# Anxiety Detection System Based on Galvanic Skin Response Signals
## 1 Introduction  
Anxiety disorder (AD) is a mental illness that is considered the most common mental disorder among groups and individuals. According to the World Health Organization (WHO), ADs \"are characterized by excessive constant fear and worrying\", and they cause various physical symptoms, e.g., chest pain, headaches, elevated heart rate (HR), and abdominal pain [1]. In 2018, a survey [2] highlighted the prevalence of ADs worldwide, ranging from 6% to 16% of the population. According to the WHO, one in four people worldwide will experience a mental disorder at some point in their lives. The WHO also ranks mental disorders as the second leading cause of economic loss globally, after ischemic heart disease. In addition, 23% of all deaths worldwide are attributed to depression and anxiety [3]. This alarming trend is further compounded by the insufficient number of specialists in this field, and the number of specialists is not growing at a rate that is commensurate with the surge in individuals suffering from ADs [1].  
Currently, psychological instruments, e.g., self-questionnaires, and commercial measurement instruments to analyze biological signals are the primary tools used to detect anxiety and stress [4]. Commonly used tools, e.g., interviews, questionnaires, and self-reports, rely on subjective responses and interpretation, leading to debatable results [5]. However, the early identification and assessment of stress is challenging. To overcome this limitation, previous studies have proposed the use of sensors, electronic measurement systems, and data processing algorithms to acquire and analyze physiological signals and biological markers associated with stress and anxiety [5, 6, 7].  
Several studies have investigated biomarkers that correlate with the physiological response to stress and anxiety, with HR and blood pressure being utilized commonly [8]. In addition, the skin conductance response (SCR) and skin conductance level measured using electrodermal activity (EDA) sensors have shown promise in detecting stress [9]. Physiological signals are considered reliable for anxiety detection because they cannot be controlled consciously by the subject [10].  
Research has reported that analyzing psychological signals using computational algorithms has shown promising results when applied to the detection of anxiety and stress [4]. However, few studies have investigated computing technologies or technological platforms that can assist in identifying individuals with mental health issues in nonclinical settings.  
Thus, this study attempts to detect anxiety through physiological signals, specifically the galvanic skin response (GSR). The GSR is a sensitive indicator of the skin's electrical conductance, which varies with emotional arousal. Anxiety is a state of heightened emotional arousal; thus, the GSR can be an effective tool for detecting anxiety. Previous studies [11; 12] have demonstrated that the GSR can differentiate between anxious and nonanxious individuals, and it can be used to predict anxiety levels in response to stressors. The procedures involved in recognizing anxiety include signals preprocessing, feature extraction, and classification of EDA data.  
The remainder of this paper is organized as follows. Section 2 presents a brief overview of the knowledge and terminologies required to understand the scope of GSR signal-based anxiety detection using ML. Section 3 summarizes previous research efforts in this field. Section 4 designs the experimental methodology. Section 5 discusses the results. Section 6 presents the research conclusion.