# Multimodal Classification of Anxiety Based on Physiological Signals
## 1 Introduction  
Stress and anxiety have become buzzwords of the present time, affecting many people worldwide, irrespective of gender, age group, or work profile. This trend may be due to the challenging demands of contemporary work culture, changing lifestyles, and technological interactions [1; 2; 3]. According to the 2021 health statistics report of the Organization for Economic Cooperation and Development (OCDE), in 2019 and 2020, Portugal was the OCDE country with the highest anxiolytic consumption [4]. Moreover, the ability to understand and perceive mental disorders has even more relevance considering the new challenges associated with the COVID-19 pandemic, which include increased anxiety and depression rates at a global level. Anxiety is a specific reaction to stress that may persist even after a concern has passed. Anxiety can manifest psychologically and physiologically through particular responses of the nervous system. A strong association has been reported between prolonged and severeanxiety with mental disorders, such as depression and anxiety disorders, and an increased risk for other health complications, like cardiovascular diseases [5; 6; 7; 8; 9]. Furthermore, multiple studies show an association between anxiety disorders and depression and an imbalance of the Autonomic Nervous Sysystem (ANS), characterized by increased sympathetic activity and reduced parasympathetic or vagal activity [6; 10; 11]. The ANS maintains the homeostasis of unconscious bodily functions such as heart and respiration rates, blood pressure, and sweating, among others. The two main branches of the ANS are the Sympathetic Nervous System (SNS) and the Parasympathetic Nervous System (PNS) [5]. The SNS responds to emergencies, activating our organs and functions to respond to stress (fight-or-flight responses), while the PNS relaxes these functions back to rest (rest-and-digest activity) [12]. Physiological signals can reflect spontaneous, ongoing activity or activity that results from external stimulation. Physiological signals of interest for stress and anxiety detection include hormone levels, Electrocardiogram (ECG), Electrocardiogram (EEG), Blood Pressure (BP), Skin Temperature (ST), Electromyogram (EMG), Respiration Volume (BV), and Pupil Diameter (PD) [13; 14]. Several parameters associated with the physiological processes of anxiety can be obtained from the physiological signals, reinforcing the need to pre-process these signals to remove noise and artifacts during collection. Relying on physiological signals, namely ECG, EDA, and EMG, recorded with wearable sensors and self-reported, specifically the 6-item State-Trait Inventory questionnaire (6-STAI) [15], this study proposes to attain the following objectives that can help in the current increased needs for the management of mental health:  
1. physiological characterization of anxiety levels, providing further knowledge about the physiological responses and the physiological interactions ruling anxiety levels;
2. automatic detection by classifying anxiety levels using machine learning algorithms;
3. understand how to improve the classification of anxiety levels in such an imbalanced domain and if data-level pre-processing techniques can be good solutions to the imbalanced class distribution problem in mental health datasets. Our approach differs from conventional studies in that it addresses anxiety levels without controlled elicitation. This ensures the classification of anxiety as a neutral condition, providing valuable insights that can contribute to developing better techniques and strategies for people to manage their health and well-being [16]. Moreover, by tackling this from a multimodal perspective, we ensure higher classification accuracy, as research has pointed out that recognition systems that base their decisions on multiple physiological data tend to be more accurate than their single-signal counterparts [17].