## 4 Conclusions  
In this study, three-fold feature extraction and selection steps are proposed. In the first step, the tsfresh library is used to calculate 1578 time-series features of heart rate and respiratory rate (789 features each) signals, which are then shortlisted to 314 features after the hypothesis test. In the second stage, PCA is applied to further reduce the feature dimensions from 314 to only 19 feature components. To detect and eliminate the most correlated features with the estimated feature list, a correlation analysis (with a threshold coefficient value of \(\pm\)0.4) is performed using three different methods. The Pearson, Kendall, and Spearman correlation analysis determined the count of peaks within the respiratory rate reading to be the best and well-distinguishable feature among all other heart rate and respiratory rate-related features. For the univariate (heart rate signal) analysis, the number of CWT peaks was the most specific feature to distinguish the stress state from the baseline state. Furthermore, this study also trained and validated different supervised machine-learning classification models using the stratified 10-fold cross-validation technique. The performance of the classification models was measured in terms of classification accuracy, standard deviation (of the model's accuracy), precision, recall, f1-score, sensitivity, and specificity. The general statistical features (mean, standard deviation, median, mean absolute deviation) that have been frequently used in the literature give only an accuracy of 67.4%. The proposed correlation-based time-series feature selection algorithm resulted in more accurate classification performance compared to conventional statistical features. The time-series correlation analysed feature set, when used in conjunction with the statistical features, significantly improved the performance of the classifiers and resulted in high-stress classification accuracies; the highest being 98.6% using the KNN classifier. Future work includes the translation of the proposed algorithm as an online feature learning system for real-time scenarios. The objective will be to update the selected features based on the updated data received. This would eventually lead to a more robust and accurate stress detection system. Additionally, there is a need for dynamic thresholding for PCA, as different time-series features (like heart rate, respiratory rate, skin conductance, muscle activation, and skin temperature) have different PCA subspaces. Thus, they require the estimation of best-suited thresholding levels when applying PCA. Furthermore, a comparison of other supervised and unsupervised machine learning classification models is also the prospect of future work. Conceptualization, T.I., A.E., W.W., B.A. and A.S.; methodology, T.I., A.E. and A.S.; validation, T.I. and A.E. ; formal analysis, T.I. ; investigation, T.I. and B.A. ; writing--original draft preparation, T.I. ; writing--review and editing, A.E., W.W., A.S. and B.A. ; visualization, T.I. and B.A. ; supervision, A.E., W.W. and A.S.; project administration, W.W.; funding acquisition, W.W. All authors have read and agreed to the published version of the manuscript.