Early Detection of Depression using Screening Tools and Electroencephalogram (EEG) Measurements

Abstract

Mental illness refers to mental disorder that causes mild to severe disturbances in thoughts and behavior, resulting in the inability to cope with ordinary demands and daily life routines.  Among the wide spectrum of mental health conditions, depression was found to have the highest prevalence globally.  The current practice of depression detection depends on screening tools which are either physician-administered or self-administered and behavioral observations. Both methods are widely used but relies on subjective interpretation.  Thus, the method may lack the reliability and accuracy of detecting depression which may lead to incorrect diagnosis of medications. Many mental illness patients still suffer from their mental conditions as well as the side effects of medications during the treatment process.  Physiological measurement methods, such as electroencephalogram (EEG) measurements were found to be able to evaluate the condition of patients suffering from mental illness through recorded brain waves.  The paper sought to investigate the relationship between brainwaves and depression and to propose an alternative method to further evaluate the condition of patients who suffer from depression in a more accurate and effective way. Subjects underwent an initial screening to evaluate and classify their mental health condition based on the score obtained from the screening tools - Patient Health Questionnaire (PHQ)-9.  Subjects also underwent an EEG experiment with a given video-watching stimulus to evaluate their brain activity.  The study proves that increased depression severity subjects have shown distinctly lower Alpha waves at almost all electrode channels and comparatively lower Beta and Theta in the frontal region. This shows that there is a relationship between mental illness and brainwaves activities.  Despite that, the findings from the study showed that there were no strong association found between mean EEG amplitudes and the score from PHQ-9 which suggest that the current practice which only depends on subjective methods may not be sufficient for depression diagnosis. Using a more objective method showed that there are strong associations found between mean EEG amplitudes and the proposed EEG scoring especially in Alpha waves.  There were also strong association between the EEG scoring and the EEG amplitudes at all electrode channels in Alpha waves.  The use of EEG measurement may be considered as an effective and more accurate method to support the current practice in detecting early signs of mental illness in patients, specifically depression.