1
|
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.
Collapse
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
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Gajendran MK, Rohowetz LJ, Koulen P, Mehdizadeh A. Novel Machine-Learning Based Framework Using Electroretinography Data for the Detection of Early-Stage Glaucoma. Front Neurosci 2022; 16:869137. [PMID: 35600610 PMCID: PMC9115110 DOI: 10.3389/fnins.2022.869137] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 03/28/2022] [Indexed: 01/05/2023] Open
Abstract
PurposeEarly-stage glaucoma diagnosis has been a challenging problem in ophthalmology. The current state-of-the-art glaucoma diagnosis techniques do not completely leverage the functional measures' such as electroretinogram's immense potential; instead, focus is on structural measures like optical coherence tomography. The current study aims to take a foundational step toward the development of a novel and reliable predictive framework for early detection of glaucoma using machine-learning-based algorithm capable of leveraging medically relevant information that ERG signals contain.MethodsERG signals from 60 eyes of DBA/2 mice were grouped for binary classification based on age. The signals were also grouped based on intraocular pressure (IOP) for multiclass classification. Statistical and wavelet-based features were engineered and extracted. Important predictors (ERG tests and features) were determined, and the performance of five machine learning-based methods were evaluated.ResultsRandom forest (bagged trees) ensemble classifier provided the best performance in both binary and multiclass classification of ERG signals. An accuracy of 91.7 and 80% was achieved for binary and multiclass classification, respectively, suggesting that machine-learning-based models can detect subtle changes in ERG signals if trained using advanced features such as those based on wavelet analyses.ConclusionsThe present study describes a novel, machine-learning-based method to analyze ERG signals providing additional information that may be used to detect early-stage glaucoma. Based on promising performance metrics obtained using the proposed machine-learning-based framework leveraging an established ERG data set, we conclude that the novel framework allows for detection of functional deficits of early/various stages of glaucoma in mice.
Collapse
Affiliation(s)
- Mohan Kumar Gajendran
- Department of Civil and Mechanical Engineering, School of Computing and Engineering, University of Missouri-Kansas City, Kansas City, MO, United States
| | - Landon J. Rohowetz
- Vision Research Center, Department of Ophthalmology, University of Missouri-Kansas City, Kansas City, MO, United States
| | - Peter Koulen
- Vision Research Center, Department of Ophthalmology, University of Missouri-Kansas City, Kansas City, MO, United States
- Department of Biomedical Sciences, University of Missouri-Kansas City, Kansas City, MO, United States
| | - Amirfarhang Mehdizadeh
- Department of Civil and Mechanical Engineering, School of Computing and Engineering, University of Missouri-Kansas City, Kansas City, MO, United States
- Vision Research Center, Department of Ophthalmology, University of Missouri-Kansas City, Kansas City, MO, United States
- *Correspondence: Amirfarhang Mehdizadeh
| |
Collapse
|
4
|
Rao A, Padhy D, Pal A, Roy AK. Visual function tests for glaucoma practice - What is relevant? Indian J Ophthalmol 2022; 70:749-758. [PMID: 35225508 PMCID: PMC9114550 DOI: 10.4103/ijo.ijo_1390_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Glaucoma represents one of the most important ocular diseases causing irreversible ganglion cell death. It is one of the most common causes of visual impairment and morbidity in the elderly population. There are various tests for measuring visual function in glaucoma. While visual field remains the undisputed method for screening, diagnosis, and monitoring disease progression, other tests have been studied for their utility in glaucoma practice. This review discusses some of the commonly used tests of visual function that can be routinely used in clinics for glaucoma management. Among the various modalities of testing visual function in glaucoma, this review highlights the tests that are most clinically relevant.
Collapse
Affiliation(s)
- Aparna Rao
- Department of Glaucoma Services, L. V. Prasad Eye Institute, Bhubaneswar, Odisha, India
| | - Debananda Padhy
- Department of Glaucoma Services, L. V. Prasad Eye Institute, Bhubaneswar, Odisha, India
| | - Anindita Pal
- Department of Glaucoma Services, L. V. Prasad Eye Institute, Bhubaneswar, Odisha, India
| | - Avik Kumar Roy
- Department of Glaucoma Services, L. V. Prasad Eye Institute, Bhubaneswar, Odisha, India
| |
Collapse
|
5
|
López-Dorado A, Pérez J, Rodrigo M, Miguel-Jiménez J, Ortiz M, de Santiago L, López-Guillén E, Blanco R, Cavalliere C, Morla EMS, Boquete L, Garcia-Martin E. Diagnosis of multiple sclerosis using multifocal ERG data feature fusion. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2021; 76:157-167. [PMID: 34867127 PMCID: PMC8475498 DOI: 10.1016/j.inffus.2021.05.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 11/15/2020] [Accepted: 05/17/2021] [Indexed: 05/16/2023]
Abstract
The purpose of this paper is to implement a computer-aided diagnosis (CAD) system for multiple sclerosis (MS) based on analysing the outer retina as assessed by multifocal electroretinograms (mfERGs). MfERG recordings taken with the RETI-port/scan 21 (Roland Consult) device from 15 eyes of patients diagnosed with incipient relapsing-remitting MS and without prior optic neuritis, and from 6 eyes of control subjects, are selected. The mfERG recordings are grouped (whole macular visual field, five rings, and four quadrants). For each group, the correlation with a normative database of adaptively filtered signals, based on empirical model decomposition (EMD) and three features from the continuous wavelet transform (CWT) domain, are obtained. Of the initial 40 features, the 4 most relevant are selected in two stages: a) using a filter method and b) using a wrapper-feature selection method. The Support Vector Machine (SVM) is used as a classifier. With the optimal CAD configuration, a Matthews correlation coefficient value of 0.89 (accuracy = 0.95, specificity = 1.0 and sensitivity = 0.93) is obtained. This study identified an outer retina dysfunction in patients with recent MS by analysing the outer retina responses in the mfERG and employing an SVM as a classifier. In conclusion, a promising new electrophysiological-biomarker method based on feature fusion for MS diagnosis was identified.
