MfERG responses to long-duration white stimuli in glaucoma patients

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

Purpose

Early-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.

Methods

ERG 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.

Results

Random 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.

Conclusions

The 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.

Continuous-wavelet-transform analysis of the multifocal ERG waveform in glaucoma diagnosis

The vast majority of multifocal electroretinogram (mfERG) signal analyses to detect glaucoma study the signals' amplitudes and latencies. The purpose of this paper is to investigate application of wavelet analysis of mfERG signals in diagnosis of glaucoma. This analysis method applies the continuous wavelet transform (CWT) to the signals, using the real Morlet wavelet. CWT coefficients resulting from the scale of maximum correlation are used as inputs to a neural network, which acts as a classifier. mfERG recordings are taken from the eyes of 47 subjects diagnosed with chronic open-angle glaucoma and from those of 24 healthy subjects. The high sensitivity in the classification (0.894) provides reliable detection of glaucomatous sectors, while the specificity achieved (0.844) reflects accurate detection of healthy sectors. The results obtained in this paper improve on the previous findings reported by the authors using the same visual stimuli and database.

Structural and functional changes in glaucoma: comparing the two-flash multifocal electroretinogram to optical coherence tomography and visual fields

PURPOSE

To correlate multifocal electroretinogram (mfERG) findings in the macular area of glaucoma patients with automated perimetry (visual fields) and with optical coherence tomography (OCT).

METHODS

A two-global flash mfERG (VERIS™) was recorded in 20 eyes with primary open-angle glaucoma. The root mean square was calculated, and three response epochs were analysed: the direct component (15-45 ms) and two induced components (IC-1 at 45-75 ms and IC-2 at 75-105 ms). The central 10° of the mfERG was compared to the central 10° of the OCT and of the visual field. Responses grouped in a superior and in an inferior semicircle, extending between 10° and 20°, were also compared to the corresponding areas of the OCT and of the visual fields. In addition, the area of the papillomacular bundle was also analysed separately.

RESULTS

In glaucoma patients, mfERG responses showed a significant positive association with retinal thickness in the central 10° for IC2 (p = 0.001) and a trend for IC1 (p = 0.066). A significant association was found between the central IC1 and IC2 of the mfERG and corresponding perimetric sensitivities expressed in linear units (p < 0.01). The OCT showed a positive association with visual field sensitivities (p < 0.05) in all areas examined (p < 0.05). Separation of the papillomacular bundle area did not improve structure-function association further.

CONCLUSIONS

In our study, mfERG showed a statistically significant correlation with perimetric sensitivity measured in linear units and with structural macular changes detected with time-domain OCT.