Spectral Analysis of Light-Adapted Electroretinograms in Neurodevelopmental Disorders: Classification with Machine Learning DOI Creative Commons
Paul A. Constable, Javier Orlando Pinzón-Arenas, Luís Roberto Mercado Díaz

и другие.

Bioengineering, Год журнала: 2024, Номер 12(1), С. 15 - 15

Опубликована: Дек. 28, 2024

Electroretinograms (ERGs) show differences between typically developing populations and those with a diagnosis of autism spectrum disorder (ASD) or attention deficit/hyperactivity (ADHD). In series ERGs collected in ASD (n = 77), ADHD 43), + 21), control 137) groups, this analysis explores the use machine learning feature selection techniques to improve classification these clinically defined groups. Standard time domain signal features were evaluated different models. For classification, balanced accuracy (BA) 0.87 was achieved for male participants. ADHD, BA 0.84 female When three-group model (ASD, control) lower, at 0.70, fell further 0.53 when all groups included control). The findings support role ERG establishing broad two-group but model's performance depends upon sex is limited multiple classes are modeling.

Язык: Английский

Remodeling the light-adapted electroretinogram using a bayesian statistical approach DOI Creative Commons
Marek Brabec, Fernando Marmolejo‐Ramos, Lynne Loh

и другие.

BMC Research Notes, Год журнала: 2025, Номер 18(1)

Опубликована: Янв. 23, 2025

Язык: Английский

Процитировано

0
Synthetic electroretinogram signal generation using a conditional generative adversarial network DOI Creative Commons
Mikhail Kulyabin, Aleksei Zhdanov, Irene Lee

и другие.

Documenta Ophthalmologica, Год журнала: 2025, Номер unknown

Опубликована: Апрель 16, 2025

Abstract Purpose The electroretinogram (ERG) records the functional response of retina. In some neurological conditions, ERG waveform may be altered and could support biomarker discovery. heterogeneous or rare populations, where either large data sets availability a challenge, synthetic signals with Artificial Intelligence (AI) help to mitigate against these factors classification models. Methods This approach was tested using publicly available dataset real ERGs, n = 560 (ASD) 498 (Control) recorded at 9 different flash strengths from 18 ASD (mean age 12.2 ± 2.7 years) 31 Controls 11.8 3.3 that were augmented waveforms, generated through Conditional Generative Adversarial Network. Two deep learning models used classify groups only combined ERGs. One Time Series Transformer (with waveforms in their original form) second Visual model utilizing images wavelets derived Continuous Wavelet Transform Model performance classifying evaluated Balanced Accuracy (BA) as main outcome measure. Results BA improved 0.756 0.879 when ERGs included across all recordings for training Transformer. also achieved best 0.89 single strength 0.95 log cd s m −2 . Conclusions supports application AI improve group recordings.

Язык: Английский

Процитировано

0
Spectral Analysis of Light-Adapted Electroretinograms in Neurodevelopmental Disorders: Classification with Machine Learning DOI Creative Commons
Paul A. Constable, Javier Orlando Pinzón-Arenas, Luís Roberto Mercado Díaz

и другие.

Bioengineering, Год журнала: 2024, Номер 12(1), С. 15 - 15

Опубликована: Дек. 28, 2024

Electroretinograms (ERGs) show differences between typically developing populations and those with a diagnosis of autism spectrum disorder (ASD) or attention deficit/hyperactivity (ADHD). In series ERGs collected in ASD (n = 77), ADHD 43), + 21), control 137) groups, this analysis explores the use machine learning feature selection techniques to improve classification these clinically defined groups. Standard time domain signal features were evaluated different models. For classification, balanced accuracy (BA) 0.87 was achieved for male participants. ADHD, BA 0.84 female When three-group model (ASD, control) lower, at 0.70, fell further 0.53 when all groups included control). The findings support role ERG establishing broad two-group but model's performance depends upon sex is limited multiple classes are modeling.

Язык: Английский

Процитировано

0