# Advanced Analysis of Electoretinograms Based on Wavelet Scalogram Processing
## 1 Introduction  
Biomedical research is a multidisciplinary area of medicine, biology, informatics, and engineering. The results of the research in this area may provide for the development of new methods in the diagnostics and treatments of various diseases [1]. The present study describes the use of biomedical ophthalmic signals for the diagnosis of retinal diseases. The non-invasive ocular test called electoretinogram (ERG) evaluates the retinal function by measuring the electrical responses of the retina cells that are generated by a light stimulus. ERG consists of several responses, namely, the photoreceptors in the outer retina, inner retinal layers, and the final output neurons [2]. ERG is mostly used for the diagnostic assessment of toxic retinopathies, diabetic retinopathies, hereditary diseases, etc. The phenomenon of an electrical potential of a living entity's eye under a light stimulus action was found by a Swedish physician [PERSON] in 1865. This was the background for the scientific discovery of ERG. Further studies of [PERSON], [PERSON], [PERSON], and [PERSON] identified ERG signals components that were conditioned by physiological responses [3, 4, 5, 6]. The modern concept of ERG analysis involves the measurements of a- and b-waves. An a-wave is characterized as being the first large negative component associated with the photoreceptor response in the outer retina. A b-wave describes a positive component that is associated with the inner layers response of the retina [7; 8; 9; 10; 11]. The ERG technique has a great potential for early disease detection, diagnosis, and interventions in the field of ophthalmology. Another popular technique is the optical coherence tomography (OCT) test. The ERG can be a useful addition for child patients and those who are cognitively or physically disabled, or who in some other way cannot participate. In addition, it can reflect the retinal lesion several years before visual symptomatology or structural damage are detected by OCT. In recent years, the ocular toxicity method has obtained an increased popularity in the drug therapy assessment of visual impairment, where the ERG technique has shown good prospects in the retinal toxicity test [12; 13]. Manual ERG analysis is, however, highly dependent on the experience of the clinician, as a misdiagnosis might mean that the patient misses the optimal time for treatment. An automated algorithm will be a powerful tool in ERG signal analysis, but it requires large databases for verification and validation. The strong motivation to understand in more detail the ERG signals start at first glance with building a signal's database, which includes adults and children. In this way, waveforms can be analyzed in terms of a physiological process and compared according to waveform morphology. The novelty of the current study relates to the processing of pediatric signals, which, to the best of our knowledge, has a limited description in the scientific literature. Additionally, we propose an approach to wavelet feature extraction that improves upon the accuracy of both adult and pediatric diagnosis classification. The limitations of the work: The work provides initial study results on a rather limited database. In the next step, the database will be assumed to be exceeding. The paper includes the comparison of the classical existent approach using four features and our one, which extends the results leading to classification accuracy improvement. To extract the parameters, the cwt function of the PyWT library is used, where the Gaussian wavelet of the eighth order was chosen as the basic function. The rest of the paper is organized as follows: Section 2 discusses the advantages and disadvantages of a variety of signal processing methods. Section 3 presents our ERG signals database, as well as a pipeline for signals processing and feature extraction using classical approach and wavelet transform. In Section 4, we present the statistical summary for the classical ERG features in accordance with the ISCEV standard [14].