Continuous wavelet transform analysis of ERG in patients with diabetic retinopathy

Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer

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.

Characteristics of glucose change in diabetes mellitus generalized through continuous wavelet transform processing: A preliminary study

BACKGROUND

The continuous glucose monitoring (CGM) system has become a popular evaluation tool for glucose fluctuation, providing a detailed description of glucose change patterns. We hypothesized that glucose fluctuations may contain specific information on differences in glucose change between type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM), despite similarities in change patterns, because of different etiologies. Unlike Fourier transform, continuous wavelet transform (CWT) is able to simultaneously analyze the time and fre-quency domains of oscillating data.

AIM

To investigate whether CWT can detect glucose fluctuations in T1DM.

METHODS

The 60-d and 296-d glucose fluctuation data of patients with T1DM (n = 5) and T2DM (n = 25) were evaluated respectively. Glucose data obtained every 15 min for 356 d were analyzed. Data were assessed by CWT with Morlet form (n = 7) as the mother wavelet. This methodology was employed to search for limited frequency glucose fluctuation in the daily glucose change. The frequency and enclosed area (0.02625 scalogram value) of 18 emerged signals were compared. The specificity for T1DM was evaluated through multiple regression analysis using items that demonstrated significant differences between them as explanatory variables.

RESULTS

The high frequency at midnight (median: 75 Hz, cycle time: 19 min) and middle frequency at noon (median: 45.5 Hz, cycle time: 32 min) were higher in T1DM vs T2DM (median: 73 and 44 Hz; P = 0.006 and 0.005, respectively). The area of the > 100 Hz zone at midnight to forenoon was more frequent and larger in T1DM vs T2DM. In a day, the lower frequency zone (15-35 Hz) was more frequent and the area was larger in T2DM than in T1DM. The three-dimensional scatter diagrams, which consist of the time of day, frequency, and area of each signal after CWT, revealed that high frequency signals belonging to T1DM at midnight had a loose distribution of wave cycles that were 17-24 min. Multivariate analysis revealed that the high frequency signal at midnight could characterize T1DM (odds ratio: 1.33, 95% confidence interval: 1.08-1.62; P = 0.006).

CONCLUSION

CWT might be a novel tool for differentiate glucose fluctuation of each type of diabetes mellitus using CGM data.

Optimal Combination of Mother Wavelet and AI Model for Precise Classification of Pediatric Electroretinogram Signals

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.

OculusGraphy: Signal Analysis of the Electroretinogram in a Rabbit Model of Endophthalmitis Using Discrete and Continuous Wavelet Transforms

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.

MicroRNA-150 (miR-150) and Diabetic Retinopathy: Is miR-150 Only a Biomarker or Does It Contribute to Disease Progression?

Diabetic retinopathy (DR) is a chronic disease associated with diabetes mellitus and is a leading cause of visual impairment among the working population in the US. Clinically, DR has been diagnosed and treated as a vascular complication, but it adversely impacts both neural retina and retinal vasculature. Degeneration of retinal neurons and microvasculature manifests in the diabetic retina and early stages of DR. Retinal photoreceptors undergo apoptosis shortly after the onset of diabetes, which contributes to the retinal dysfunction and microvascular complications leading to vision impairment. Chronic inflammation is a hallmark of diabetes and a contributor to cell apoptosis, and retinal photoreceptors are a major source of intraocular inflammation that contributes to vascular abnormalities in diabetes. As the levels of microRNAs (miRs) are changed in the plasma and vitreous of diabetic patients, miRs have been suggested as biomarkers to determine the progression of diabetic ocular diseases, including DR. However, few miRs have been thoroughly investigated as contributors to the pathogenesis of DR. Among these miRs, miR-150 is downregulated in diabetic patients and is an endogenous suppressor of inflammation, apoptosis, and pathological angiogenesis. In this review, how miR-150 and its downstream targets contribute to diabetes-associated retinal degeneration and pathological angiogenesis in DR are discussed. Currently, there is no effective treatment to stop or reverse diabetes-caused neural and vascular degeneration in the retina. Understanding the molecular mechanism of the pathogenesis of DR may shed light for the future development of more effective treatments for DR and other diabetes-associated ocular diseases.

