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

Zhdanov A, Constable P, Manjur SM, Dolganov A, Posada-Quintero HF, Lizunov A (2023)

Publication Type: Journal article

Publication year: 2023

Journal

Bioengineering MDPI

Book Volume: 10

Article Number: 708

Journal Issue: 6

DOI: 10.3390/bioengineering10060708

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.

Authors with CRIS profile

Aleksei Zhdanov Machine Learning and Data Analytics Lab

Involved external institutions

University of Connecticut US United States (USA) (US) Flinders University AU Australia (AU) Ural Federal University B.N. Yeltsin RU Russian Federation (RU) Intersectoral Research and Technology Complex Eye Microsurgery / S. Fyodorov Eye Microsurgery Federal State Institution / Межотраслевой научно-технический комплекс «Микрохирургия глаза» RU Russian Federation (RU)

How to cite

APA:

Zhdanov, A., Constable, P., Manjur, S.M., Dolganov, A., Posada-Quintero, H.F., & Lizunov, A. (2023). OculusGraphy: Signal Analysis of the Electroretinogram in a Rabbit Model of Endophthalmitis Using Discrete and Continuous Wavelet Transforms. Bioengineering, 10(6). https://dx.doi.org/10.3390/bioengineering10060708

MLA:

Zhdanov, Aleksei, et al. "OculusGraphy: Signal Analysis of the Electroretinogram in a Rabbit Model of Endophthalmitis Using Discrete and Continuous Wavelet Transforms." Bioengineering 10.6 (2023).

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