberto Mercado DIAZ, Auteur ; Irene O. LEE, Auteur ; David H. SKUSE, Auteur ; Dorothy A. THOMPSON, Auteur ; Fernando MARMOLEJOS-RAMOS, Auteur ; Paul A. CONSTABLE, Auteur ; Hugo F. POSADA-QUINTERO, Auteur Article en page(s) : p.1365-1378 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : 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. En ligne : https://doi.org/10.1007/s10803-024-06290-w Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=550 [article] Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths [Texte imprimé et/ou numérique] / Sultan Mohammad MANJUR, Auteur ; Luis Roberto Mercado DIAZ, Auteur ; Irene O. LEE, Auteur ; David H. SKUSE, Auteur ; Dorothy A. THOMPSON, Auteur ; Fernando MARMOLEJOS-RAMOS, Auteur ; Paul A. CONSTABLE, Auteur ; Hugo F. POSADA-QUINTERO, Auteur . - p.1365-1378. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 55-4 (April 2024) . - p.1365-1378 Index. décimale : PER Périodiques Résumé : 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. En ligne : https://doi.org/10.1007/s10803-024-06290-w Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=550

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