# Advanced Analysis of Electoretinograms Based on Wavelet Scalogram Processing
## 4 Statistical Analysis of the Electroretinogram Signal Database
### Adults Group  
For the adults group, 38 signals were used for the analysis. We diagnosed 11 as healthy and 27 as subjects with pathology among them. Table 3 presents the values of amplitudes and latencies for \(a\) and \(b\) waves. According to the ISCEV Standard we present median values, standard deviation (STD), 5-th and 95-th quantile (Q5% and Q95%). The data  
Figure 5: Boxplots of amplitudes and latencies for \(a\)- and \(b\)-waves: (**a**) pediatric group; (**b**) adult group.  
For the WA1-2 features set, the following four features were considered as being important (the number denotes the segment number of the wavelet scalogram): \(B_{max}1\), \(A_{median}1\), \(t_{1}2\), \(f_{1_{0}0}1\).  
For the WA1-4 features set, the following eight features were considered as being important: \(A_{median}1\), \(A_{mean}1\), \(A_{median}3\), \(A_{mean}3\), \(f_{1}3\), \(t4\), \(t_{2}4\), \(t_{2_{0}0}4\).  
For the CF+WA1-4 features set, the following four features were considered as being important: \(l_{a}\), \(A_{median}3\), \(A_{mean}3\), \(t4\).  
Judging from the data in Table 4, it can be concluded that the proposed new WA features seem to provide more robust and consistent models compared to the Classical features used.  
Analyzing the lists of important features, it is possible to conclude: the WA features of segments 1 and 2 seem to have a higher classification \"power\" than the WA features of segments 3 and 4. At the same time, the Classical Features seem to better complement the WA features of segments 3 and 4.  
Table 5 shows the values of the classification metrics for the adults group.  
### Adults Group  
For the CF features set, the following three features were considered as being important: \(a\), \(b\), and \(l_{b}\).  
For the WA1-2 features set, the following two features were considered as being important: \(t_{1_{0}}1\), and \(t_{2}\).  
For the WA1-4 features set, the following two features were considered as being important: \(t_{1_{0}}1\), \(t_{2_{0}0}2\).  
For the CF+WA1-4 features set, the following five features were considered as being important: \(l_{b}\), \(t_{1}\), \(t_{1_{0}0}1\), \(t_{2}\), \(A_{median}2\).  
Judging from the data in Table 5, it can be concluded that the proposed new WA features seem to provide more robust and consistent models compared to the Classical features used. Analyzing the lists of important features, it is possible to conclude: the WA features of segment 1 and 2 seem to have the highest classification \"power\". Interestingly, the joined features set has no WA features of segments 3 and 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Features Set** & **Metric** & **Accuracy** & **F1** & **Precision** & **Recall** \\ \hline \multirow{3}{*}{CF} & Mean & 0.52 & 0.52 & 0.72 & 0.52 \\  & STD & 0.06 & 0.18 & 0.19 & 0.32 \\ \multirow{3}{*}{WA1–4} & Mean & 0.70 & 0.74 & 0.78 & 0.76 \\  & STD & 0.18 & 0.15 & 0.17 & 0.24 \\ \multirow{3}{*}{WA1–4} & Mean & 0.60 & 0.64 & 0.70 & 0.61 \\  & STD & 0.06 & 0.05 & 0.08 & 0.12 \\ \multirow{3}{*}{CF+WA1–4} & Mean & 0.58 & 0.65 & 0.66 & 0.66 \\  & STD & 0.10 & 0.07 & 0.07 & 0.10 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the classical and wavelet feature sets for pediatric group classification.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Features Set** & **Metric** & **Accuracy** & **F1** & **Precision** & **Recall** \\ \hline \multirow{3}{*}{CF} & Mean & 0.54 & 0.62 & 0.55 & 0.73 \\  & STD & 0.24 & 0.35 & 0.31 & 0.43 \\ \multirow{3}{*}{WA1–4} & Mean & 0.78 & 0.84 & 0.86 & 0.84 \\  & STD & 0.23 & 0.17 & 0.19 & 0.16 \\ \multirow{3}{*}{WA1–4} & Mean & 0.81 & 0.86 & 0.87 & 0.88 \\  & STD & 0.20 & 0.14 & 0.17 & 0.17 \\ \multirow{3}{*}{CF+WA1–4} & Mean & 0.83 & 0.88 & 0.89 & 0.88 \\  & STD & 0.10 & 0.07 & 0.07 & 0.10 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the classical and wavelet feature sets for adults group classification.