Abstract

     Depression is a serious mental health condition that may lead to poor mental and emotional functioning at work, at school and in the family causing the mental imbalance. In worst scenarios, depression may lead to severe anxiety or suicide. Hence, it is necessary to diagnose depression at early stages. This paper elaborates the development of a novel approach for a convolutional neural network model that can examine facial images from the recorded interview sessions to discover facial patterns that could indicate depression level. The user-generated data helps to distinguish between different depressive groups with depression symptoms that can manifest people with various mental illnesses in different ways. In particular, we want to automatically predict the depression scale and differentiate depression from other mental disorders using the patient’s psychiatric illness history and dynamic textual descriptions extracted from the user inputs. We apply the k-nearest neighbour algorithm on the dynamic textual descriptors to make a linguistic analysis for classifying mental illness into different classes. We apply dimensionality reduction and regression using the Random Forest algorithm to predict the depression scale. The proposed framework is an extension to pre-existing frameworks, replacing the handcrafted feature extraction technique with the deep feature extraction. The model performs 2.7% better than existing frameworks in facial detection and feature extraction.

Introduction

     In India, depression is common in adults between the ages of 16–۲۵, considered to be a leading cause of disability. For years, researchers have to identify and map the relationship between brain function and structure using neuroimaging data. The researchers at the University of Texas have identified a unique technique to categorize people susceptible to developing depression and anxiety using deep learning with a supercomputer. Now they are using the Stampede supercomputer at the Texas advanced computing centre to train deep learning algorithms that can identify similarities among hundreds of patients using magnetic resonance imaging genomics data and other factors to predict patients at risk of depression and psychological disorders (Valstar et al., 2013). In the past, the researchers have worked on the development of a model that takes raw text and audio segments as input and analyzes the wave-forms that predict depression. The work provides a method for deep learning-based segmentation to detect depression, as well as irregular segmentation and masks used for Gabor wavelength detection (Long et al, 2014). A Gabor filter, named after Dennis Gabor, is a linear local texture filter in image processing. It analyses if the model includes any specific frequency content in specific directions in a localized region around the point or region of analysis. The researchers trained the deep learning algorithm using extracted audio and textual features from clinical patients with suicide attempts and higher rate of mood swings (Ma et al, 2016). Extreme Gradient boosting technique is used for identifying and categorizing the important parameters of depression and predicting depression cases by re-sampling methods using different balanced samples but the researchers were not able to precisely predict the depression scale. Also differentiating the mental disorders is a tedious task due to similarities in symptoms (Marcus et al., 2012).

Conclusion and future work

     Here, we have developed an AI-based automated system for predicting the depression scale. The model is purely based on recognizing facial expressions from recorded video sessions of depressed patients. We have used deep learning algorithms for extracting dynamic facial features and recognizing expressions producing the feature dynamic history histogram applied to feature dynamic vector sequences applied to the video sample. After calculating the feature dynamic history histogram, we apply regression techniques to correlate between feature and depression scales. The experimental results showed that the model had performed well on the test video dataset. The deep feature extraction technique significantly performs much better than the handcrafted feature extraction technique. The feature attributes are directly extracted from the responses provided by the convolutional layers perform best rather the performance of the neural networks are closely connected giving benchmarking results.