Machine Learning to Detect Anxiety Disorders from Error-Related Negativity and EEG Signals

Anxiety is endemic to every person, with an occurrence rate of approximately 20% [World Health Organization, 2017]. Between 2020 and 2022, over one in six people (17.2% or 3.4 million people) aged 16 to 85 years experienced an anxiety disorder [Australian Bureau of Statistics]. Anxiety is caused by changes in the situation, nervousness and common symptoms, including sweating, trembling and excessive worrying, which affect a person's daily life. Anxiety disorders encompass a range of conditions, such as generalised anxiety disorder (GAD), panic disorder (PD), social anxiety disorder (SAD), obsessive-compulsive disorder (OCD), various phobia-related disorders, physical pain related protective behaviour [Li et al., 2020, 2021] and depression [Ghosh and Anwar, 2021]. Current clinical approaches for diagnosing these disorders often suffer from limitations in accuracy and objectivity, relying heavily on self-reports, patient histories and clinical observations. These methods can be subjective and may not capture the nuanced neural and behavioural patterns associated with anxiety, leading to potential misdiagnoses. Recent research has shown promising results in using machine learning techniques to detect anxiety through physiological analysis [Abd-Alrazaq et al., 2023], such as respiration, electrocardiogram (ECG), photoplethysmography (PPG), electrodermal response (EDA) and electroencephalography (EEG), to identify patterns associated with anxiety states [Abd-Alrazaq et al., 2023].