# 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

Table 6: Classification results of 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)—using two factors for standard deviation (SD), a = 3 as the default value, and a = 1.5 as the factor with the highest performance, and for interquartile range (IQR), b = 1.5 as the default value, and b = 2 as the factor with the highest performance. These approaches are tested on 70 thresholds or features using linear SVM (P: precision; R: recall; F1: f-measure). The IQR-based EBR values show statistically significant differences between groups with different memory problems (e.g., ND vs. MCI, ND vs. FMD), but not as consistently between memory problems and healthy controls. This suggests that IQR captures subtle, intra-clinical group differences in EBR behavior, making it more potentially effective for distinguishing between the ND, MCI, and FMD classes. In contrast, SD-based EBR features demonstrate stronger statistical differences between each memory problem group and the healthy control (HC) group (e.g., ND vs. HC, MCI vs. HC, FMD vs. HC), with many comparisons yielding extremely significant \(p\)-values. This supports the observation that using the SD-based method is more effective for tasks that require distinguishing participants with memory problems from healthy controls groups. These findings confirm and strengthen our earlier interpretation that both the IQR and SD methods serve different yet complementary purposes in eye blink rate (EBR) analysis. The IQR method, by capturing small variations in behavior, performs better when distinguishing between individuals with different memory problems (ND, MCI, FMD). In contrast, the SD method is more effective for differentiating individuals with memory problems from healthy controls, as it reflects larger behavioral differences. This distinction likely arises from varying data characteristics, including recording environments, EAR \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification Task** & **Best IQR or SD?** & \(p\)**-Value** \\ \hline ND vs. MCI vs. FMD vs. HC & IQR & 0.04 * \\ ND vs. MCI vs. FMD & IQR & 0.02 * \\ ND vs. MCI & IQR & 0.003 ** \\ ND vs. FMD & IQR & 0.02 * \\ MCI vs. FMD & IQR & 0.03 * \\ \hline ND vs. HC & SD & 0.002 * \\ MCI vs. HC & SD & 0.01 * \\ FMD vs. HC & SD & 0.0004 ** \\ MP vs. HC & SD & 0.0001 ** \\ D vs. Non-D & IQR & 0.0001 ** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: All classification tasks and the approach to upper boundary (UB) determination that achieved the highest performance in the \(t\)-test to show the significant difference between the highest results obtained using the standard deviation (SD) and interquartile range (IQR) (*: statistically significant; **:** extremely statistically significant). Figure 12: An illustration of the number of statistically significant eye blink rate (EBR) thresholds identified using standard deviation (SD) and interquartile range (IQR) across different group pairs. range shifts, and sample size. In summary, the IQR method is more effective for spotting subtle differences within memory-impaired groups, while the SD method is better suited for distinguishing individuals with memory problems from healthy controls. Future studies should be conducted on larger datasets to further validate these findings.