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Li M, Wang Y, Gao H, Xia Z, Zeng C, Huang K, Zhu Z, Lu J, Chen Q, Ke X, Zhang W. Exploring autism via the retina: Comparative insights in children with autism spectrum disorder and typical development. Autism Res 2024; 17:1520-1533. [PMID: 39075780 DOI: 10.1002/aur.3204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Accepted: 07/11/2024] [Indexed: 07/31/2024]
Abstract
Autism spectrum disorder (ASD) is a widely recognized neurodevelopmental disorder, yet the identification of reliable imaging biomarkers for its early diagnosis remains a challenge. Considering the specific manifestations of ASD in the eyes and the interconnectivity between the brain and the eyes, this study investigates ASD through the lens of retinal analysis. We specifically examined differences in the macular region of the retina using optical coherence tomography (OCT)/optical coherence tomography angiography (OCTA) images between children diagnosed with ASD and those with typical development (TD). Our findings present potential novel characteristics of ASD: the thickness of the ellipsoid zone (EZ) with cone photoreceptors was significantly increased in ASD; the large-caliber arteriovenous of the inner retina was significantly reduced in ASD; these changes in the EZ and arteriovenous were more significant in the left eye than in the right eye. These observations of photoreceptor alterations, vascular function changes, and lateralization phenomena in ASD warrant further investigation, and we hope that this work can advance interdisciplinary understanding of ASD.
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Affiliation(s)
- Mingchao Li
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
- Future Lab, Tsinghua University, Beijing, China
| | - Yuexuan Wang
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Huiyun Gao
- Child Mental Health Research Center, Brain Hospital Affiliated to Nanjing Medical University, Nanjing, China
| | - Zhengwang Xia
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Chaofan Zeng
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Kun Huang
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Zhaoqi Zhu
- Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jianfeng Lu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Qiang Chen
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Xiaoyan Ke
- Child Mental Health Research Center, Brain Hospital Affiliated to Nanjing Medical University, Nanjing, China
| | - Weiwei Zhang
- Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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2
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Kulyabin M, Zhdanov A, Maier A, Loh L, Estevez JJ, Constable PA. Generating Synthetic Light-Adapted Electroretinogram Waveforms Using Artificial Intelligence to Improve Classification of Retinal Conditions in Under-Represented Populations. J Ophthalmol 2024; 2024:1990419. [PMID: 39045382 PMCID: PMC11265936 DOI: 10.1155/2024/1990419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 05/27/2024] [Accepted: 06/25/2024] [Indexed: 07/25/2024] Open
Abstract
Visual electrophysiology is often used clinically to determine the functional changes associated with retinal or neurological conditions. The full-field flash electroretinogram (ERG) assesses the global contribution of the outer and inner retinal layers initiated by the rods and cone pathways depending on the state of retinal adaptation. Within clinical centers, reference normative data are used to compare clinical cases that may be rare or underpowered within a specific demographic. To bolster either the reference dataset or the case dataset, the application of synthetic ERG waveforms may offer benefits to disease classification and case-control studies. In this study and as a proof of concept, artificial intelligence (AI) to generate synthetic signals using generative adversarial networks is deployed to upscale male participants within an ISCEV reference dataset containing 68 participants, with waveforms from the right and left eye. Random forest classifiers further improved classification for sex within the group from a balanced accuracy of 0.72-0.83 with the added synthetic male waveforms. This is the first study to demonstrate the generation of synthetic ERG waveforms to improve machine learning classification modelling with electroretinogram waveforms.
