Disclosing Critical Voice Features for Discriminating between Depression and Insomnia—A Preliminary Study for Developing a Quantitative Method
<b>Background:</b> 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 ma...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/10/5/935 |
| _version_ | 1797499421118693376 |
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| author | Ray F. Lin Ting-Kai Leung Yung-Ping Liu Kai-Rong Hu |
| author_facet | Ray F. Lin Ting-Kai Leung Yung-Ping Liu Kai-Rong Hu |
| author_sort | Ray F. Lin |
| collection | DOAJ |
| description | <b>Background:</b> 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. <b>Purpose:</b> This study aimed to disclose critical vocal features for discriminating between depression and insomnia. <b>Methods:</b> 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. <b>Results:</b> 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. <b>Conclusions:</b> 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. |
| first_indexed | 2024-03-10T03:47:06Z |
| format | Article |
| id | doaj.art-32d6369c5dae4f0a920edd1c8b6b72e3 |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-10T03:47:06Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
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| series | Healthcare |
| spelling | doaj.art-32d6369c5dae4f0a920edd1c8b6b72e32023-11-23T11:15:21ZengMDPI AGHealthcare2227-90322022-05-0110593510.3390/healthcare10050935Disclosing Critical Voice Features for Discriminating between Depression and Insomnia—A Preliminary Study for Developing a Quantitative MethodRay F. Lin0Ting-Kai Leung1Yung-Ping Liu2Kai-Rong Hu3Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan 32003, TaiwanDepartment of Radiology, Taoyuan General Hospital, Ministry of Health and Welfare, No. 1492, Zhongshan Rd., Taoyuan City 33004, TaiwanDepartment of Industrial Engineering and Management, Chaoyang University of Technology, Taichung 413310, TaiwanDepartment of Industrial Engineering and Management, Yuan Ze University, Taoyuan 32003, Taiwan<b>Background:</b> 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. <b>Purpose:</b> This study aimed to disclose critical vocal features for discriminating between depression and insomnia. <b>Methods:</b> 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. <b>Results:</b> 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. <b>Conclusions:</b> 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.https://www.mdpi.com/2227-9032/10/5/935depressioninsomniaopenSMILEvoice featurecomputer-aided diagnosis |
| spellingShingle | Ray F. Lin Ting-Kai Leung Yung-Ping Liu Kai-Rong Hu Disclosing Critical Voice Features for Discriminating between Depression and Insomnia—A Preliminary Study for Developing a Quantitative Method Healthcare depression insomnia openSMILE voice feature computer-aided diagnosis |
| title | Disclosing Critical Voice Features for Discriminating between Depression and Insomnia—A Preliminary Study for Developing a Quantitative Method |
| title_full | Disclosing Critical Voice Features for Discriminating between Depression and Insomnia—A Preliminary Study for Developing a Quantitative Method |
| title_fullStr | Disclosing Critical Voice Features for Discriminating between Depression and Insomnia—A Preliminary Study for Developing a Quantitative Method |
| title_full_unstemmed | Disclosing Critical Voice Features for Discriminating between Depression and Insomnia—A Preliminary Study for Developing a Quantitative Method |
| title_short | Disclosing Critical Voice Features for Discriminating between Depression and Insomnia—A Preliminary Study for Developing a Quantitative Method |
| title_sort | disclosing critical voice features for discriminating between depression and insomnia a preliminary study for developing a quantitative method |
| topic | depression insomnia openSMILE voice feature computer-aided diagnosis |
| url | https://www.mdpi.com/2227-9032/10/5/935 |
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