# Multimodal Classification of Anxiety Based on Physiological Signals
## 3 Materials and Methods
### Dataset Description  
To achieve the proposed goals, we used a publicly available multimodal dataset for stress and affect detection--the WESAD dataset [1]--containing self-reports, motion, and physiological data of 15 healthy participants during a lab study designed for stress and affect detection.  
The data were collected under a study protocol designed for stress and affect detection, in which the subjects were exposed to different affective stimuli (neutral, stress, and amusement) and two meditation periods (to de-excite the participants). Besides the physiological signals collected during these conditions, the dataset also includes context notes about the participants and self-assessment report results. Self-reports were collected from all participants after each affective stimulus condition. The responses to the 6--STAI S questionnaire [15] applied after the baseline condition were used to assess their current anxiety level.  
According to the authors of the dataset, two subjects were excluded from the initial seventeen subjects that participated in the study due to sensor malfunction, resulting in 15 healthy subjects (three of whom were women). The mean age of the subjects is around 27.5 years, ranging from 25 to 29 years old.  
Our study aims to address the classification of anxiety levels without any state elicitation for those or any pre-designed affective stimuli. Thus, only the data collected during the 20-min baseline condition was used to ensure a neutral affective state. The selection of the physiological signals for anxiety is supported by the literature [17; 19], being described as reliable indicators of anxiety.  
The dataset contains high-resolution data sampled at 700 Hz from a chest-worn device (RespiBAN) and a lower-resolution wrist-worn device (Empatica E4); nonetheless, only data from the RespiBAN device was employed in this study. The RespiBAN device contains multimodal sensors that allow obtaining the following physiological signals: ECG, EDA, EMG, respiratory activity (RESP), temperature (TEMP), and motion accelerometer (ACC). In this study, EMG data was recorded on the upper trapezius muscle on both sides of the spine, and the EDA signal was recorded on the rectus abdominis.  
A short excerpt of these signals from two participants with anxiety levels of \"High\" and \"Low and moderate\", with STAI-S scores of 16 and 10 (respectively), is shown in Figure 2. Regarding the ECG, depicted on the left-hand side of the figure, it can be seen that  
Figure 1: Graphical representation of the study’s workflow.  
the participant with a higher level of anxiety presents a higher heart rate (HR), as the signal presents more R-peaks for the same period. From the EDA signals, we can observe an almost constant Tonic Skin Conductance Level (SCL) for the participant with a \"Low and moderate\" level of anxiety. In contrast, besides showing more fluctuation, the participant with a \"High\" level also presents a higher number of SCR (Skin Conductance Response) peaks and higher amplitude values. Regarding the EMG signal, the right-hand side figure shows that higher anxiety levels present a higher number of bursts of activity and that these active muscle periods are longer than for lower anxiety levels. These observations in the physiological signals, supporting an increased sympathetic activity in higher levels of anxiety, are in accordance with what has been reported in the literature regarding reactions in the ANS related to anxiety disorders [6,10].