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Affiliation(s)
- Mikhail Kulyabin
- Pattern Recognition LabDepartment of Computer ScienceFriedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Aleksei Zhdanov
- Engineering School of Information TechnologiesTelecommunications and Control SystemsUral Federal University, Yekaterinburg, Russia
| | - Andreas Maier
- Pattern Recognition LabDepartment of Computer ScienceFriedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Lynne Loh
- Flinders UniversityCollege of Nursing and Health SciencesCaring Futures Institute, Adelaide, South Australia, Australia
| | - Jose J. Estevez
- Flinders UniversityCollege of Nursing and Health SciencesCaring Futures Institute, Adelaide, South Australia, Australia
| | - Paul A. Constable
- Flinders UniversityCollege of Nursing and Health SciencesCaring Futures Institute, Adelaide, South Australia, Australia
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3
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Saad A, Turgut F, Sommer C, Becker M, DeBuc D, Barboni M, Somfai GM. The Use of the RETeval Portable Electroretinography Device for Low-Cost Screening: A Mini-Review. Klin Monbl Augenheilkd 2024; 241:533-537. [PMID: 38653305 DOI: 10.1055/a-2237-3814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
Electroretinography (ERG) provides crucial insights into retinal function and the integrity of the visual pathways. However, ERG assessments classically require a complicated technical background with costly equipment. In addition, the placement of corneal or conjunctival electrodes is not always tolerated by the patients, which restricts the measurement for pediatric evaluations. In this short review, we give an overview of the use of the RETeval portable ERG device (LKC Technologies, Inc., Gaithersburg, MD, USA), a modern portable ERG device that can facilitate screening for diseases involving the retina and the optic nerve. We also review its potential to provide ocular biomarkers in systemic pathologies, such as Alzheimer's disease and central nervous system alterations, within the framework of oculomics.
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Affiliation(s)
- Amr Saad
- Ophthalmology, Stadtspital Zürich Triemli, Zürich, Switzerland
| | - Ferhat Turgut
- Ophthalmology, Stadtspital Zürich Triemli, Zürich, Switzerland
- Ophthalmology, Gutblick, Pfäffikon, Switzerland
| | - Chiara Sommer
- Ophthalmology, Stadtspital Zürich Triemli, Zürich, Switzerland
| | - Matthias Becker
- Ophthalmology, Stadtspital Zürich Triemli, Zürich, Switzerland
| | - Delia DeBuc
- Bascom Palmer Eye Institute, University of Miami School of Medicine, Miami, Florida, United States
| | - Mirella Barboni
- Department of Ophthalmology, Semmelweis University, Budapest, Hungary
| | - Gabor Mark Somfai
- Ophthalmology, Stadtspital Zürich Triemli, Zürich, Switzerland
- Department of Ophthalmology, Semmelweis University, Budapest, Hungary
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4
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Manjur SM, Diaz LRM, Lee IO, Skuse DH, Thompson DA, Marmolejos-Ramos F, Constable PA, Posada-Quintero HF. Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths. J Autism Dev Disord 2024:10.1007/s10803-024-06290-w. [PMID: 38393437 DOI: 10.1007/s10803-024-06290-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/13/2024] [Indexed: 02/25/2024]
Abstract
PURPOSE Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are conditions that similarly alter cognitive functioning ability and challenge the social interaction, attention, and communication skills of affected individuals. Yet these are distinct neurological conditions that can exhibit diverse characteristics which require different management strategies. It is desirable to develop tools to assist with early distinction so that appropriate early interventions and support may be tailored to an individual's specific requirements. The current diagnostic procedures for ASD and ADHD require a multidisciplinary approach and can be lengthy. This study investigated the potential of electroretinogram (ERG), an eye test measuring retinal responses to light, for rapid screening of ASD and ADHD. METHODS Previous studies identified differences in ERG amplitude between ASD and ADHD, but this study explored time-frequency analysis (TFS) to capture dynamic changes in the signal. ERG data from 286 subjects (146 control, 94 ASD, 46 ADHD) was analyzed using two TFS techniques. RESULTS Key features were selected, and machine learning models were trained to classify individuals based on their ERG response. The best model achieved 70% overall accuracy in distinguishing control, ASD, and ADHD groups. CONCLUSION The ERG to the stronger flash strength provided better separation and the high frequency dynamics (80-300 Hz) were more informative features than lower frequency components. To further improve classification a greater number of different flash strengths may be required along with a discrimination comparison to participants who meet both ASD and ADHD classifications and carry both diagnoses.
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Affiliation(s)
| | | | - Irene O Lee
- Behavioral and Brain Sciences Unit, Population Policy and Practice Program, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - David H Skuse
- Behavioral and Brain Sciences Unit, Population Policy and Practice Program, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Dorothy A Thompson
- Tony Kriss Visual Electrophysiology Unit, Clinical and Academic Department of Ophthalmology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
- UCL Great Ormond Street Institute for Child Health, University College London, London, UK
| | | | - Paul A Constable
- College of Nursing and Health Sciences, Flinders University, Caring Futures Institute, Adelaide, Australia
| | - Hugo F Posada-Quintero
- Department of Biomedical Engineering, University of Connecticut, 06269, Storrs, CT, USA.
