Estimation of Visual Heart Rate to Predict Depression
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Abstract
Depression is a common but a leading mental disorder impacting people of all ages globally. Depression can be predicted accurately by analyzing a Heart bit rate of a particular person for 24 hours. Heart Rate (HR) is very useful Physiological parameter which acts as indicator for physiological state of human being. Eulerian Video Magnification (EVM) helps for the development of the healthcare solutions by extracting physiological signals from inputted facial video. In this research paper the potential of inputted video is examined using EVM to extract physiological data, especially heart rate.
The person’s heart rates who is suffering with depression is elevated during the night time, when it usually reduces. From the heart rate individual can be identified as mentally healthy or depressive. The dataset used in this paper is clinically evaluated on which proposed methodology is applied. Results indicate that visually calculated heart bit rate is efficient tool for predicting depression in an individual.
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