## 4 Discussion  
It has been shown that a depressive state in an individual can be identified with a quantifiable degree of precision, thus addressing the need to objectively combat depression for the benefit of both patients and healthcare professionals. This was accomplished through statistical analyzes that elucidated distinct characteristics that exhibited differential behavior between various groups in response to disparate physical stimuli. In particular, when the data were processed through a machine learning classification system with two types of validation techniques, a correct identification rate of depressive states was achieved in up to 92% of cases using a stratified cross-validation method. In this study, the best results were obtained using the stratified validation method \(k\) -fold rather than the split train test, with metrics exceeding 0.9. Therefore, the discussion will focus on evaluating these superior outcomes. Consequently, it can be substantiated that ECG and PPG signals, particularly the derived characteristics that have the highest impact on classification, can contribute to the diagnostic process or serve as corroborative evidence to assess whether an individual is experiencing depression or a depressive episode. This methodology offers an ancillary layer of support predicated on the individual's biophysical data. There is a pronounced improvement in the performance of the machine learning algorithm when the sample size is increased. This is attributable to the markedly superior performance of the selected model in the more extensive dataset compared to the other two, which contained only half the amount of data. Although we achieved an accuracy rate of up to 92% with the emotional stimuli dataset, the 88% accuracy yielded by the combined dataset is likely to be more reliable due to the increased volume of information it provides. In most models, \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Feature Name** & **Mean** & **Deviation** & \(p\)**-Value** \\ \hline PCRES & 0.134 & 0.051 & 0.168 \\ PCHRV & 0.188 & 0.083 & 0.174 \\ PCTTP & 0.193 & 0.104 & **<0.001** \\ IDCG & 0.182 & 0.038 & 0.335 \\ HRVM & 0.803 & 0.131 & 0.660 \\ TTPM & 0.606 & 0.031 & 0.931 \\ FCM & 74.664 & 12.131 & 0.721 \\ DEHRV & 0.086 & 0.078 & 0.916 \\ DETTP & 0.013 & 0.009 & 0.101 \\ CVECG & 56.093 & 14.502 & 0.156 \\ CVPPG & 56.917 & 9.452 & 0.319 \\ POHRV & 32.592 & 15.795 & 0.143 \\ P07 TP & 39.583 & 17.809 & 0.258 \\ PORES & 50.330 & 13.373 & 0.364 \\ SHAHRV & 13.413 & 0.350 & 0.176 \\ SHATTP & 13.408 & 0.349 & 0.081 \\ SHARES & 13.426 & 0.351 & 0.169 \\ SCHRV & 0.156 & 0.025 & **0.021** \\ SCTTP & 0.170 & 0.029 & **0.013** \\ SCRES & 0.196 & 0.019 & 0.751 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Hypothesis test results of the Mann–Whitney U test are shown for each variable, displaying the values of the mean, standard deviation, and \(p\)-value. The values that rejected the null hypothesis have been highlighted in bold. algorithms trained on limited data frequently exhibit deficient generalization capabilities during validation, likely due to the insufficiency of available information. Compared with other authors and the state-of-the-art methods, it was found that the proposed method, with the reported values, shows results equivalent to most similar methodologies. For example, [PERSON] et al. (2020) reported an accuracy of 86% with a Bayesian classifier, analyzing only non-linear features in the time and frequency domains of HRV. Our results are comparable, achieving higher levels of accuracy even with a similar number of features and fewer test subjects. Furthermore, our results can be compared to those of [PERSON] et al. (2020), who achieved results greater than 72% for binary classification in depression using cardiac variability characteristics and an SVM model with a greater set of features and volunteers than ours. Furthermore, in another study by [PERSON] et al. (2020), metrics exceeding 70% were achieved using only the entropy characteristics in HRV.