Depression detection using Machine Learning and Deep Learning

Abstract: A method for real monitoring of the heart for depression episodes is described here. We have developed a convolutional neural network (CNN) based machine learning algorithm for classifying into depression episodes of the heart with an accuracy over 92%. Our algorithm is capable of detecting depression episodes of varying duration. The algorithm is evaluated using Database. The best results obtained here are 0.95%, 0.98%, and 0.91% respectively for accuracy, sensitivity, and specificity.

Keywords: CNN, image preprocessing, depression, Depression Detection.