Collapse
Affiliation(s)
- A. López-Dorado
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, Alcalá de Henares, Spain
| | - J. Pérez
- Department of Ophthalmology, Miguel Servet University Hospital, Zaragoza, Spain
- Aragon Institute for Health Research (IIS Aragon). Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), University of Zaragoza, Spain
| | - M.J. Rodrigo
- Department of Ophthalmology, Miguel Servet University Hospital, Zaragoza, Spain
- Aragon Institute for Health Research (IIS Aragon). Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), University of Zaragoza, Spain
- RETICS: Thematic Networks for Co-operative Research in Health for Ocular Diseases, Spain
| | - J.M. Miguel-Jiménez
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, Alcalá de Henares, Spain
| | - M. Ortiz
- School of Physics, University of Melbourne, VIC 3010, Australia
| | - L. de Santiago
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, Alcalá de Henares, Spain
| | - E. López-Guillén
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, Alcalá de Henares, Spain
| | - R. Blanco
- Department of Surgery, Medical and Social Sciences, University of Alcalá, Alcalá de Henares, Spain
- RETICS: Thematic Networks for Co-operative Research in Health for Ocular Diseases, Spain
| | - C. Cavalliere
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, Alcalá de Henares, Spain
| | - E. Mª Sánchez Morla
- Department of Psychiatry, Hospital 12 de Octubre Research Institute (i+12), 28041 Madrid, Spain
- Faculty of Medicine, Complutense University of Madrid, 28040 Madrid, Spain
- CIBERSAM: Biomedical Research Networking Centre in Mental Health, 28029 Madrid, Spain
| | - L. Boquete
- Biomedical Engineering Group, Department of Electronics, University of Alcalá, Alcalá de Henares, Spain
- RETICS: Thematic Networks for Co-operative Research in Health for Ocular Diseases, Spain
| | - E. Garcia-Martin
- Department of Ophthalmology, Miguel Servet University Hospital, Zaragoza, Spain
- Aragon Institute for Health Research (IIS Aragon). Miguel Servet Ophthalmology Innovation and Research Group (GIMSO), University of Zaragoza, Spain
- RETICS: Thematic Networks for Co-operative Research in Health for Ocular Diseases, Spain
| |
Collapse
|
6
|
Ahmadieh H, Behbahani S, Safi S. Continuous wavelet transform analysis of ERG in patients with diabetic retinopathy. Doc Ophthalmol 2020; 142:305-314. [PMID: 33226538 DOI: 10.1007/s10633-020-09805-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 11/10/2020] [Indexed: 01/02/2023]
Abstract
PURPOSE Diabetic retinopathy (DR) is one of the leading causes of blindness worldwide. Non-proliferative diabetic retinopathy (NPDR) is a stage of the disease that contains morphological and functional disruption of the retinal vasculature and dysfunction of retinal neurons. This study aimed to compare time and time-frequency-domain analysis in the evaluation of electroretinograms (ERGs) in subjects with NPDR. METHOD The ERG responses were recorded in 16 eyes from 12 patients with NPDR and 24 eyes from 12 healthy subjects as the control group. The implicit time, amplitude, and time-frequency-domain parameters of photopic and scotopic ERGs were analyzed. RESULTS The implicit times of b-waves in the dark-adapted 10.0 (P = 0.0513) and light-adapted 3.0 (P = 0.0414) were significantly increased in the NPDR group. The amplitudes of a- and b-wave showed a significantly decreased dark-adapted 10.0 (P = 0.0212; P = 0.0133) and light-adapted 3.0 (P = 0.0517; P = 0.0021) ERG of the NPDR group. The Cohen's d effect size had higher values in the amplitude of dark-adapted 10.0 b-wave (|d|= 1.8058) and amplitude of light-adapted 3.0 b-wave (|d|= 1.9662). The CWT results showed that the frequency ranges of the dominant components in dark-adapted 10.0 and light-adapted 3.0 ERG were decreased in the NPDR group compared to the healthy group (P < 0.05). The times associated with the NDPR group's dominant components were increased compared to normal eyes in both dark-adapted 10.0 and light-adapted 3.0 ERG (P < 0.05). All Cohen's d effect sizes of the implicit times and dominant frequency components were on a large scale (|d|> 1). CONCLUSION These findings suggest that the time and time-frequency parameters of both photopic and scotopic ERGs can be good indicators for DR. However, time-frequency-domain analysis could present more information might be helpful in the assessment of the DR severity.
