# Anxiety Detection System Based on Galvanic Skin Response Signals
## 5 Results and Discussion
[38] proposed Stressalyzer, a stress classification and personalization framework utilizing the WESAD dataset and ML models. This framework encompassed data preprocessing, which involved segmenting the data into 60 s windows with 50% overlap and normalization using min-max scaling. Feature extraction was performed through convolutional operations between the input and a weight matrix or kernel to capture complex features by learning smaller and simpler features. In addition, classification was performed using CNNs. The results demonstrated excellent performance of the single-channel neural network-based model, with the CNN achieving an accuracy of 92.85% and an F1-score of 0.89 for binary stress classification. [PERSON] et al. [39] proposed a stress detection and classification framework using a CNN without performing feature computation. In the CNN, the convolution operation was applied between the input and weight matrices or filters to extract complex features by learning smaller and simpler features progressively. Here, a one-dimensional CNN was deemed suitable for their stress detection and classification approach; thus, it was employed as the learning algorithm in their study. The trained CNN model achieved a classification accuracy of 94.8% on the training set and 90.9% on the test set for bi-affective state classification, i.e., stress vs. nonstress classification. In addition, an F1-score of 0.89 was achieved. [PERSON] et al. [40] developed a framework for stress detection using the GSR signals in the WESAD dataset and ML algorithms. They extracted 87 features from the EDA signal, applied normalization per person, and selected the most informative features. Three ML \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Preprocessing** & **Feature Extraction** & **Classifier** & **\# of Classes** & **Accuracy** \\ \hline [PERSON] et al., 2020 [11] & Downsampling, moving averaging, low-pass Butterworth filter, and normalization & Statistical features (cvxEDA), e.g., phasic data, onset and offset, and peaks & KNN & 3 Neutral, stress, amusement & 90\% \\ \hline [PERSON] et al., 2022 [38] & Segmentation and normalization (min-max) & Computing the dot product of the kernel creating different layers & CNN & 3 Normal, stress, normal, stress, amusement & 92.85\% \\ \hline [PERSON] et al., 2021 [39] & Segmentation (60 s overlap of 50%) and normalization (min-max) & N/A & CNN & 2 Stress, no stress & 90.9\% \\ \hline [PERSON] et al., 2022 [40] & Upsampling and denoising & cvxEDA algorithm, NeuroKit & Adaboost & 2 Stress, no stress & 97.03\% \\ \hline [PERSON] et al., 2021 [35] & Downsampling and moving averaging & pyEDA toolkit & Naïve Bayes & 2 Stress, no stress & 97\% \\ \hline [PERSON] et al., 2022 [41] & Data segmentation, component separation, data splitting & Statistical features and SCR features & RF & 4 & 4 \\ \hline [PERSON] et al., 2019 [42] & Splitting data, normalization & cvxEDA & RF & 1 = ”Not at all” to 4 = ”Very much” & 85.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative analysis of related research using the same dataset. algorithms (i.e., the AdaBoost, RF, and SVM algorithms) were evaluated, and the results demonstrated a stress detection accuracy of 97.03% using the leave-one-out method. [PERSON] et al. [35] developed an open-source Python toolkit for EDA signal preprocessing and automatic feature extraction. They evaluated the toolkit using various ML algorithms on the WESAD dataset. The results exhibited higher accuracy for stress detection using automatically extracted features. They trained two models for each algorithm, i.e., one model using statistical features and the other model using automatic features. The best accuracy of 97% was achieved by the naive Bayes classifier using the second model with automatic features. [PERSON] et al. [41] conducted a feasibility study on stress detection using GRS signals.