Diagnosing Clinical Manifestation of Apathy Using Machine Learning and Micro-facial Expressions Detection

Abstract

Apathy is a behavioral and personality change and is generally defined as a loss of motivation. In this study, we will explore the detection of apathy in two phases. An apathy detection phase, and relevant features identification phase. In the first one, we apply micro-facial expressions detection systems and counter for the purpose of diagnosing clinical manifestation of apathy from a video for Lebanese citizens. The method works by applying Histogram of Oriented Gradients (HOG) as a feature descriptor on video dataset of spontaneous micro facial movements. Micro-facial expressions appear by video recording participants reacting to emotional stimulating COPE cards. Results are compared to Lille Apathy Rating Scale LARS scores. Kappa agreement was calculated to be 95.96% showing the proposed classification method has a high accuracy of estimation. In a second phase, we aim to identify the demographics and habits that might be affecting the manifestation of apathy using machine leaning algorithms. A statistical model is built based on the results to identify the characteristics that affect the manifestation of apathy by analyzing the data and making a statistical description. Using a sample of 470 participants, we base our results on the decision tree (CART) combined with logistic regression. Finally, we found that insomnia, genetic background and stress are the most important features that influence the manifestation of apathy, with an accuracy of 96.7%.