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5
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Rakhmatulin I, Dao MS, Nassibi A, Mandic D. Exploring Convolutional Neural Network Architectures for EEG Feature Extraction. SENSORS (BASEL, SWITZERLAND) 2024; 24:877. [PMID: 38339594 PMCID: PMC10856895 DOI: 10.3390/s24030877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/12/2024] [Accepted: 01/20/2024] [Indexed: 02/12/2024]
Abstract
The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We considered the characteristics of EEG signals, coupled with an exploration of various signal processing and data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, and dimension reduction, among others. In addition, we conduct an in-depth analysis of well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, and combined architecture. This paper further offers a comprehensive evaluation of these architectures, covering accuracy metrics, hyperparameters, and an appendix that contains a table outlining the parameters of commonly used CNN architectures for feature extraction from EEG signals.
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Affiliation(s)
- Ildar Rakhmatulin
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
| | - Minh-Son Dao
- National Institute of Information and Communications Technology (NICT), Tokyo 184-0015, Japan
| | - Amir Nassibi
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
| | - Danilo Mandic
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
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Kulyabin M, Zhdanov A, Dolganov A, Ronkin M, Borisov V, Maier A. Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer. SENSORS (BASEL, SWITZERLAND) 2023; 23:8727. [PMID: 37960427 PMCID: PMC10648817 DOI: 10.3390/s23218727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 10/16/2023] [Accepted: 10/23/2023] [Indexed: 11/15/2023]
Abstract
The electroretinogram (ERG) is a clinical test that records the retina's electrical response to light. Analysis of the ERG signal offers a promising way to study different retinal diseases and disorders. Machine learning-based methods are expected to play a pivotal role in achieving the goals of retinal diagnostics and treatment control. This study aims to improve the classification accuracy of the previous work using the combination of three optimal mother wavelet functions. We apply Continuous Wavelet Transform (CWT) on a dataset of mixed pediatric and adult ERG signals and show the possibility of simultaneous analysis of the signals. The modern Visual Transformer-based architectures are tested on a time-frequency representation of the signals. The method provides 88% classification accuracy for Maximum 2.0 ERG, 85% for Scotopic 2.0, and 91% for Photopic 2.0 protocols, which on average improves the result by 7.6% compared to previous work.
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Affiliation(s)
- Mikhail Kulyabin
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany;
| | - Aleksei Zhdanov
- Engineering School of Information Technologies, Telecommunications and Control Systems, Ural Federal University Named after the First President of Russia B. N. Yeltsin, 620002 Yekaterinburg, Russia; (A.Z.); (A.D.); (M.R.); (V.B.)
| | - Anton Dolganov
- Engineering School of Information Technologies, Telecommunications and Control Systems, Ural Federal University Named after the First President of Russia B. N. Yeltsin, 620002 Yekaterinburg, Russia; (A.Z.); (A.D.); (M.R.); (V.B.)
| | - Mikhail Ronkin
- Engineering School of Information Technologies, Telecommunications and Control Systems, Ural Federal University Named after the First President of Russia B. N. Yeltsin, 620002 Yekaterinburg, Russia; (A.Z.); (A.D.); (M.R.); (V.B.)
| | - Vasilii Borisov
- Engineering School of Information Technologies, Telecommunications and Control Systems, Ural Federal University Named after the First President of Russia B. N. Yeltsin, 620002 Yekaterinburg, Russia; (A.Z.); (A.D.); (M.R.); (V.B.)
| | - Andreas Maier
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany;
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Brabec M, Constable PA, Thompson DA, Marmolejo-Ramos F. Group comparisons of the individual electroretinogram time trajectories for the ascending limb of the b-wave using a raw and registered time series. BMC Res Notes 2023; 16:238. [PMID: 37773138 PMCID: PMC10542250 DOI: 10.1186/s13104-023-06535-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 09/25/2023] [Indexed: 10/01/2023] Open
Abstract
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.
