Different Approaches in Depression Analysis : A Review

Swathy Krishna, Anju. J
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引用次数: 4

Abstract

Clinical depression has been a common but a serious mood disorder nowadays affecting people of any age group. Since depression affects the mental state, the patient will find it difficult to communicate his/her condition to the doctor. Commonly used diagnostic measures are interview style assessment or questionnaires about the symptoms, laboratory tests to check whether the depression symptoms are related with other serious illness. With the emergence of machine learning and convolutional neural networks, many techniques have been developed for supporting the diagnosis of depression in the past few years. Since depression is a multifactor disorder, the diagnosis of depression should follow a multimodal approach for its effective assessment. This paper presents a review of various unimodal and multimodal approaches that have been developed with the aim of analyzing the depression using emotion recognition. The unimodal approach considers either of the attributes among facial expressions, speech, etc. for depression detection while multimodal approaches are based on the combination of one or more attributes. This paper also reviews several depression detection systems that use facial feature extraction methods that use eigenvalue algorithm, fisher vector algorithm, etc. and speech features such as spectral, acoustic feature, etc. The survey covers the existing emotion detection research efforts that use audio and visual data for depression detection. The survey shows that the depression detection using multimodal approach and deep learning techniques achieve greater performance over unimodal approaches in the depression analysis.
抑郁症分析的不同方法综述
临床抑郁症是一种常见但严重的情绪障碍,影响着任何年龄组的人。由于抑郁症影响精神状态,患者会发现很难向医生传达他/她的病情。常用的诊断方法有访谈式评估或症状问卷,实验室检查抑郁症状是否与其他严重疾病有关。随着机器学习和卷积神经网络的出现,在过去的几年里,许多技术被开发出来用于支持抑郁症的诊断。由于抑郁症是一种多因素障碍,抑郁症的诊断应遵循多模式的方法,以有效地评估其。本文介绍了各种单模态和多模态方法的回顾,这些方法的目的是利用情绪识别来分析抑郁症。单模态方法考虑面部表情、语音等属性中的任意一个来进行抑郁检测,而多模态方法是基于一个或多个属性的组合。本文还综述了几种使用特征值算法、fisher向量算法等人脸特征提取方法以及频谱、声学等语音特征的抑郁检测系统。该调查涵盖了现有的使用音频和视觉数据进行抑郁检测的情绪检测研究成果。调查显示,在抑郁症分析中,使用多模态方法和深度学习技术的抑郁症检测比单模态方法取得了更高的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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