## 4 Discussion
[68] propose for the first time a _hardware_ solution that makes it possible to detect stress in real time, among four possible states--_baseline_, _stress_, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Previous Studies** & **Population (Subjects)** & **Biological Signals** & **Classifier** & **Best Accuracy (\%)** \\ \hline [PERSON] et al. (2010) [70] & 9 & BVP, RR, EEG, & Elman neural & \\  & & GSR, PPG & network & \\ [PERSON] et al. (2014) [71] & 10 & PPG (HRV), & SVM & 85.0 \\ [PERSON] et al. (2016) [72] & 12 & PPG (HRV) & SVM RBF & 94.4 \\ [PERSON] et al. (2016) [73] & 10 & PPG (HR, HRV, BR) & [PERSON] & 86.0 \\ [PERSON] et al. (2016) [74] & 18 & PPG, EDA, HRV & SVM RBF, & 94.0 \\  & & & SVM & \\ [PERSON] et al. (2017) [75] & 23 & facial rPPG, & KNN, GLR, & \\  & & facial videos & NVB, SVM & 91.68 \\ [PERSON] and [PERSON] (2019) [76] & 32 & PPG, ECG & LS-SVM & 93.0 \\ [PERSON] and [PERSON] (2020) [77] & 15 & PPG & MLPNN, DNN & 91.0 \\  & & ECG, PPG, ST, & DT, RF, AB, & \\ [PERSON] and [PERSON] (2020) [78] & 15 & RESP, EMG, EDA, & LDA, KNN, SVM, & 95.0 \\  & & ACC & ANN & \\ [PERSON] and [PERSON] (2020) [79] & 15 & ST, PPG, EDA & LR, DT, RF & 96.9 \\  & & ECG, PPG, ST, & LDA, RF, & 98.0 \\  & & RESP, EMG, EDA & SVM, ANN & \\ [PERSON] and [PERSON] (2021) [81] & 40 & EDA, PPG, & RF & 94.0 \\  & & & IBI, ST & DT, AdaBoost, RF, & \\  & & & LDA, SVM & 96.5 \\  & & & & \\ [PERSON] and [PERSON] (2022) [82] & 27 & PPG (PRV) & KNN, GA & 81.0 \\ [PERSON] et al. (2022) [68] & 15 & PPG & AE, SVM & 99.0 \\ [PERSON] et al. (2023) [83] & 32 & PPG & Threshold-based & 98.43 \\  & & & classification & \\ Our approach (2023) & 40 & PPG & CNN & 97.0 \\  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Previous work on stress detection using the PPG signal. _amusement_ and _meditation_--with an accuracy of 99%, an \(F_{1}\)-score of 99% and a sensitivity 98%. Memory requirements are not severe (\(\sim\)1.7 MB) and latency time is \(\sim\)0.4 s, with a minimum PPG signal trace of 5 s. To preserve such high performance, they require the extraction of 60 characteristics of the PPG signal. Recently, [PERSON] et al. [83] used a threshold classification method to, based on two characteristics of the PPG signal, identify a stress episode with an accuracy of 98.4%, a sensitivity of 96.87% and a specificity of 100%. However, the authors do not provide conclusive evidence on the minimum PPG signal trace, memory requirements or latency time needed to satisfy such excellent performance. The potential of our proposal resides in the fact that with a single characteristic of the PPG signal, its diffusive dynamics, which houses the integral spectrum of cardiorespiratory factors, it is feasible to detect the stress of an individual with an accuracy and \(F_{1}\)-score of \(\sim\)97% and a sensitivity also of 97% but \(\sim\)100% for the stressed state class. Its migration to portable _hardware_ operating in real time is immediate since its memory requirements are minimal (\(\sim\)2.8 MB, of the same order of magnitude, as suggested by [PERSON] et al. [68]) and it takes 4 s of PPG signal, compared to 5 s for the latter, with a latency time of \(\sim\)19-20 ms, to detect stress with virtually 100% reliability.