# Advanced Analysis of Electoretinograms Based on Wavelet Scalogram Processing
## 2 Related Work  
The ERG signal is a complex signal that shows the electrical activity of retinal cells after stimulation. It should be noted that the ERG is a short signal containing many spectral components. ERG is high frequency (2 kHz) and short (200 ms); therefore, the signal spectrum is in the range of 0 to 1 kHz, the spectrum sampling is 7.5 Hz, and useful information is distributed non-linearly and may appear in a narrower range. Currently, the ERG is evaluated using the amplitude and latency of the known waves in this signal.  
The authors of article [15] described all the possible methods for investigating ERG signals that are found in the scientific literature, including a comparative analysis of the methods, their advantages, and disadvantages. In this paper, we focus on the time-frequency-specific methods for ERG data processing.  
The current scientific literature presents studies of ERG analysis in the frequency domain, mainly based on the Fast Fourier transform (FFT), power spectral density (PSD), and linear prediction (LP) [16]. Scientific articles show a variety of research methods in the field of diagnosing diseases of the retina, but the results are difficult to generalize due to differences in ERG protocols. The methods presented in the articles demonstrate the accuracy of the analysis in the time domain. Some studies are focused on the frequency in domain analysis of the oscillatory potential, since this signal has a shorter amplitude than other ERG components.  
Obviously, the disadvantage of the FFT method is the lack of temporal localization, which means that the power spectrum density cannot provide information about the specific frequencies of the signal. To solve this problem, Short-Time Fourier Transform (STFT) was proposed to analyze small sections of the signal using a window [17]. Thus, STFT served as the basis for time-frequency analysis.  
It is known from studies that a smaller window size leads to an improved temporal resolution and reduces the number of discrete frequency components. Thus, the wavelet analysis (WA) provides full analysis potential for the discrimination of the ERG components.  
The use of WA demonstrates some advantages over other frequency-domain methods because it has a different window size that is more appropriate for analyzing sudden and short-term signal changes. Most articles about ERG analysis in the time-frequency domain describe the use of continuous wavelet transform (CWT) and discrete wavelet transform (DWT) [18]. The first wavelet type requires more computation, resulting in a slower processing time compared to a discrete one. It is possible to lose any information if the correct level of decomposition is not chosen while using DWT. When analyzing WA, two important factors should be considered, namely the wavelet type and decomposition. Other papers that describe time-frequency domain analysis are also based on the DWT technique. Both the CWT and DWT methods have their specific advantages and disadvantages. CWT is more reliable than DWT because it can extract all of the available information without down sampling. However, CWT requires more computation, resulting in a slower process compared to DWT [19].  
Moreover, CWT has the property of being highly redundant, which is beneficial from one point of view and a curse from another. Because of it, no information is lost, unlike DWT. In DWT, it is possible to lose some useful information if the correct level of decomposition is not chosen. When analyzing WA, two important factors should be considered; they are the wavelet type and decomposition. A careful evaluation of the feature extraction method and reference database is required in order to extract the information needed from the ERG signals using the above-mentioned methods.