Collapse
Affiliation(s)
- Hamid Ahmadieh
- Ophthalmic Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Soroor Behbahani
- Department of Biomedical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.
| | - Sare Safi
- Ophthalmic Epidemiology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| |
Collapse
|
7
|
Abstract
OBJECTIVES Electrophysiological testing of the visual system has been continuously used in studies involving the evaluation of retinal ganglion cells and the diagnosis of glaucoma. This study aims to review the results of recent studies regarding the clinical applicability of electrophysiological tests to glaucoma. METHODS A systematic review of the literature was carried out by 2 independent reviewers using the PubMed and EMBASE electronic databases, searching for articles published in English from January 1, 2014 to July 1, 2019 using a combination of the following keywords: ("glaucoma" OR "ocular hypertension") AND ("electrophysiolog" OR "electroretinogra" OR "ERG" OR "mfERG" OR "Pattern-reversal electroretinography" OR "PERG" OR "mfPERG" OR "photopic negative response" OR "pattern electroretinogram" OR "visual evoked potential" OR "multifocal electroretinography" OR "multifocal electroretinogram" OR "electro-oculography" OR "multifocal VEP" OR "mf-ERG"). A total of 38 studies were selected and the data of 30 of them were tabulated in this review. RESULTS Among the 30 studies selected, the photopic negative response and the reversal pattern electroretinogram were found to be the major methods used to record the electroretinographic responses generated by the retinal ganglion cell. Their multifocal versions and the multifocal visual evoked potential were also proposed during this period. In general, the results underscored a consistent but general correlation between the amplitude and latency measures and routine tests for glaucoma, such as perimetry and optical coherence tomography. DISCUSSION In agreement with previous reviews, clinical electrophysiological testing of the visual system reasonably matched with both the structural and functional analyses for glaucoma. No definitive indications of these tests have been established either at early detection or during follow-up of the disease, and easier protocols and better topographical correspondence with current glaucoma tests are warranted for their routine use.
Collapse
|
8
|
Brandao LM, Monhart M, Schötzau A, Ledolter AA, Palmowski-Wolfe AM. Wavelet decomposition analysis in the two-flash multifocal ERG in early glaucoma: a comparison to ganglion cell analysis and visual field. Doc Ophthalmol 2017; 135:29-42. [PMID: 28593391 PMCID: PMC5532413 DOI: 10.1007/s10633-017-9593-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2016] [Accepted: 05/23/2017] [Indexed: 11/25/2022]
Abstract
PURPOSE To further improve analysis of the two-flash multifocal electroretinogram (2F-mfERG) in glaucoma in regard to structure-function analysis, using discrete wavelet transform (DWT) analysis. METHODS Sixty subjects [35 controls and 25 primary open-angle glaucoma (POAG)] underwent 2F-mfERG. Responses were analyzed with the DWT. The DWT level that could best separate POAG from controls was compared to the root-mean-square (RMS) calculations previously used in the analysis of the 2F-mfERG. In a subgroup analysis, structure-function correlation was assessed between DWT, optical coherence tomography and automated perimetry (mf103 customized pattern) for the central 15°. RESULTS Frequency level 4 of the wavelet variance analysis (144 Hz, WVA-144) was most sensitive (p < 0.003). It correlated positively with RMS but had a better AUC. Positive relations were found between visual field, WVA-144 and GCIPL thickness. The highest predictive factor for glaucoma diagnostic was seen in the GCIPL, but this improved further by adding the mean sensitivity and WVA-144. CONCLUSIONS mfERG using WVA analysis improves glaucoma diagnosis, especially when combined with GCIPL and MS.
Collapse
Affiliation(s)
- Livia M Brandao
- Department of Ophthalmology, Basel University Hospital, Basel, BS, Switzerland.
- Universitätsspital Basel Augenklinik, Mittlere Strasse 91, 4031, Basel, Switzerland.
| | | | - Andreas Schötzau
- Department of Ophthalmology, Basel University Hospital, Basel, BS, Switzerland
| | - Anna A Ledolter
- Department of Ophthalmology, Medical University of Vienna, Vienna, Austria
| | | |
Collapse
|