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Affiliation(s)
- Marek Brabec
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic
| | - Paul A Constable
- Flinders University, College of Nursing and Health Sciences, Caring Futures Institute, Adelaide, SA, Australia.
| | - Dorothy A Thompson
- 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
| | - Fernando Marmolejo-Ramos
- Centre for Change and Complexity in Learning, The University of South Australia, Adelaide, Australia
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Constable PA, Lim JKH, Thompson DA. Retinal electrophysiology in central nervous system disorders. A review of human and mouse studies. Front Neurosci 2023; 17:1215097. [PMID: 37600004 PMCID: PMC10433210 DOI: 10.3389/fnins.2023.1215097] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 07/17/2023] [Indexed: 08/22/2023] Open
Abstract
The retina and brain share similar neurochemistry and neurodevelopmental origins, with the retina, often viewed as a "window to the brain." With retinal measures of structure and function becoming easier to obtain in clinical populations there is a growing interest in using retinal findings as potential biomarkers for disorders affecting the central nervous system. Functional retinal biomarkers, such as the electroretinogram, show promise in neurological disorders, despite having limitations imposed by the existence of overlapping genetic markers, clinical traits or the effects of medications that may reduce their specificity in some conditions. This narrative review summarizes the principal functional retinal findings in central nervous system disorders and related mouse models and provides a background to the main excitatory and inhibitory retinal neurotransmitters that have been implicated to explain the visual electrophysiological findings. These changes in retinal neurochemistry may contribute to our understanding of these conditions based on the findings of retinal electrophysiological tests such as the flash, pattern, multifocal electroretinograms, and electro-oculogram. It is likely that future applications of signal analysis and machine learning algorithms will offer new insights into the pathophysiology, classification, and progression of these clinical disorders including autism, attention deficit/hyperactivity disorder, bipolar disorder, schizophrenia, depression, Parkinson's, and Alzheimer's disease. New clinical applications of visual electrophysiology to this field may lead to earlier, more accurate diagnoses and better targeted therapeutic interventions benefiting individual patients and clinicians managing these individuals and their families.
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Affiliation(s)
- Paul A. Constable
- College of Nursing and Health Sciences, Caring Futures Institute, Flinders University, Adelaide, SA, Australia
| | - Jeremiah K. H. Lim
- Discipline of Optometry, School of Allied Health, University of Western Australia, Perth, WA, Australia
| | - Dorothy A. Thompson
- The Tony Kriss Visual Electrophysiology Unit, Clinical and Academic Department of Ophthalmology, Great Ormond Street Hospital for Children NHS Trust, London, United Kingdom
- UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
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Mahroo OA. Visual electrophysiology and "the potential of the potentials". Eye (Lond) 2023; 37:2399-2408. [PMID: 36928229 PMCID: PMC10397240 DOI: 10.1038/s41433-023-02491-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 02/09/2023] [Accepted: 03/06/2023] [Indexed: 03/18/2023] Open
Abstract
Visual electrophysiology affords direct, quantitative, objective assessment of visual pathway function at different levels, and thus yields information complementary to, and not necessarily obtainable from, imaging or psychophysical testing. The tests available, and their indications, have evolved, with many advances, both in technology and in our understanding of the neural basis of the waveforms, now facilitating more precise evaluation of physiology and pathophysiology. After summarising the visual pathway and current standard clinical testing methods, this review discusses, non-exhaustively, several developments, focusing particularly on human electroretinogram recordings. These include new devices (portable, non-mydiatric, multimodal), novel testing protocols (including those aiming to separate rod-driven and cone-driven responses, and to monitor retinal adaptation), and developments in methods of analysis, including use of modelling and machine learning. It is likely that several tests will become more accessible and useful in both clinical and research settings. In future, these methods will further aid our understanding of common and rare eye disease, will help in assessing novel therapies, and will potentially yield information relevant to neurological and neuro-psychiatric conditions.
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Affiliation(s)
- Omar A Mahroo
- Institute of Ophthalmology, University College London, 11-43 Bath Street, London, UK.
- Retinal and Genetics Services, Moorfields Eye Hospital, 162 City Road, London, UK.
- Section of Ophthalmology and Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital Campus, Westminster Bridge Road, London, UK.
- Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge, UK.
- Department of Translational Ophthalmology, Wills Eye Hospital, Philadelphia, PA, USA.
