## 3 Results
### Model Interpretation Using SHAP  
We employed the SHAP technique to rank the marginal contribution of the physiological variables to the XGBoost model for APS detection. The marginal contribution of each feature is measured by calculating the mean of the absolute SHAP values per feature across the testing data. Figure 3 shows the beeswarm SHAP summary plot, which shows the feature importance in descending order on the vertical axis. The plot indicates that GSR is the most important signal, HR is the second most important, and ST is the third most important, followed by the ACC \(X\)-axis, ACC \(Y\)-axis, and ACC \(Z\)-axis. Each dot on the feature row represents the SHAP value of a sample in the testing data. Dark navy-colored dots represent high biosignal feature values, while yellow dots represent low biosignal feature values. To visualize the distribution, overlapping points are jittered in the \(Y\)-axis direction. Figure 3 shows that the increases in GSR and HR values are positively correlated with the occurrence of APS, as indicated by high positive SHAP values. The decrease in ST is positively correlated with APS, while accelerometer signals have the least contribution to the APS detection model. Acceleration in the direction of the positive \(X\)-axis, negative \(Y\)-axis, and negative \(Z\)-axis are correlated with APS. Acceleration in the direction of the positive \(X\)-axis can be attributed to lateral motions of the left arm moving to the right, such as when crossing the arms over the chest. Similarly, acceleration in the direction of the negative \(Y\)-axis captures movements of the hand moving backward or towards the body. Lastly, the negative \(Z\)-axis records the lowering of the hand or arm downwards. The SHAP technique can provide an interpretation of model predictions for individual instances. Figure 4 shows the force plot for a correctly classified APS instance. The contribution of each physiological signal to the prediction is indicated by its SHAP value, represented as a horizontal arrow. The length of the arrow indicates the magnitude of the SHAP value, with red arrows indicating positive SHAP values and blue arrows indicating negative SHAP values. The mean standardized values of the biosignals are shown under the arrows. In this correctly classified APS instance, the GSR, HR, ST, ACC \(Z\)-axis, and \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **APS** & **NS** \\ \hline Precision (\%) & 99.95 & 99.83 \\ Recall (\%) & 99.97 & 99.66 \\ F1-score (\%) & 99.96 & 99.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Precision, recall, and F1 score for APS classification XGBoost model. Figure 2: Confusion matrix for APS classification XGBoost model. ACC \(Y\)-axis all have positive SHAP values, while the ACC \(X\)-axis has a negative SHAP value. The base value, or the expected value, represents the average model prediction over the entire training dataset, which equals 10.46. The sum of all SHAP values for this instance (2.87 for GSR, 1.99 for HR, 1.05 for ACC \(Y\)-axis, 0.81 for ST, 0.75 for ACC \(Z\)-axis, and \(-\)0.62 for ACC \(X\)-axis) combined with the expected base value of 10.46 equals 17.31. The sum of all SHAP values for this instance combined with the expected base value is higher than the base value alone, indicating that the sum of all signal contributions positively affects the model's APS prediction for this instance. The positive standardized mean GSR value of 2.037 has the most significant positive effect on the model's APS prediction, with a SHAP value of 2.87. The positive standardized mean HR value of 1.081 has the second-most significant positive effect, with a SHAP value of 1.99. Meanwhile, the negative standardized mean ACC \(Y\)-axis value of \(-\)1.989 has the third most significant effect, contributing positively to the model's APS prediction with a SHAP value of 1.05. Figure 4: Force plot for correctly classified APS instance. Figure 3: Beeswarm SHAP summary plot for the XGboost APS model.