文章基本信息

标题：Disclosing Critical Voice Features for Discriminating between Depression and Insomnia—A Preliminary Study for Developing a Quantitative Method
本地全文：下载
作者：Ray F. Lin ; Ting-Kai Leung ; Yung-Ping Liu 等
期刊名称：Healthcare
电子版ISSN：2227-9032
出版年度：2022
卷号：10
期号：5
DOI：10.3390/healthcare10050935
语种：English
出版社：MDPI Publishing
摘要：Background: Depression and insomnia are highly related—insomnia is a common symptom among depression patients, and insomnia can result in depression. Although depression patients and insomnia patients should be treated with different approaches, the lack of practical biological markers makes it difficult to discriminate between depression and insomnia effectively. Purpose: This study aimed to disclose critical vocal features for discriminating between depression and insomnia. Methods: Four groups of patients, comprising six severe-depression patients, four moderate-depression patients, ten insomnia patients, and four patients with chronic pain disorder (CPD) participated in this preliminary study, which aimed to record their speaking voices. An open-source software, openSMILE, was applied to extract 384 voice features. Analysis of variance was used to analyze the effects of the four patient statuses on these voice features. Results: statistical analyses showed significant relationships between patient status and voice features. Patients with severe depression, moderate depression, insomnia, and CPD reacted differently to certain voice features. Critical voice features were reported based on these statistical relationships. Conclusions: This preliminary study shows the potential in developing discriminating models of depression and insomnia using voice features. Future studies should recruit an adequate number of patients to confirm these voice features and increase the number of data for developing a quantitative method.
关键词：endepressioninsomniaopenSMILEvoice featurecomputer-aided diagnosis