4.6 Article

Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer

Related references

Note: Only part of the references are listed.
Article

Ophthalmic Bioengineering. Review

V. O. Ponomarev et al.

Oftalmologiya (2023)

Review Neurosciences

Retinal electrophysiology in central nervous system disorders. A review of human and mouse studies

Paul A. A. Constable et al.

Summary: The retina and brain have similar neurochemistry and neurodevelopmental origins. Retinal findings are being used as potential biomarkers for central nervous system disorders. Despite limitations, functional retinal biomarkers show promise in neurological disorders. This review summarizes functional retinal findings, retinal neurotransmitters, and electrophysiological tests, and suggests that future applications of signal analysis and machine learning algorithms could provide new insights into the pathophysiology and classification of various clinical disorders.

FRONTIERS IN NEUROSCIENCE (2023)

Article Computer Science, Theory & Methods

Transformers in Vision: A Survey

Salman Khan et al.

Summary: Transformer models have shown impressive results in computer vision tasks by simulating long dependencies, supporting parallel processing, and handling multi-modal data. They are widely used in visual recognition, generative modeling, multi-modal tasks, video processing, low-level vision, and three-dimensional analysis, showcasing their strengths in scalability and flexibility.

ACM COMPUTING SURVEYS (2022)

Article Ophthalmology

ISCEV Standard for full-field clinical electroretinography (2022 update)

Anthony G. Robson et al.

Summary: This document presents an updated and revised ISCEV Standard for clinical electroretinogram (ERG) testing. It specifies minimum protocols for ERG stimuli, recording methods, and reporting to ensure consistency and facilitate diagnosis, monitoring, and inter-laboratory comparisons. The main changes include allowing ERGs to meet the Standard without mydriasis and providing more detail on analyzing oscillatory potentials (OPs).

DOCUMENTA OPHTHALMOLOGICA (2022)

Review Ophthalmology

Clinical electroretinography in diabetic retinopathy: a review

Jason McAnany et al.

Summary: The electroretinogram (ERG) is a noninvasive technique that evaluates retinal function and has become increasingly important in studying diabetic retinopathy. Recent clinical studies using various types of ERG have provided evidence of neural dysfunction in diabetic patients, even in those without apparent retinal vascular abnormalities. The ERG has the potential to play a growing role in understanding the natural history of neural dysfunction in diabetes and as an outcome measure in clinical trials targeting neural preservation in diabetic retinopathy.

SURVEY OF OPHTHALMOLOGY (2022)

Article Neurosciences

Novel Machine-Learning Based Framework Using Electroretinography Data for the Detection of Early-Stage Glaucoma

Mohan Kumar Gajendran et al.

Summary: This study develops a novel machine learning algorithm to predict the development of early-stage glaucoma by analyzing ERG signals. The results show that machine learning models can detect subtle changes using advanced features derived from ERG signals.

FRONTIERS IN NEUROSCIENCE (2022)

Article Neurosciences

Discrete Wavelet Transform Analysis of the Electroretinogram in Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder

Paul A. Constable et al.

Summary: This study used a discrete wavelet transform (DWT) approach to evaluate the electroretinogram waveform in autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). Significant group differences were found in the b-wave and oscillatory potentials (OPs) energies, and the b-wave amplitude. The results suggest that retinal signal analysis may provide insights into group differences in neurodevelopmental or neurodegenerative conditions. Further research is needed to determine if it can be used as a classification model for conditions such as ASD and ADHD.

FRONTIERS IN NEUROSCIENCE (2022)

Review Chemistry, Analytical

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

Nida Aslam et al.

Summary: This article reviews the application of machine learning in diagnosing Multiple Sclerosis (MS). Researchers have developed various models using different types of data such as MRI and clinical data. SVM, RF, and CNN are identified as the most successful approaches. The article discusses the challenges in MS diagnosis, including differentiating the disease from others with similar symptoms, data confidentiality, interpretability, and the difficulty of collecting large reliable datasets. Moreover, opportunities such as secure platforms, better AI solutions, and combining different data types are discussed to improve the automated diagnosis of MS.

SENSORS (2022)

Article Ophthalmology

Phenotyping of ABCA4 Retinopathy by Machine Learning Analysis of Full-Field Electroretinography

Sophie L. Glinton et al.

