# Analysis of Facial Cues for Cognitive Decline Detection Using In-the-Wild Data
## 4 Experiments
### Performance Using Eye Blink Features
#### 4.1.2 Experiment 2: Extending Classification Evaluation Using Optimal SD and IQR Parameters
The table shows that the IQR approach achieves a better performance on most classification tasks that detect people with MPs (ND/MCI/FMD) and the four-way classification. The difference between the SD and IQR results is considered to be statistically significant. However, the SD approach shows a higher performance when any class of people with MPs is classified from HCs. MCI can be distinguished from HCs better than the ND and FMD groups. The extracted statistical features of eye blink rate (EBR) from the IVA data can provide valuable insights into behavioral differences between these groups. To investigate this, we conducted a series of statistical significance tests across the ND, MCI, FMD, and HC groups, treated as binary comparisons (ND vs. MCI, ND vs. FMD, MCI vs. FMD, ND vs. HC, MCI vs. HC and FMD vs. HC). First, a normality test was applied to determine whether the features followed a normal distribution. Based on the result, a parametric two-tailed \(t\)-test was used for normally distributed features, while a non-parametric Wilcoxon test was used for features that did not meet the normality assumption. A significance level of \(p=0.05\) was used for all tests. Figure 12 shows the number of statistically significant eye blink rate (EBR) thresholds for each group pair using the two methods: IQR and SD. Each bar represents the count of thresholds that showed significant differences between groups of memory-related problems (ND, MCI, and FMD) and HC. As mentioned previously, the multiple thresholds approach was developed to be participant-dependent (PD), where a range of thresholds is calculated based on the individual minimum and maximum EAR values for each participant. From the chart, it can be seen that some group pairs, such as MCI vs. HC and FMD vs. HC have higher significant thresholds than other group pairs (e.g., ND vs. MCI, ND vs. FMD, and ND vs. HC). This indicates that the differences between these groups are more consistent and noticeable across a wider range of EBR values, suggesting that the EBR feature is more sensitive and reliable for distinguishing between them. Figure 11: The confusion matrices for the two-way classifications—neurodegenerative disorder (ND) vs. healthy controls (HCs), mild cognitive impairment (MCI) vs. healthy controls (HCs), and functional memory disorder (FMD) vs. healthy controls (HCs)—for both the standard deviation (SD) and interquartile range (IQR) with their default factors and the ones with the highest performance, using 70 features or thresholds. In the confusion matrix, darker colors indicate higher true predicted values, while lighter colors indicate lower predictions. (rows: true labels; columns: classified labels). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**ND vs. HC**} & \multicolumn{4}{c}{**MCI vs. HC**} & \multicolumn{4}{c}{**FMD vs. HC**} \\ \hline
**Factor** & **Approach** & **Accuracy** & **P** & **R** & **F1** & **Accuracy** & **P** & **R** & **F1** & **Accuracy** & **P** & **R** & **F1** \\ \hline \multirow{2}{*}{a,b = 1.5} & SD (Min-UB) & **69\%** & **70\%** & **69\%** & **69\%** & **89\%** & **89\%** & **89\%** & **89\%** & **77\%** & **78\%** & **76\%** & **76\%** \\  & IQR & 69\% & 67\% & 66\% & 65\% & 72\% & 83\% & 72\% & 71\% & 67\% & 65\% & 65\% & 65\% \\ \hline \multirow{2}{*}{a = 3, b = 2} & SD (Min-UB) & **78\%** & **81\%** & **79\%** & **79\%** & **78\%** & **80\%** & **79\%** & **77\%** & **77\%** & **76\%** & **76\%** \\  & IQR & 72\% & 71\% & 71\% & 70\% & 72\% & 83\% & 72\% & 71\% & 67\% & 65\% & 64\% & 64\% \\ \hline \hline \end{tabular}
\end{table}