Depression is a major cause of global burden that can be a life-threatening mental disorder . World Health Organization (WHO) reported that more than 300 million people worldwide suffer from depression. The bigger problem is that the procedure of depression diagnosis is complicated. Diagnosis of depression is usually done through interviews with physicians, accompanying tests such as Beck's depression inventory (BDI) or Hamilton Depression Rating Scale (HDRS). However, this process is time-consuming, burdensome for the patient, and reflects much of the doctor's personal subjectivity.
In recent, many studies are trying to find biomarker for depression using brain activity to diagnose in a more objective and time-saving way [2, 3]. Among several methods of measuring brain activity, non-invasive EEG is best suited as a quick and simple way to diagnose depression. EEG is also has lots of advantages: less time consuming, and cost-efficient than neuroimaging methods like as functional magnetic resistance imaging (fMRI).
Band power is the most representative indicator in the EEG. Prior studies showed that biomarkers of depression were studied with band power at 25% of total, followed by Alpha asymmetry (20.8%), and evoked potential (18.8%) . Findings regarding band power has also been actively reported, and Alpha band accounting for a large portion for important feature among them [4–7]. Another biomarker for depression is Alpha asymmetry, which can be obtained from difference of alpha band power between brain hemispheres. Several findings about Alpha asymmetry as a biomarker of depression have been reported recently [8–10].
Predicting result of disease diagnosis using a classification model is an example of how to discover influential biomarkers. The more influential biomarkers are, the greater the performance of the model. Indeed, prior studies of detecting depression using artificial neural network and achieving a high accuracy of more than 90% have already been reported[11, 12]. However, the almost previous studies have a limitation that the number of subjects were under 15 per group, and all subjects were already clinically diagnosed with Major depressive disorder (MDD). Furthermore, the models are relatively complex and overfitting is concerning because non-linear features have been applied to the Artificial Neural Network (ANN) to be trained with numerous parameters.
In the present study, we divided potentially depressed people and healthy people from database by optimal BDI criteria . We propose a reliable potential depression predictive model based on sufficient subjects and a very simple z-scored band power. The significance of our study lies in predicting potential depression among those who have not been clinically diagnosed.