文献类型：会议论文

英文题名：Optimizing Clinical Depression Detection: Extracting Depression-Specific Feature Sets Using spFSR to Enhance Speech-Based Diagnosis

作者：Guo, Wenhui[1];Mao, Xinyu[1];Chen, Binxiao[1];Gu, Manyue[1];Li, Dongdong[1];Yang, Hai[1]

机构：[1]East China Univ Sci & Technol, Shanghai, Peoples R China

会议论文集：2024 International Conference on Bioinformatics and Biomedicine

会议日期：DEC 03-06, 2024

会议地点：Lisbon, PORTUGAL

语种：英文

外文关键词：depression; speech; feature selection

摘要：Accurate detection of depression through speech analysis offers a promising non-invasive approach for early diagnosis and intervention. However, the high dimensionality and complexity of speech features present significant challenges in identifying the most relevant features for depression detection. This study applies the spFSR (Feature Selection and Ranking via Simultaneous Perturbation Stochastic Approximation) technique to a comprehensive 2,268-dimensional speech feature set, focusing on selecting features specifically relevant to depression. The effectiveness of the spFSR method is evaluated using two well-known datasets: DAIC-WOZ and CMDC. The selected feature set was assessed across various machine learning models, demonstrating substantial improvements in key performance metrics on both datasets. The results indicate that the spFSR method effectively optimizes feature selection for depression detection, leading to more robust and accurate predictive models across different datasets.Our study find that the top 10 features for effective speech-based depression detection include spectral features (e.g., spectral flux, entropy, flatness), fundamental frequency metrics (e.g., lowest percentile), periodic features (e.g., jitter), and MFCC attributes (e.g., segment length, skewness).