OBJECTIVES: The electroretinogram is a clinical test commonly used in the diagnosis of retinal disorders with the peak time and amplitude of the a- and b-waves used as the main indicators of retinal function. However, subtle changes that affect the shape of the electroretinogram waveform may occur in the early stages of disease or in conditions that have a neurodevelopmental or neurodegenerative origin. In such cases, we introduce a statistical approach to mathematically model the shape of the electroretinogram waveform that may aid clinicians and researchers using the electroretinogram or other biological signal recordings to identify morphological features in the waveforms that may not be captured by the time or time-frequency domains of the waveforms. We present a statistical graphics-based analysis of the ascending limb of the b-wave (AL-b) of the electroretinogram in children with and without a diagnosis of autism spectrum disorder (ASD) with a narrative explanation of the statistical approach to illustrate how different features of the waveform based on location and scale derived from raw and registered time series can reveal subtle differences between the groups. RESULTS: Analysis of the raw time trajectories confirmed findings of previous studies with a reduced and delayed b-wave amplitude in ASD. However, when the individual time trajectories were registered then group differences were visible in the mean amplitude at registered time ~ 0.6 suggesting a novel method to differentiate groups using registration of the ERG waveform.

Centre for Change and Complexity in Learning The University of South Australia Adelaide Australia

Flinders University College of Nursing and Health Sciences Caring Futures Institute Adelaide SA Australia

Institute of Computer Science of the Czech Academy of Sciences Prague Czech Republic

The Tony Kriss Visual Electrophysiology Unit Clinical and Academic Department of Ophthalmology Great Ormond Street Hospital for Children NHS Trust London UK

UCL Great Ormond Street Institute of Child Health University College London London UK

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Remodeling the light-adapted electroretinogram using a bayesian statistical approach

Spectral Analysis of Light-Adapted Electroretinograms in Neurodevelopmental Disorders: Classification with Machine Learning