基于视听特征描述符和CNN的重度抑郁障碍(AMDD)自动检测

Nikhil Singh, Rajiv Kapoor, Ruchirangad Kapoor, S. Arora
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引用次数: 0

摘要

严重的抑郁症是一种普遍存在的心理健康问题,可能导致自残或自杀。抑郁症自动检测系统为抑郁症的临床诊断和早期干预提供了极大的帮助。在这项研究中,我们提出了一种新的自动化重度抑郁症(AMDD)检测技术,该技术利用患者的视听参数。采用主成分分析法进行降维特征选择。在提取和选择这两类信号的有效特征后,我们对每一种模态进行分类器训练。采用支持向量机进行分类。根据我们的研究,融合来自不同数据模式的特征比只使用一种数据模式的特征表现更好,并且结合视听数据模式的特征可以获得最高的分类精度。与其他方法相比,PCA显著提高了特征选择方法的准确性。支持向量机在DAIC-WOZ数据集上的分类准确率达到99.15%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated Major Depressive Disorder (AMDD) Detection using Audio-Visual Feature Discriptor and CNN
The severe forms of depression, a widespread mental health issue, can result in self-harm or suicide. Clinical diagnosis and early intervention of depression are greatly aided by an automatic depression detection system. In this study, we suggest a novel automated major depressive disorder (AMDD) detection technique that makes use of audio-visual parameters from patients. PCA is used as dimension reduction for feature selection. After these two types of signals' effective characteristics were extracted and selected, we trained classifiers on each modality. SVM is taken into account for classification. According to our research, fusing features from different data modalities performs better than using just one, and combining audio-visual data modalities' features results in the highest classification accuracy. Comparing the accuracy of the feature selection approach to other methods, PCA significantly increased the accuracy. Also, SVM gives the best in class accuracy of 99.15% on DAIC-WOZ dataset.
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