Differentiation between depression and bipolar disorder in child and adolescents by voice features

• Main Authors: Jie Luo (Author), Yuanzhen Wu (Author), Mengqi Liu (Author), Zhaojun Li (Author), Zhuo Wang (Author), Yi Zheng (Author), Lihui Feng (Author), Jihua Lu (Author), Fan He (Author)
• Format: Book
• Published: BMC, 2024-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s13034-024-00708-0; 1753-2000
• Keywords: Mood disorder; Voice features; Diagnosis; Child and adolescent; Classification accuracy; Pediatrics; RJ1-570; Psychiatry; RC435-571
• Source: Child and Adolescent Psychiatry and Mental Health, Vol 18, Iss 1, Pp 1-9 (2024)

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.