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Speech Quality Feature Analysis for Classification of Depression and Dementia Patients

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...

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Autores principales: Sumali, Brian, Mitsukura, Yasue, Liang, Kuo-ching, Yoshimura, Michitaka, Kitazawa, Momoko, Takamiya, Akihiro, Fujita, Takanori, Mimura, Masaru, Kishimoto, Taishiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7348868/
https://www.ncbi.nlm.nih.gov/pubmed/32604728
http://dx.doi.org/10.3390/s20123599
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author Sumali, Brian
Mitsukura, Yasue
Liang, Kuo-ching
Yoshimura, Michitaka
Kitazawa, Momoko
Takamiya, Akihiro
Fujita, Takanori
Mimura, Masaru
Kishimoto, Taishiro
author_facet Sumali, Brian
Mitsukura, Yasue
Liang, Kuo-ching
Yoshimura, Michitaka
Kitazawa, Momoko
Takamiya, Akihiro
Fujita, Takanori
Mimura, Masaru
Kishimoto, Taishiro
author_sort Sumali, Brian
collection PubMed
description 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.
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spelling pubmed-73488682020-07-22 Speech Quality Feature Analysis for Classification of Depression and Dementia Patients Sumali, Brian Mitsukura, Yasue Liang, Kuo-ching Yoshimura, Michitaka Kitazawa, Momoko Takamiya, Akihiro Fujita, Takanori Mimura, Masaru Kishimoto, Taishiro Sensors (Basel) Article 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. MDPI 2020-06-26 /pmc/articles/PMC7348868/ /pubmed/32604728 http://dx.doi.org/10.3390/s20123599 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sumali, Brian
Mitsukura, Yasue
Liang, Kuo-ching
Yoshimura, Michitaka
Kitazawa, Momoko
Takamiya, Akihiro
Fujita, Takanori
Mimura, Masaru
Kishimoto, Taishiro
Speech Quality Feature Analysis for Classification of Depression and Dementia Patients
title Speech Quality Feature Analysis for Classification of Depression and Dementia Patients
title_full Speech Quality Feature Analysis for Classification of Depression and Dementia Patients
title_fullStr Speech Quality Feature Analysis for Classification of Depression and Dementia Patients
title_full_unstemmed Speech Quality Feature Analysis for Classification of Depression and Dementia Patients
title_short Speech Quality Feature Analysis for Classification of Depression and Dementia Patients
title_sort speech quality feature analysis for classification of depression and dementia patients
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7348868/
https://www.ncbi.nlm.nih.gov/pubmed/32604728
http://dx.doi.org/10.3390/s20123599
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