Empirical Mode Decomposition-Based Filter Applied to Multifocal Electroretinograms in Multiple Sclerosis Diagnosis

Influence of sex on chronic steroid-induced glaucoma: 24-Weeks follow-up study in rats

The objective was to evaluate ocular changes based on sex in steroid-induced glaucoma models in rats comparing healthy controls, over 24 weeks follow-up. Eighty-nine Long-Evans rats (38 males and 51 females) with steroid-induced glaucoma were analysed. Two steroid-induced glaucoma models were generated by injecting poly-co-lactic-glycolic acid microspheres loaded with dexamethasone (MMDEX model) and dexamethasone-fibronectin (MMDEXAFIBRO model) into the ocular anterior chamber. Intraocular pressure was measured by rebound tonometer Tonolab®. Neuroretinal function was analysed using dark- and light-adapted electroretinography (Roland consult® RETIanimal ERG), and structure was analysed using optical coherence tomography (OCT Spectralis, Heidelberg® Engineering) using Retina Posterior Pole, Retinal Nerve Fibre Layer and Ganglion Cell Layer protocols over 24 weeks. Males showed statistically (p < 0.05) higher intraocular pressure measurements. In both sexes and models neuroretinal thickness tended to decrease over time. In the MMDEX model, males showed higher IOP values and greatest percentage thickness loss in the Ganglion Cell Layer (p = 0.015). Females receiving MMDEXAFIBRO experienced large fluctuations in thickness, a higher percentage loss (on average) in Retina Posterior Pole (p = 0.035), Retinal Nerve Fibre Layer and Ganglion Cell Layer than aged-matched males, and the highest thickness loss rate by mmHg. Although no difference was found by sex in dark- and light-adapted electroretinography, increased amplitude in photopic negative response was found in MMDEX males and MMDEXAFIBRO females at 12 weeks. Although both glaucoma models used dexamethasone, different intraocular pressure and neuroretinal changes were observed depending on sex and other influential cofactors (fibronectin). Both sex and the induced glaucoma model influenced neuroretinal degeneration.

Tunable degrees of neurodegeneration in rats based on microsphere-induced models of chronic glaucoma

This study compares four different animal models of chronic glaucoma against normal aging over 6 months. Chronic glaucoma was induced in 138 Long-Evans rats and compared against 43 aged-matched healthy rats. Twenty-five rats received episcleral vein sclerosis injections (EPIm cohort) while the rest were injected in the eye anterior chamber with a suspension of biodegradable microspheres: 25 rats received non-loaded microspheres (N-L Ms cohort), 45 rats received microspheres loaded with dexamethasone (MsDexa cohort), and 43 rats received microspheres co-loaded with dexamethasone and fibronectin (MsDexaFibro cohort). Intraocular pressure, neuroretinal function, structure and vitreous interface were evaluated. Each model caused different trends in intraocular pressure, produced specific retinal damage and vitreous signals. The steepest and strongest increase in intraocular pressure was seen in the EPIm cohort and microspheres models were more progressive. The EPIm cohort presented the highest vitreous intensity and percentage loss in the ganglion cell layer, the MsDexa cohort presented the greatest loss in the retinal nerve fiber layer, and the MsDexaFibro cohort presented the greatest loss in total retinal thickness. Function decreased differently among cohorts. Using biodegradable microspheres models it is possible to generate tuned neurodegeneration. These results support the multifactorial nature of glaucoma based on several noxa.

Early Diagnosis of Multiple Sclerosis Using Swept-Source Optical Coherence Tomography and Convolutional Neural Networks Trained with Data Augmentation

BACKGROUND

The aim of this paper is to implement a system to facilitate the diagnosis of multiple sclerosis (MS) in its initial stages. It does so using a convolutional neural network (CNN) to classify images captured with swept-source optical coherence tomography (SS-OCT).

METHODS

SS-OCT images from 48 control subjects and 48 recently diagnosed MS patients have been used. These images show the thicknesses (45 × 60 points) of the following structures: complete retina, retinal nerve fiber layer, two ganglion cell layers (GCL+, GCL++) and choroid. The Cohen distance is used to identify the structures and the regions within them with greatest discriminant capacity. The original database of OCT images is augmented by a deep convolutional generative adversarial network to expand the CNN's training set.

RESULTS

The retinal structures with greatest discriminant capacity are the GCL++ (44.99% of image points), complete retina (26.71%) and GCL+ (22.93%). Thresholding these images and using them as inputs to a CNN comprising two convolution modules and one classification module obtains sensitivity = specificity = 1.0.

CONCLUSIONS

Feature pre-selection and the use of a convolutional neural network may be a promising, nonharmful, low-cost, easy-to-perform and effective means of assisting the early diagnosis of MS based on SS-OCT thickness data.

Diagnosis of multiple sclerosis using multifocal ERG data feature fusion

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.

Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach-Part III: Other Biosignals

Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).