Speech Quality Feature Analysis for Classification of Depression and Dementia Patients

• Main Authors: Brian Sumali, Yasue Mitsukura, Kuo-ching Liang, Michitaka Yoshimura, Momoko Kitazawa, Akihiro Takamiya, Takanori Fujita, Masaru Mimura, Taishiro Kishimoto
• Format: Article
• Language: English
• Published: MDPI AG 2020-06-01
• Series: Sensors
• Subjects: pseudodementia; automated mental health screening; audio features; statistical testing; machine learning
• Online Access: https://www.mdpi.com/1424-8220/20/12/3599

Loss of cognitive ability is commonly associated with dementia, a broad category of progressive brain diseases.~However, major depressive disorder may also cause temporary deterioration of one’s cognition known as pseudodementia.~Differentiating a true dementia and pseudodementia is still difficult even for an experienced clinician and extensive and careful examinations must be performed. Although mental disorders such as depression and dementia have been studied, there is still no solution for shorter and undemanding pseudodementia screening. This study inspects the distribution and statistical characteristics from both dementia patient and depression patient, and compared them. It is found that some acoustic features were shared in both dementia and depression, albeit their correlation was reversed. Statistical significance was also found when comparing the features. Additionally, the possibility of utilizing machine learning for automatic pseudodementia screening was explored. The machine learning part includes feature selection using LASSO algorithm and support vector machine (SVM) with linear kernel as the predictive model with age-matched symptomatic depression patient and dementia patient as the database. High accuracy, sensitivity, and specificity was obtained in both training session and testing session. The resulting model was also tested against other datasets that were not included and still performs considerably well. These results imply that dementia and depression might be both detected and differentiated based on acoustic features alone. Automated screening is also possible based on the high accuracy of machine learning results.