# Estimation of Mental Distress from Photoplethysmography
## 4 Discussion
In the study, a global accuracy of 81.16% using Naive Bayes classifier was reported. Similarly, temporal, frequency and non-linear parameters on short-time HRV series, achieving a global performance of 64% using a support vector machine classifier, have been presented [41]. The same author reported an improved performance of 79% using additional non-lineal features using a complex tree classifier [42]. A global accuracy of 84.6% using exclusively RMSSD of ultra-short time HRV series and using a combination of binary tree classifiers has been reported [43]. It is mandatory to underline that these discriminant rates have been reached by combining parameters computed from different domains through advanced classifiers. Finally, there is also a number of recent works that have evaluated stress detection by using HRV in the context of long-term analysis, reporting stress detection classification rates ranging from 70 to 78% [36; 44; 45; 46; 47]. In view of these outcomes, our algorithm achieved a notably global accuracy of 82.35% using exclusively classical temporal parameters and a binary-tree classifier. Although all features were included in a multi-parametric analysis, only _SDNN_, _MNN_, _SENN_ and _RMSFD_ were chosen, showing an underlying complementarity of temporal features to classify stress patients. This interconnection is in agreement with other works previously published. Thus, a recent approach, proposed a methodology for stress detection based exclusively in the combination of time-domain features (_MNN_, _RMSSD_ and _pNN50_) achieving global classification of 74.6% [36]. Just as in our approach, the authors state that it can be efficiently implemented on mobile devices, since the proposed method only uses time-domain features. In the same line, the arousal level has been assessed on patients, reporting that temporal parameters RMSSD, PNN12 and PNN20 showed statistical significance on ultra-short temporal series of 15 s length [48]. No global precision was reported in this study. It is also interesting to notice that some recent works are exploring new areas and methodologies to enhance mental stress estimation. In this regard, some authors are computing standard and advanced non-linear analysis of HRV to recognize stress, among other emotions [49]. Unfortunately, some non-linear methodologies require a heavy computation cost, which makes the implementation of algorithms in real-time systems not viable. Furthermore, other authors have recently explored multivariate analysis to enhance distress classification. In these studies, some physiological variables like electro-dermal activity (EDA), electromyography (EMG) or skin temperature (SKT) are also combined with heart functioning to detect mental stress. Thus, multiple parameters, extracted from HRV, SKT and EDA with a support vector machine algorithm and K-means clustering to classify the obtained training data and index the user's stress level, which resulted in an overall 91.26% accuracy, were combined [44]. In the same line, a myriad of sensors, such as foot EDA, respiration, hand EDA, HR and EMG to improve the stress classification have been used [45]. Although our design is much easier and gets reasonable performance results with one single sensor, the aforementioned potential areas of research may benefit from the occurrence of new metrics and methodologies for mental distress estimation. Finally, some comments about the suitability of PRV metric as substitute of HRV deserve consideration. At this point, several works have been published during last years, discussing if PRV series computed from PPG signals can be used instead of HRV computed from ECG recordings. Throughout the last years, some studies have claimed that PRV is a surrogate for HRV [50; 51; 52]. Furthermore, some recent works deserve a special mention, as they not only claim that PRV is suitable for HRV analysis, but they also provide details about which parameters extracted from HRV are most prone to show errors comparing both ECG and PPG methodologies.