# Anxiety Detection System Based on Galvanic Skin Response Signals
## 5 Results and Discussion
They employed the publicly available WESAD and VerBIO datasets, and they applied ML methods (KNN, logistic regression, and RF) for binary classification. The results demonstrated that the RF algorithm achieved 85.7% accuracy when predicting stress levels. Their findings demonstrated the potential of using EDA data from wearable devices for stress detection, highlighting the importance of these data in analyzing mental health. [PERSON] et al. [42] proposed a stress detection system using the WESAD dataset. The data preprocessing involved splitting and normalization, which was followed by feature extraction using cvxEDA. The classification algorithms included the RF, LDA, and quadratic discriminant analysis (QDA). The RF algorithm achieved an accuracy of 78.3% for the stress detection task. Here, the classification involved two class labels, i.e., the baseline and stress, with the data sampled at a frequency of 4 Hz. As shown in Table 2, various preprocessing techniques, feature extraction methods, classification algorithms, and number of class labels were employed for stress detection using EDA signals obtained from wearable devices. The preprocessing techniques included downsampling and moving averaging in two studies, normalization in three studies, segmentation with 60 s windows and 50% overlap in two studies, upsampling and denoising in one study, data segmentation with labels assigned by a 30 s nonoverlapping sliding window and component separation in one study, and data splitting in two studies. For feature extraction, two studies utilized CNNs; three studies employed cvxEDA; two studies used EDA analysis; and one study used modality selection, NeuroKit, PyEDA toolkit, SCR features, and a low-pass Butterworth filter. The classification algorithms also varied across the studies, with three studies using KNN; two studies using naive Bayes Gaussian classifiers; five studies using RF models; three studies using SVM; two studies using CNN models; and one study using AdaBoost, 100-bagged classification trees, STAI, logistic regression, LDA, and QDA. In addition, the number of class labels varied, with two studies using neutral, stress, and amusement states; three studies using stress and no-stress states; one study using a scale from \"Not at all\" to \"Very much so\"; and one study using baseline and stress states. The frequency of the EDA signals was 5 Hz in two studies and 4 Hz in five studies. The reported accuracy results were also diverse. As shown in Table 2, one study achieved 90% accuracy using the KNN algorithm, another study achieved 92.85% accuracy for wrist EDA data, one study reported a classification accuracy of 94.8% on the training set and 90.9% on the test set with an F1-score of 0.89, one study obtained 97.03% accuracy and 97.36% precision with AdaBoost, one study achieved an F1-score of 97.03% with the naive Bayes classifier, one study reported 97% accuracy when using the naive Bayes classifier, one study achieved 85.7% accuracy with the RF algorithm, and one study obtained 78.3% accuracy with the RF algorithm using EDA. These findings demonstrate the diversity of approaches and the effectiveness of different techniques in stress detection using EDA signals.