# Anxiety Detection System Based on Galvanic Skin Response Signals
## 4 Experimental Design
### Materials and Methods  
In this study, we used the publicly available wearable stress and affect detection (WESAD) dataset (Table 1). The WESAD dataset was designed for stress and affect detection through data obtained from wearable devices, including physiological and emotional data collected using the professional Empatica E4 (wrist-worn) and RespiBAN (chest-worn) devices.  
The data included different types of signals: ECG, blood volume pulse, electromyogram, respiration, skin temperature, three-axis accelerometer, HR, and EDA. The dataset was collected from 15 subjects (12 males and 3 females) in a laboratory setting. The purpose of using the WESAD dataset was to evoke and classify three affective states, i.e., the baseline or normal state (neutral reading), a stress state (exposed to the Tier Social Stress Test (TSST)), and an amusement state (watching funny videos) [34].  
The WESAD dataset was selected in this study due to its extensive usage in over 600 research papers, indicating its widespread acceptance in the academic community. The reliability of the dataset is attributed to the inclusion of high-quality data that are well-labeled and defined, which established it as a valuable resource for testing our anxiety detection method [34].  
Previous studies focused on utilizing multiple signals using different physiological readings, which were used for classification. In the current study, we focused on using only the EDA data obtained from wrist-worn devices.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Anxiety Model** & **Three Affective States (Neutral, Stress, and Amusement)** \\ \hline Stimuli & Two affective stimuli (stress and amusement) were applied. In addition, a baseline and two meditation conditions (introduced to calm the participants after a stimulus) were set. \\ \hline Task & Three tasks (neutral reading of materials (magazines) to trigger the baseline state, watching funny videos to trigger the amusement state, and exposure to the TSST to trigger the stress state) were set. The TSST induces stress by requiring participants to speak publicly and perform complex mental arithmetic. \\ \hline Subjects & Fifteen subjects (12 males and 3 females) with a mean age of 27.5 \(\pm\) 2.4 years participated in the study. \\ \hline Time & Study duration: approximately 2 h. \\ \hline EDA devices & Empatica E4 (wrist-worn) and RespiBAN professional (chest-worn) devices. \\ \hline \multirow{6}{*}{Experimental protocol} & The baseline condition had a duration of 20 min, the amusement condition had a duration of 392 s (6.5 min), and the stress condition had a duration of 21 min. \\ \cline{1-1}  & Having experienced both stressful and amusement stimuli, the subjects were invited to participate in a guided meditation to facilitate emotional recovery. In the stress condition, the participants had to first deliver a 5 min speech on their personal traits in front of a three-person panel, focusing on strengths and weaknesses. After the TSST, the participants were given a 10 min rest, and the meditation condition had a duration of 7 min. \\ \hline \hline \end{tabular}
\end{table}
Table 1: WESAD dataset.  
The data represent time-series signals where all features are real numbers. The data were segmented into 60 s windows with 50% overlap, resulting in 240 trials (features). The length of each trial was 315,120 instances. We used holdout cross-validation to split the data into training and testing sets. The data were split into training and testing sets in an 80:20 ratio, with 80% of the data used for training and 20% used for testing.