Preventive Efficacy of an Antioxidant Compound on Blood Retinal Barrier Breakdown and Visual Dysfunction in Streptozotocin-Induced Diabetic Rats

In diabetic retinopathy (DR), high blood glucose drives chronic oxidative stress and inflammation that trigger alterations of the neurovascular balance finally resulting in vascular abnormalities and retinal cell death, which converge towards altered electroretinogram (ERG). In the last years, a growing body of preclinical evidence has suggested that nutrients with anti-inflammatory/antioxidant properties can be able to hamper DR progression since its very early stages. In the present study, we used a streptozotocin-induced rat model of DR, which mimics most aspects of the early stages of human DR, to test the preventive efficacy of a novel compound containing cyanidin-3-glucoside (C3G), verbascoside and zinc as nutrients with antioxidant and anti-inflammatory properties. Western blot, immunofluorescence and electroretinographic analyses demonstrated a dose-dependent inhibition of oxidative stress- and inflammation-related mechanisms, with a significant counterpart in preventing molecular mechanisms leading to DR-associated vasculopathy and its related retinal damage. Preventive efficacy of the compound on dysfunctional a- and b-waves was also demonstrated by electroretinography. The present demonstration that natural compounds, possibly as a consequence of vascular rescue following ameliorated oxidative stress and inflammation, may prevent the apoptotic cascade leading to ERG dysfunction, adds further relevance to the potential application of antioxidants as a preventive therapy to counteract DR progression.

Oscillatory Potentials in Achromatopsia as a Tool for Understanding Cone Retinal Functions

Achromatopsia (ACHM) is an inherited autosomal recessive disease lacking cone photoreceptors functions. In this study, we characterize the time-frequency representation of the full-field electroretinogram (ffERG) component oscillatory potentials (OPs), to investigate the connections between photoreceptors and the inner retinal network using ACHM as a model. Time-frequency characterization of OPs was extracted from 52 controls and 41 achromat individuals. The stimulation via ffERG was delivered under dark-adaptation (DA, 3.0 and 10.0 cd·s·m-2) to assess mixed rod-cone responses. The ffERG signal was subsequently analyzed using a continuous complex Morlet transform. Time-frequency maps of both DA conditions show the characterization of OPs, disclosing in both groups two distinct time-frequency windows (~70-100 Hz and >100 Hz) within 50 ms. Our main result indicates a significant cluster (p < 0.05) in both conditions of reduced relative power (dB) in ACHM people compared to controls, mainly at the time-frequency window >100 Hz. These results suggest that the strongly reduced but not absent activity of OPs above 100 Hz is mostly driven by cones and only in small part by rods. Thus, the lack of cone modulation of OPs gives important insights into interactions between photoreceptors and the inner retinal network and can be used as a biomarker for monitoring cone connection to the inner retina.

Protective role and molecular mechanism of action of Nesfatin-1 against high glucose-induced inflammation, oxidative stress and apoptosis in retinal epithelial cells

Diabetic retinopathy (DR) is a major complication of diabetes mellitus that may cause severe visual impairment. It has been reported that the levels of nesfatin-1 in the serum and vitreous humor were negatively correlated with DR; however, its role in DR has not been fully elucidated. Therefore, the present study was performed to investigate the effect of nesfatin-1 on high glucose-treated human retinal epithelial cells (ARPE-19) and explore the underlying mechanism. The effects of nesfatin-1 on cell viability, inflammation, oxidative stress and apoptosis were examined under high glucose conditions. The Cell Counting Kit-8 assay was used to determine cell viability. The levels of inflammatory cytokines were evaluated using ELISA kits. The reactive oxygen species and malondialdehyde content was estimated using commercial assay kits. Flow cytometry was performed to detect apoptotic cells and western blot analysis was employed to evaluate the expression of apoptosis-associated proteins. Moreover, the levels of NF-κB, NACHT, LRR and PYD domains-containing protein 3 (NLRP3) and high-mobility group protein B1 (HMGB1) were determined via western blot analysis. The results revealed that nesfatin-1 enhanced cell viability and suppressed inflammation, oxidative stress and apoptosis in the presence of high glucose concentration. Moreover, the activation of the NF-κB/NLRP3 inflammasome signaling and the expression of HMGB1 were inhibited by nesfatin-1. Furthermore, HMGB1 overexpression partially abrogated the inactivation of the NF-κB/NLRP3 inflammasome pathway caused by nesfatin-1. Taken together, these findings demonstrated that nesfatin-1 inhibited the activation of the NF-κB/NLRP3 inflammasome signaling via modulating HMGB1 and exerted a protective effect on ARPE-19 cells against high glucose-induced inflammation, oxidative stress and apoptosis.