文献类型：期刊文献

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 University of Science and Technology, Shanghai, China

年份：2024

起止页码：6153

外文期刊名：Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

收录：EI(收录号：20250717853675)

语种：英文

外文关键词：Diagnosis - Feature Selection - Noninvasive medical procedures - Speech analysis - Speech enhancement - Stochastic models

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). ? 2024 IEEE.