Predicting Depression Risk from Facial Video-Derived Heart Rate Estimates

Abstract

Common mental disorder is caused due to depression. Medical study shows that heart rate is linked to depression. Heart rate can give early warning of potential depression. Heart rate could predict the risk of depression. This link helps diagnosis and treatment of mental health issues like depression. Heart rate of healthy person is 60-85 beats per minute. When a person is depressed, his heart rate is not in normal range. Heart rate of depressed human being is increased beyond 85 beats per minute. Depression can be predicted with a 90% accuracy by analyzing a person’s heart rate. Using Eulerian Video Magnification algorithm, it is possible to calculate heart rate of person from facial video. Which gives benefit that no need of physical contact with the person. In the proposed research Heart rate is calculated by inputting face videos. Questionnaire is formed that contains 32 questions useful for depression assessment. Real time video dataset is collected while asking depression questionnaire to the people of all age groups. Using Eulerian Video Magnification algorithm, facial video is amplified and heart rate is estimated. Based on range, dataset is labeled as depressed or not depressed. By applying machine learning algorithms like Decision Tree (DT), Support Vector Machine (SVM) and Random forest (RF), dataset is classified with accuracy ranging from 96% to 100%. The performance of this research when compared with related work carried out for same research purpose, it is observed that accuracy obtained for this research work is 51% to 85.7% till date. This research gives more accurate model with new approach to predict risk of depression using heart rate estimated from facial videos.