DEPRESSION DETECTION SYSTEM USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Abstract: In a state of natural psychic equilibrium, tension may be viewed as a disturbance in general. A person's mental health will be put under stress if they cannot balance the expectations placed on them with their ability to cope with them. There are many different kinds of challenges. Psychological equilibrium disruption is a broad description of depression. Depression detection is one of the main areas of biomedical engineering study because it may be simple to avoid depression with the right measures. There are many bio signals accessible, including Mri, Rgb, oxygenation, and Frs. which can be used to determine depression levels because they show unique changes in depression induction. Due to the readily available recording, we use ECG as the top candidate in this endeavor. Multiple SVM model types have been examined by changing the function number and kernel type.

Keywords: Preprocessing, Segmentation, SVM Algorithm, Machine Learning. Feature Extraction, Classification CNN Algorithm.