Differentiation between depression and bipolar disorder in child and adolescents by voice features
Abstract Objective Major depressive disorder (MDD) and bipolar disorder (BD) are serious chronic disabling mental and emotional disorders, with symptoms that often manifest atypically in children and adolescents, making diagnosis difficult without objective physiological indicators. Therefore, we ai...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-01-01
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| Series: | Child and Adolescent Psychiatry and Mental Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13034-024-00708-0 |
| _version_ | 1797275853635190784 |
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| author | Jie Luo Yuanzhen Wu Mengqi Liu Zhaojun Li Zhuo Wang Yi Zheng Lihui Feng Jihua Lu Fan He |
| author_facet | Jie Luo Yuanzhen Wu Mengqi Liu Zhaojun Li Zhuo Wang Yi Zheng Lihui Feng Jihua Lu Fan He |
| author_sort | Jie Luo |
| collection | DOAJ |
| description | Abstract Objective Major depressive disorder (MDD) and bipolar disorder (BD) are serious chronic disabling mental and emotional disorders, with symptoms that often manifest atypically in children and adolescents, making diagnosis difficult without objective physiological indicators. Therefore, we aimed to objectively identify MDD and BD in children and adolescents by exploring their voiceprint features. Methods This study included a total of 150 participants, with 50 MDD patients, 50 BD patients, and 50 healthy controls aged between 6 and 16 years. After collecting voiceprint data, chi-square test was used to screen and extract voiceprint features specific to emotional disorders in children and adolescents. Then, selected characteristic voiceprint features were used to establish training and testing datasets with the ratio of 7:3. The performances of various machine learning and deep learning algorithms were compared using the training dataset, and the optimal algorithm was selected to classify the testing dataset and calculate the sensitivity, specificity, accuracy, and ROC curve. Results The three groups showed differences in clustering centers for various voice features such as root mean square energy, power spectral slope, low-frequency percentile energy level, high-frequency spectral slope, spectral harmonic gain, and audio signal energy level. The model of linear SVM showed the best performance in the training dataset, achieving a total accuracy of 95.6% in classifying the three groups in the testing dataset, with sensitivity of 93.3% for MDD, 100% for BD, specificity of 93.3%, AUC of 1 for BD, and AUC of 0.967 for MDD. Conclusion By exploring the characteristics of voice features in children and adolescents, machine learning can effectively differentiate between MDD and BD in a population, and voice features hold promise as an objective physiological indicator for the auxiliary diagnosis of mood disorder in clinical practice. |
| first_indexed | 2024-03-07T15:19:58Z |
| format | Article |
| id | doaj.art-6e05f39f9f4d4bb79f0f9c79e868060b |
| institution | Directory Open Access Journal |
| issn | 1753-2000 |
| language | English |
| last_indexed | 2024-03-07T15:19:58Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Child and Adolescent Psychiatry and Mental Health |
| spelling | doaj.art-6e05f39f9f4d4bb79f0f9c79e868060b2024-03-05T17:42:28ZengBMCChild and Adolescent Psychiatry and Mental Health1753-20002024-01-011811910.1186/s13034-024-00708-0Differentiation between depression and bipolar disorder in child and adolescents by voice featuresJie Luo0Yuanzhen Wu1Mengqi Liu2Zhaojun Li3Zhuo Wang4Yi Zheng5Lihui Feng6Jihua Lu7Fan He8National Clinical Research Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Beijing Institute for Brain Disorders Capital Medical UniversityNational Clinical Research Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Beijing Institute for Brain Disorders Capital Medical UniversityNational Clinical Research Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Beijing Institute for Brain Disorders Capital Medical UniversityBeijing Institute of Technology, School of Integrated Circuits and ElectronicsBeijing Institute of Technology, School of Integrated Circuits and ElectronicsNational Clinical Research Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Beijing Institute for Brain Disorders Capital Medical UniversityBeijing Institute of Technology, School of Optics and PhotonicsBeijing Institute of Technology, School of Integrated Circuits and ElectronicsNational Clinical Research Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Beijing Institute for Brain Disorders Capital Medical UniversityAbstract Objective Major depressive disorder (MDD) and bipolar disorder (BD) are serious chronic disabling mental and emotional disorders, with symptoms that often manifest atypically in children and adolescents, making diagnosis difficult without objective physiological indicators. Therefore, we aimed to objectively identify MDD and BD in children and adolescents by exploring their voiceprint features. Methods This study included a total of 150 participants, with 50 MDD patients, 50 BD patients, and 50 healthy controls aged between 6 and 16 years. After collecting voiceprint data, chi-square test was used to screen and extract voiceprint features specific to emotional disorders in children and adolescents. Then, selected characteristic voiceprint features were used to establish training and testing datasets with the ratio of 7:3. The performances of various machine learning and deep learning algorithms were compared using the training dataset, and the optimal algorithm was selected to classify the testing dataset and calculate the sensitivity, specificity, accuracy, and ROC curve. Results The three groups showed differences in clustering centers for various voice features such as root mean square energy, power spectral slope, low-frequency percentile energy level, high-frequency spectral slope, spectral harmonic gain, and audio signal energy level. The model of linear SVM showed the best performance in the training dataset, achieving a total accuracy of 95.6% in classifying the three groups in the testing dataset, with sensitivity of 93.3% for MDD, 100% for BD, specificity of 93.3%, AUC of 1 for BD, and AUC of 0.967 for MDD. Conclusion By exploring the characteristics of voice features in children and adolescents, machine learning can effectively differentiate between MDD and BD in a population, and voice features hold promise as an objective physiological indicator for the auxiliary diagnosis of mood disorder in clinical practice.https://doi.org/10.1186/s13034-024-00708-0Mood disorderVoice featuresDiagnosisChild and adolescentClassification accuracy |
| spellingShingle | Jie Luo Yuanzhen Wu Mengqi Liu Zhaojun Li Zhuo Wang Yi Zheng Lihui Feng Jihua Lu Fan He Differentiation between depression and bipolar disorder in child and adolescents by voice features Child and Adolescent Psychiatry and Mental Health Mood disorder Voice features Diagnosis Child and adolescent Classification accuracy |
| title | Differentiation between depression and bipolar disorder in child and adolescents by voice features |
| title_full | Differentiation between depression and bipolar disorder in child and adolescents by voice features |
| title_fullStr | Differentiation between depression and bipolar disorder in child and adolescents by voice features |
| title_full_unstemmed | Differentiation between depression and bipolar disorder in child and adolescents by voice features |
| title_short | Differentiation between depression and bipolar disorder in child and adolescents by voice features |
| title_sort | differentiation between depression and bipolar disorder in child and adolescents by voice features |
| topic | Mood disorder Voice features Diagnosis Child and adolescent Classification accuracy |
| url | https://doi.org/10.1186/s13034-024-00708-0 |
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