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10
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Kulyabin M, Zhdanov A, Dolganov A, Maier A. Optimal Combination of Mother Wavelet and AI Model for Precise Classification of Pediatric Electroretinogram Signals. SENSORS (BASEL, SWITZERLAND) 2023; 23:5813. [PMID: 37447663 DOI: 10.3390/s23135813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 06/14/2023] [Accepted: 06/19/2023] [Indexed: 07/15/2023]
Abstract
The continuous advancements in healthcare technology have empowered the discovery, diagnosis, and prediction of diseases, revolutionizing the field. Artificial intelligence (AI) is expected to play a pivotal role in achieving the goals of precision medicine, particularly in disease prevention, detection, and personalized treatment. This study aims to determine the optimal combination of the mother wavelet and AI model for the analysis of pediatric electroretinogram (ERG) signals. The dataset, consisting of signals and corresponding diagnoses, undergoes Continuous Wavelet Transform (CWT) using commonly used wavelets to obtain a time-frequency representation. Wavelet images were used for the training of five widely used deep learning models: VGG-11, ResNet-50, DensNet-121, ResNext-50, and Vision Transformer, to evaluate their accuracy in classifying healthy and unhealthy patients. The findings demonstrate that the combination of Ricker Wavelet and Vision Transformer consistently yields the highest median accuracy values for ERG analysis, as evidenced by the upper and lower quartile values. The median balanced accuracy of the obtained combination of the three considered types of ERG signals in the article are 0.83, 0.85, and 0.88. However, other wavelet types also achieved high accuracy levels, indicating the importance of carefully selecting the mother wavelet for accurate classification. The study provides valuable insights into the effectiveness of different combinations of wavelets and models in classifying ERG wavelet scalograms.
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Affiliation(s)
- Mikhail Kulyabin
- Pattern Recognition Lab, University of Erlangen-Nuremberg, 91058 Erlangen, Germany
| | - Aleksei Zhdanov
- Engineering School of Information Technologies, Telecommunications and Control Systems, Ural Federal University Named after the First President of Russia B. N. Yeltsin, Yekaterinburg 620002, Russia
| | - Anton Dolganov
- Engineering School of Information Technologies, Telecommunications and Control Systems, Ural Federal University Named after the First President of Russia B. N. Yeltsin, Yekaterinburg 620002, Russia
| | - Andreas Maier
- Pattern Recognition Lab, University of Erlangen-Nuremberg, 91058 Erlangen, Germany
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11
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Zhdanov A, Constable P, Manjur SM, Dolganov A, Posada-Quintero HF, Lizunov A. OculusGraphy: Signal Analysis of the Electroretinogram in a Rabbit Model of Endophthalmitis Using Discrete and Continuous Wavelet Transforms. Bioengineering (Basel) 2023; 10:708. [PMID: 37370639 DOI: 10.3390/bioengineering10060708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 06/04/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND The electroretinogram is a clinical test used to assess the function of the photoreceptors and retinal circuits of various cells in the eye, with the recorded waveform being the result of the summated response of neural generators across the retina. METHODS The present investigation involved an analysis of the electroretinogram waveform in both the time and time-frequency domains through the utilization of the discrete wavelet transform and continuous wavelet transform techniques. The primary aim of this study was to monitor and evaluate the effects of treatment in a New Zealand rabbit model of endophthalmitis via electroretinogram waveform analysis and to compare these with normal human electroretinograms. RESULTS The wavelet scalograms were analyzed using various mother wavelets, including the Daubechies, Ricker, Wavelet Biorthogonal 3.1 (bior3.1), Morlet, Haar, and Gaussian wavelets. Distinctive variances were identified in the wavelet scalograms between rabbit and human electroretinograms. The wavelet scalograms in the rabbit model of endophthalmitis showed recovery with treatment in parallel with the time-domain features. CONCLUSIONS The study compared adult, child, and rabbit electroretinogram responses using DWT and CWT, finding that adult signals had higher power than child signals, and that rabbit signals showed differences in the a-wave and b-wave depending on the type of response tested, while the Haar wavelet was found to be superior in visualizing frequency components in electrophysiological signals for following the treatment of endophthalmitis and may give additional outcome measures for the management of retinal disease.
