# Discrimination of Severity of Alzheimer's Disease with Multiscale Entropy Analysis of EEG Dynamics
## 3 Results
### AD Severity Index Obtained by Training HC and AD2 Groups  
Table 2 illustrates the optimal training F1 scores in differentiating the HC and AD2 groups among the 19 electrodes for each of the 1S-5S cases, respectively. The corresponding MSE scales, specificities, recalls, precisions, and accuracies are also illustrated. Table 3 further shows the weighted sum models for the 2S-5S cases, respectively. Note that the differentiating performance is the same as the performance of LOOCV listed in Table 1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Electrode** & **Case** & **1S** & **2S** & **3S** & **4S** & **5S** \\ \hline Fp1 & 0.710 & 0.774 & 0.903 & 0.903 & 0.933 \\ Fp2 & 0.733 & 0.759 & 0.815 & 0.857 & 0.857 \\ F7 & 0.710 & 0.828 & 0.933 & 0.938 & 0.938 \\ F3 & 0.710 & 0.800 & 0.889 & 0.889 & 0.889 \\ Fz & 0.710 & 0.815 & 0.903 & 0.903 & 0.903 \\ F4 & 0.714 & 0.800 & 0.828 & 0.897 & 0.897 \\ F8 & 0.750 & 0.897 & 0.933 & 0.966 & 0.966 \\ T3 & 0.733 & 0.786 & 0.867 & 0.867 & 0.857 \\ C3 & 0.741 & 0.800 & 0.867 & 0.875 & 0.897 \\ Cz & 0.750 & 0.774 & 0.867 & 0.839 & 0.857 \\ F4 & 0.714 & 0.800 & 0.828 & 0.897 & 0.897 \\ F8 & 0.750 & 0.897 & 0.933 & 0.966 & 0.966 \\ T3 & 0.733 & 0.786 & 0.867 & 0.867 & 0.857 \\ C3 & 0.741 & 0.800 & 0.867 & 0.875 & 0.897 \\ Cz & 0.750 & 0.774 & 0.867 & 0.839 & 0.857 \\ C4 & 0.800 & 0.867 & 0.867 & 0.897 & 0.933 \\ T4 & 0.786 & **0.938** & **0.966** & **0.968** & **1.000** \\ T5 & **0.828** & 0.903 & 0.903 & 0.903 & 0.933 \\ P3 & **0.828** & 0.933 & 0.966 & 0.966 & 0.966 \\ Pz & 0.786 & 0.897 & 0.903 & 0.903 & 0.903 \\ P4 & 0.759 & 0.903 & 0.933 & 0.933 & 0.968 \\ T6 & 0.621 & 0.750 & 0.750 & 0.774 & 0.774 \\ O1 & 0.800 & 0.897 & 0.897 & 0.897 & 0.897 \\ O2 & 0.667 & 0.690 & 0.667 & 0.667 & 0.645 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Optimal F1 scores of LOOCV in differentiating subjects into the HC and AD2 groups of each electrode obtained using 1–5 MSE scales (labeled as 1S–5S), respectively. Bold highlights the best performances. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Case** & **Electrode** & **Scales** & **F1 Score** & **Accuracy** & **Recall** & **Precision** & **Specificity** \\ \hline
1S & T5; P3 & [12]; [19] & 0.828 & 0.833 & 0.813 & 0.867 & 0.800 \\
2S & T4 & [6, 15] & 0.938 & 0.933 & 0.882 & 1.000 & 0.867 \\
3S & T4 & [2, 3, 14] & 0.966 & 0.967 & 1.000 & 0.933 & 1.000 \\
4S & T4 & [6, 9, 12, 17] & 0.968 & 0.967 & 0.938 & 1.000 & 0.933 \\
5S & T4 & [2, 8, 11, 15, 16] & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Optimal F1 score and corresponding accuracy, precision, recall, specificity, selected electrode, and scales for each of the 1S–5S cases, respectively. Figure 1 illustrates the optimal training result of the 2S case listed in Table 3. Figure 1a illustrates the MSE (\(\tau=6\)) vs. the MSE (\(\tau=15\)) of the 30 EEG signals recorded from the T4 electrode. The 30 signals are from 15 HC and 15 AD2 subjects. Figure 1b illustrates the weighted sum values of the 30 signals. The red dashed line is the differentiating boundary with threshold value 0; the data points above the red line with positive values are differentiated into the healthy group and the data points below the red line with negative values are differentiated into the AD group. In other words, the absolute value of the weighted sum value of each data point shown in Figure 1b is the distance between the corresponding point and the boundary line shown in Figure 1a. Note that the differentiating performance is the same as the performance of LOOCV listed in Table 1.  
Figure 2 illustrates the optimal training results of the 3S-5S cases listed in Table 3. Note that the differentiating performances are the same as the performances of LOOCV method listed in Table 1.  
Table 4 further compares the means \(\pm\) SDs of the weighted sum values among the HC, AD1, and AD2 groups for each of the 2S-5S cases.