# Multimodal Classification of Anxiety Based on Physiological Signals
## 2 Related Works  
Anxiety recognition and detection is a highly interdisciplinary research field with links to signal processing, machine learning, and neuroscience. Anxiety disorders' prevalence across the population vastly exceeds the capacity of mental health services to provide face-to-face therapy for all those affected, demanding novel approaches for deliveringtherapy. Traditional monitoring methods rely on retrospective reports, which are subject to recall bias and limit the ability to understand behavior accurately in real-time settings [18]. Therefore, having access to valuable physiological information would greatly enrich the process of intervening in these situations [18]. In this context, several works have been proposed addressing the classification of anxiety levels. Several machine learning algorithms have been used to classify anxiety and stress based on their physiological mechanisms' unique characteristics. Often, self-reported results are used as part of the labels, which puts the participant in charge of the labeling process. One of the advantages of self-reports is that they can be easily applied, although this method carries individual bias [18]. Authors have labeled anxiety in multiple ways: binary (stressed or relaxed), three classes (no anxiety, low anxiety, and high anxiety), or four classes (no, low, mild, or high anxiety) [19]. Supervised learning algorithms used for anxiety detection include Decision Tree (DT), Adaptative Boosting (ADB), Support Vector Machines (SVM), Naive Bayes (NB), Markov Models (MM), Logistic Regression (LR), Random Forest (RF), Artificial Neural Networks (ANN), Linear Discriminant Analysis (LDA), etc. [18; 19]. Previous machine learning studies have been conducted to classify distinct levels of anxiety, showing that combining different physiological signals increases the accuracy of anxiety detection systems [14]. The EDA signal was used to detect and measure stress in call center employees at work and during each call. In a binary classification task--stressful/non-stressful calls--SVMs achieved an accuracy across participants of 78.03% [20]. Accuracies higher than 95% were obtained in binary and multi-class classification of anxiety levels, in either subject-dependent or independent studies, using Linear Discriminant Analysis (LDA) with a combination of physiological signals such as GSR (Galvanic Skin Response), EMG, ECG, ST, and respiration data, with as many as three different physiological signals at a time [19]. In classifying anxiety during Virtual Reality Exposure Therapy (VRET) sessions, a combination of Blood Volume Pulse (BVP), GSR, and ST were used with an SVM to classify four different anxiety classes (\"low\", \"mild\", \"moderate\", and \"high\"), achieving an accuracy of 80.1% [19]. In this research study, the feature selection was addressed by using Random Forest (RF), i.e., considering the relationship of the features with the target variable when selecting the optimal features [19]. In another study [21], the heart activity, skin conductance, and accelerometer signal were collected from 21 participants of an algorithmic programming contest for nine days to discriminate contest stress, relatively higher cognitive load (lecture), and relaxed time activities. Principal Component Analysis (PCA) was used for data dimensionality. This study used an imbalanced dataset; therefore, to deal with the imbalance problem, a combined strategy of undersampling the majority class and oversampling the minority class was applied. When combined with ECG and EDA information, a multilayer perceptron algorithm achieved the best classification accuracy of 92.15%. To grant an immersive experience, Virtual Reality (VR) video games incorporate high-quality auditory and video feedback along with vibrotactile cues. Nonetheless, the user's state is not considered to assess the video game experience.