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

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