Summary: This study explores the application of machine learning in interpreting ERG data and correlating genotype-phenotype in ABCA4 retinopathy. It demonstrates the effectiveness of automated classification and regression analysis in quantifying the severity of ABCA4 variants and retinal dysfunction.

TRANSLATIONAL VISION SCIENCE & TECHNOLOGY (2022)

Proceedings Paper Computer Science, Artificial Intelligence

TinyViT: Fast Pretraining Distillation for Small Vision Transformers

Kan Wu et al.

Summary: This paper proposes TinyViT, a new family of small and efficient vision transformers pretrained on large-scale datasets using a fast distillation framework. TinyViT achieves excellent performance on multiple tasks while saving computational and memory resources.

COMPUTER VISION, ECCV 2022, PT XXI (2022)

Article Ophthalmology

Continuous wavelet transform analysis of ERG in patients with diabetic retinopathy

Hamid Ahmadieh et al.

Summary: The study compared time and time-frequency-domain analysis in evaluating ERGs in subjects with NPDR, finding that implicit times of b-waves were significantly increased, and amplitudes of a- and b-wave were significantly decreased in the NPDR group.

DOCUMENTA OPHTHALMOLOGICA (2021)

Article Engineering, Biomedical

Effects of obesity on time-frequency components of electroretinogram signal using continuous wavelet transform

Okan Erkaymaz et al.

Summary: This study investigates the impact of obesity on electroretinogram signals, utilizing analysis of a and b waves to examine different responses and finding that continuous wavelet transform provides better results for time-frequency analysis. Mexican hat wavelet is identified as the most suitable for analyzing obesity effects, and it is shown that the b wave is significantly affected by obesity for the maximal combined response.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2021)

Article Genetics & Heredity

Clinical and Genetic Characteristics of Korean Congenital Stationary Night Blindness Patients

Hyeong-Min Kim et al.

Summary: This study investigated the clinical and genetic characteristics of 19 Korean patients with congenital stationary night blindness (CSNB) at two tertiary hospitals. Different genetic mutations were found to be associated with different types of CSNB, and 10 novel variants were identified. Further studies with a larger number of subjects are needed to explore the clinical and genetic aspects of CSNB.

GENES (2021)

Review Computer Science, Information Systems

Feature Extraction Methods for Electroretinogram Signal Analysis: A Review

Soroor Behbahani et al.

Summary: Feature extraction is crucial for analyzing and compressing ERG signals. Different methods, such as time-domain, frequency-domain, time-frequency domain, and nonlinear techniques, have been widely used. This paper reviews and compares various feature extraction methods for ERG signals to guide the selection of the most suitable method based on performance.

IEEE ACCESS (2021)

Article Ophthalmology

ERG shrinks by 10% when reducing dark adaptation time to 10 min, but only for weak flashes

Michael Bach et al.

DOCUMENTA OPHTHALMOLOGICA (2020)

Review Ophthalmology

Artificial intelligence in retina

Ursula Schmidt-Erfurth et al.

PROGRESS IN RETINAL AND EYE RESEARCH (2018)

Article Ophthalmology

Baseline Detrending for the Photopic Negative Response

Jessica Tang et al.

TRANSLATIONAL VISION SCIENCE & TECHNOLOGY (2018)

Article Ophthalmology

Full-field electroretinogram in autism spectrum disorder

Paul A. Constable et al.

DOCUMENTA OPHTHALMOLOGICA (2016)

Article Ophthalmology

ISCEV Standard for full-field clinical electroretinography (2015 update)

Daphne L. McCulloch et al.

DOCUMENTA OPHTHALMOLOGICA (2015)

Article Computer Science, Interdisciplinary Applications

An approach based on wavelet analysis for feature extraction in the a-wave of the electroretinogram

R. Barraco et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2011)

Article Biology

ERG signal analysis using wavelet transform

R. Barraco et al.

THEORY IN BIOSCIENCES (2011)

Article Ophthalmology

Using multifocal ERG ring ratios to detect and follow Plaquenil retinal toxicity: a review

Jonathan S. Lyons et al.

DOCUMENTA OPHTHALMOLOGICA (2009)