A Descriptive Analysis on Various Depression Detection Models of Human Brain: A Review Article

It is scientifically proved that mental health affects other organs of human. So, people start giving attention to depression condition of patients. This mild depressive state can be converted into major depressive state if not treated at right time. Many persons are affected by depressed state every year which lead to their death, suicidal thoughts with some health- related problems. EEG tool is proved helpful in diagnosis of depressive state very soon to prevent any worrying symptoms in future. The EEG feature extraction technique is able to differentiate healthy one from depressed subject. This methodology is helpful in finding exact cause behind real mechanisms to find out biomarkers for detection of abnormalities. This comprehensive study is focused on EEG-based algorithms for MDD patients. Different papers are explored extensively to understand the EEG frequency rhythm, band power, various filtration techniques, pre-processing methods, feature extraction and classification techniques. In many papers, more than one classifiers are used to differentiate between best and worst outcomes. The performance of these classifiers is calculated using parameters such as accuracy, sensitivity, specificity, entropy, S.D. (Standard Deviation) and ROC (Receiver Operating Characteristic) curve etc. This study aims at providing concrete ideas regarding possibilities of future research based on this study.