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

Attention to the Electroretinogram: Gated Multilayer Perceptron for ASD Classification DOI Creative Commons
Mikhail Kulyabin, Paul A. Constable, Aleksei Zhdanov

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 52352 - 52362

Опубликована: Янв. 1, 2024

The electroretinogram (ERG) is a clinical test that records the retina's electrical response to brief flash of light as waveform signal. Analysis ERG signal offers promising non-invasive method for studying different neurodevelopmental and neurodegenerative disorders. Autism Spectrum Disorder (ASD) condition characterized by poor communication, reduced reciprocal social interaction, restricted and/or repetitive stereotyped behaviors should be detected early possible ensure timely appropriate intervention support individual their family. In this study, we applied gated Multilayer Perceptron (gMLP) light-adapted classification an effective alternative Transformers. first reported application model ASD which consisted basic multilayer perceptrons, with fewer parameters than We compared performance time-series models on ASD-Control dataset found superiority gMLP in accuracy was best at 89.7% supports use based recordings involving case-control comparisons.

Язык: Английский

Процитировано

4
Generating Synthetic Light‐Adapted Electroretinogram Waveforms Using Artificial Intelligence to Improve Classification of Retinal Conditions in Under‐Represented Populations DOI Creative Commons
Mikhail Kulyabin, Aleksei Zhdanov, Andreas Maier

и другие.

Journal of Ophthalmology, Год журнала: 2024, Номер 2024(1)

Опубликована: Янв. 1, 2024

Visual electrophysiology is often used clinically to determine the functional changes associated with retinal or neurological conditions. The full‐field flash electroretinogram (ERG) assesses global contribution of outer and inner layers initiated by rods cone pathways depending on state adaptation. Within clinical centers, reference normative data are compare cases that may be rare underpowered within a specific demographic. To bolster either dataset case dataset, application synthetic ERG waveforms offer benefits disease classification case‐control studies. In this study as proof concept, artificial intelligence (AI) generate signals using generative adversarial networks deployed upscale male participants an ISCEV containing 68 participants, from right left eye. Random forest classifiers further improved for sex group balanced accuracy 0.72–0.83 added waveforms. This first demonstrate generation improve machine learning modelling

Язык: Английский

Процитировано

4
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
Comparison of Bioelectric Signals and Their Applications in Artificial Intelligence: A Review DOI Creative Commons

Juarez-Castro Flavio Alfonso,

Toledo-Rios Juan Salvador,

Marco Antonio Aceves-Fernández

и другие.

Computers, Год журнала: 2025, Номер 14(4), С. 145 - 145

Опубликована: Апрель 11, 2025

This review examines the role of various bioelectrical signals in conjunction with artificial intelligence (AI) and analyzes how these are utilized AI applications. The applications electroencephalography (EEG), electroretinography (ERG), electromyography (EMG), electrooculography (EOG), electrocardiography (ECG) diagnostic therapeutic systems focused on. Signal processing techniques discussed, relevant studies that have clinical research settings highlighted. Advances signal classification methodologies powered by significantly improved accuracy efficiency medical analysis. integration algorithms for real-time monitoring diagnosis, particularly personalized medicine, is emphasized. AI-driven approaches shown to potential enhance precision improve patient outcomes. However, further needed optimize models diverse environments fully exploit interaction between technologies.

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
Design of a Smartphone-Based Clinical Electroretinogram Recording System DOI

Nicolas Cordoba,

Samuel Daza,

Paul A. Constable

и другие.

2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Год журнала: 2024, Номер unknown, С. 1 - 2

Опубликована: Июнь 26, 2024

Язык: Английский

Процитировано

1
High-Resolution Time-Frequency Analysis of EEG Signals for Affective Computing DOI
Yedukondala Rao Veeranki, Hugo F. Posada–Quintero

Опубликована: Июль 15, 2024

Affective computing is a critical aspect of human-computer interaction. Electroencephalographic (EEG) signals, which reflect electrical brain activity, are widely used for the understanding human emotional states. However, these signals nonlinear and nonstationary, making traditional analysis methods insufficient. To address challenges, recent studies have focused on time-frequency analysis. In this paper, we propose variable frequency complex demodulation (VFCDM) approach to obtain high-resolution spectra (TFS) from EEG signals. First, compute TFS using time-varying optimal parameter search technique capture spectral information. Then generate VFCDM sub-bands extract statistical features each sub-bands. These then with Random Forest algorithm classify arousal valence dimensions. Our results demonstrate robustness its ability accurately discriminate affective The δ-VFCDM γ-VFCDM bands produced highest F1 scores 71.80% Arousal 69.55% Valence differentiation. This work significantly advances EEG-based opens avenues more emotionally attuned interaction systems.

Язык: Английский

Процитировано

1
Early diagnosis of children with autism using artificial intelligence during dental care DOI Open Access
E. Veseli, Teuta Pustina-Krasniqi

European Archives of Paediatric Dentistry, Год журнала: 2024, Номер 25(3), С. 453 - 453

Опубликована: Март 27, 2024

Язык: Английский

Процитировано

0
Exploring autism via the retina: Comparative insights in children with autism spectrum disorder and typical development DOI Open Access
Mingchao Li, Yuexuan Wang,

Huiyun Gao

и другие.

Autism Research, Год журнала: 2024, Номер 17(8), С. 1520 - 1533

Опубликована: Июль 29, 2024

Abstract Autism spectrum disorder (ASD) is a widely recognized neurodevelopmental disorder, yet the identification of reliable imaging biomarkers for its early diagnosis remains challenge. Considering specific manifestations ASD in eyes and interconnectivity between brain eyes, this study investigates through lens retinal analysis. We specifically examined differences macular region retina using optical coherence tomography (OCT)/optical angiography (OCTA) images children diagnosed with those typical development (TD). Our findings present potential novel characteristics ASD: thickness ellipsoid zone (EZ) cone photoreceptors was significantly increased ASD; large‐caliber arteriovenous inner reduced these changes EZ were more significant left eye than right eye. These observations photoreceptor alterations, vascular function changes, lateralization phenomena warrant further investigation, we hope that work can advance interdisciplinary understanding ASD.

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