# Multimodal Classification of Anxiety Based on Physiological Signals
## 5 Discussion
### Comparison with Other Works  
Multiple studies have been developed in the field of anxiety and mental stress detection, either using a single signal or multi-signal approach, to attain anxiety as an induced condition using different stimuli such as driving or public speaking. Our study did not use anxiety stimuli since we relied on the data collected during a neutral affective condition. Therefore, these methods are not directly comparable to ours. However, it is important to place our results in comparison with the existing anxiety detection systems. Concerning studies conducted in real-life settings, the study [17] relies on the classifier LDA and the EDA signal to discriminate between three mental stress levels--low, medium, and high--corresponding to three driving conditions: at rest, on the highway, and city driving. The study used the MIT Media lab \"stress database\" and eighteen EDA-extracted features. The Fisher projection was applied for dimensionality reduction, and LDA was used to classify the mental stress levels, achieving a recognition rate of 81.82%. Additionally, as described in the review article [40], in research carried out by [PERSON] et al., the same driving task was used to induce mental stress. Using the signals EDA, ECG, EMG, and respiration combined, the classifier LDA reported a recognition rate of 100%, 94.7%, and 97.4% for low, medium, and high stress levels, respectively. In the work [21], regarding a programming contest scenario, heart activity, skin conductance, and accelerometer data from 21 participants were gathered to discriminate between contest stress, higher cognitive load, and relaxed time. The dataset is imbalanced, so minority examples were added and the majority ones were removed. The study evaluated six classifiers, and when combining ECG and EDA, MLP attained an accuracy of, at most, 92.15%. For the VR-based approaches introduced in Section 2, when evaluating five classifiers using features from ECG, EDA, and EMG signals, the study [22] obtained an accuracy of 89.7%. Relying on BVP, GSR, and ST signals from 30 participants collected during a VRET-based public speaking anxiety treatment session, the work [19] attained a four-level anxiety (low, mild, moderate, and high) classification. The authors used RF to select the best features. The SVM penalty parameter C was set for class balancing by adjusting weights inversely proportional to class frequencies, and the obtained accuracy was 86.3%. Concerning the laboratory-induced anxiety studies, the review work [17] analyzed mental stress and anxiety recognition approaches, mostly using multimodal binary classification, like our study, and reported accuracies ranging from 40% to 95%. [PERSON] et al. [41], who conducted both a lab and a field study, achieved a 92% mean accuracy in detecting stress based on laboratory data and 62% on field data. In the survey [21], the EDA and HR combination yielded the best results with more than 95% accuracy. While inducing stress and alleviation through several stimuli and based on a multi-modal approach with ECG, GSR, BP, respiration, and oximeter data, [PERSON] et al. [42] applied SVM and KNN for binary classification and achieved 95.8% accuracy. Attaining a binary mental stress classification task, the laboratory study by [PERSON] et al. [43], which used GSR and BVP with an SVM classifier, reported an accuracy of 80%. When compared to other studies, we can state that our results are consistent. Our study's accuracy ranges from 62.7% (LDA with Borderline SMOTE 2) to 92.0% (RF with SMOTE). Thus, even though none of these works tackled the classification problem from an imbalanced domain learning perspective as we did, we can report:  
1. compared to real-life driving and contest scenario studies, our study shows better accuracy results than the first driving study [16] but slightly worse results than the second one and the contest scenario study [21; 40]. 2. our study attained better accuracy results than both VR-based studies [19; 22].