Formation of the pediatric electroretinogram database parameters for the development of doctor’s decisionmaking algorithm

Abstract

Electroretinography is a non-invasive electrophysiological method standardized by the International Society for Clinical Electrophysiology of Vision (ISCEV). Electroretinography has been used for the clinical application and standardization of electrophysiological protocols for diagnosing the retina since 1989. Electroretinography become fundamental ophthalmological research method that may assesses the state of the retina. To transfer clinical practice to patients the establishment of standardized protocols is an important step. It is important for monitoring successful molecular therapy in retinal degeneration. Retinitis pigmentosa or achromatopsia and, consequently, affected cones or rods photoreceptors is corresponded to complete absent of electrical response. Thus, detection of even modest improvements after therapeutic treatment is required. Standardized protocols allow the implementation of electroretinography under conditions of optimization of sensitivity and specificity during clinical trials. It should be noted that the literature on retinal diseases demonstrates clinical cases in which patients may have several retinal diseases at the same time. In such cases, it is necessary to detect a group of characteristics of electrophysiological signals with high accuracy to improve the application of various diagnostic solutions. The classification of electroretinogram signals depends on the quality of labeled biomedical information or databases, in addition to this, the accuracy of the classification results obtained depends not only on computer technology, but also on the quality of the input data. To date, the analysis of electroretinogram signals is realized manually and largely depends on the experience of clinicians. The development of automated algorithms for analyzing electroretinogram signals may simplify routine processes and improve the quality of diagnosing eye diseases. This article describes the formation of the parameters of pediatric electroretinogram database parameters for the development of doctor’s decision-making algorithm. The signal parameters were obtained by extracting the parameters from the wavelet scalogram of the electroretinogram signal using digital image processing and machine learning methods.

Keywords

• electroretinography
• electroretinogram
• erg
• electrophysiological study
• eps
• wavelet analysis
• wavelet scalogram
• machine learning
• decision tree