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Affiliation(s)
- Aleksei Zhdanov
- Machine Learning and Data Analytics Lab, University of Erlangen-Nuremberg, 91052 Erlangen, Germany
- Engineering School of Information Technologies, Telecommunications and Control Systems, Ural Federal University Named after the First President of Russia B. N. Yeltsin, 620002 Yekaterinburg, Russia
| | - Paul Constable
- College of Nursing and Health Sciences, Caring Futures Institute, Flinders University, Adelaide, SA 5042, Australia
| | | | - Anton Dolganov
- Engineering School of Information Technologies, Telecommunications and Control Systems, Ural Federal University Named after the First President of Russia B. N. Yeltsin, 620002 Yekaterinburg, Russia
| | | | - Aleksander Lizunov
- Department of Functional Diagnostics, IRTC Eye Microsurgery Ekaterinburg Center, 620149 Yekaterinburg, Russia
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12
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Friedel EBN, Schäfer M, Endres D, Maier S, Runge K, Bach M, Heinrich SP, Ebert D, Domschke K, Tebartz van Elst L, Nickel K. Electroretinography in adults with high-functioning autism spectrum disorder. Autism Res 2022; 15:2026-2037. [PMID: 36217563 DOI: 10.1002/aur.2823] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 09/19/2022] [Indexed: 12/15/2022]
Abstract
The electroretinogram (ERG) allows the investigation of retinal signaling pathways and has increasingly been applied in individuals with mental disorders in search for potential biomarkers of neurodevelopmental disorders. Preceding ERG examinations in individuals with autism spectrum disorders (ASD) showed inconsistent results, which might be due to the small number of participants, heterogeneity of the ASD population, differences in age ranges, and stimulation methods. The aim of this study was to investigate functional retinal responses in adults with ASD by means of the light-adapted (photopic) ERG. Light-adapted ERG measurements were obtained with the RETeval® system applying three different stimulation protocols. In the final analysis, the ERG parameters a-wave, b-wave, the photopic negative response (PhNR), the photopic hill parameters as well as additional amplitude ratios were compared between 32 adults with high-functioning ASD and 31 non-autistic controls. Both groups were matched with regard to sex and age. No significant functional retinal differences in amplitude or peak time of the a- or b-wave, PhNR, the photopic hill parameters or the ERG-amplitude ratios could be detected in individuals with ASD compared to non-autistic participants. The absence of electrophysiological functional retinal alterations in ASD, suggests that changes in visual perception, such as increased attention to detail or visual hypersensitivity in ASD, are not due to impairments at early levels of retinal signal processing.
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Affiliation(s)
- Evelyn B N Friedel
- Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Eye Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Mirjam Schäfer
- Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Dominique Endres
- Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Simon Maier
- Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Kimon Runge
- Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Michael Bach
- Eye Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Sven P Heinrich
- Eye Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Dieter Ebert
- Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Katharina Domschke
- Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Center for Basics in Neuromodulation, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Ludger Tebartz van Elst
- Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Kathrin Nickel
- Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Sarossy M, Crowston J, Kumar D, Weymouth A, Wu Z. Time-Frequency Analysis of ERG With Discrete Wavelet Transform and Matching Pursuits for Glaucoma. Transl Vis Sci Technol 2022; 11:19. [PMID: 36227605 PMCID: PMC9583752 DOI: 10.1167/tvst.11.10.19] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 09/13/2022] [Indexed: 02/01/2023] Open
Abstract
Purpose To examine the performance of two time-frequency feature extraction techniques applied to electroretinograms (ERGs) for the prediction of glaucoma severity. Methods ERGs targeting the photopic negative response were obtained in 103 eyes of 55 patients with glaucoma. Features from the ERG recordings were extracted using two time-frequency extraction techniques based on the discrete wavelet transform (DWT) and the matching pursuit (MP) decomposition. Amplitude markers of the time-domain signal were also extracted. Linear and multivariate adaptive regression spline (MARS) models were fitted using combinations of these features to predict estimated retinal ganglion cell counts, a measure of glaucoma disease severity derived from standard automated perimetry and optical coherence tomography imaging. Results Predictive models using features from the time-frequency analyses-using both DWT and MP-combined with amplitude markers outperformed predictive models using the markers alone with linear (P = 0.001) and MARS (P ≤ 0.011) models. For example, the proportions of variance (R2) explained by the MARS model using the DWT and MP features with amplitude markers were 0.53 and 0.63, respectively, compared to 0.34 for the model using the markers alone (P = 0.011 and P = 0.001, respectively). Conclusions Novel time-frequency features extracted from the photopic ERG substantially added to the prediction of glaucoma severity compared to using the time-domain amplitude markers alone. Translational Relevance Substantial information about retinal ganglion cell dysfunction exists in the time-frequency domain of ERGs that could be useful in the management of glaucoma.
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Affiliation(s)
- Marc Sarossy
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Victoria, Australia
| | | | | | - Anne Weymouth
- Department of Optometry and Vision Sciences, University of Melbourne, Melbourne, Victoria, Australia
| | - Zhichao Wu
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Victoria, Australia
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
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