Speech Quality Feature Analysis for Classification of Depression and Dementia Patients

Abstract

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.

Iniciação Científica - 24º Seminário de Pesquisa e 19º Seminário de Iniciação Científica

“…Por fim, Sumali et al (2020) realizaram a pesquisa para avaliar a detecção entre pacientes depressivos e pacientes com demência, os autores com o auxílio de machine learning conseguiram classificar a diferença entre pacientes depressivos e portadores de demência através da voz, entretanto os dados de áudio da pesquisa estavam contaminados com a voz do entrevistador, o que pode ter alterado os resultados, porém os pesquisadores com os dados obtidos consideram plausível a utilização de algoritmos para a identificação de doenças psíquicas.…”

Dementia Detection from Speech Using Machine Learning and Deep Learning Architectures

“…Haider et al [ 27 ] studied the statistical functionals of numerous speech features such as jitter, shimmer, MFCC, fundamental frequency etc. Various classifiers such as decision trees (DT), 1-nearest neighbour (1-NN), support vector machines (SVM) [ 28 ] and random forest (RF) were used to study the speech features and classify dementia. The hard fusion of results obtained from decision trees trained using distinct speech feature sets gave an overall accuracy of 78.7% [ 27 ].…”

Natural Language Processing as an Emerging Tool to Detect Late-Life Depression

“…One study compared LLD to those with early AD on a picture description task, and found that those with AD had reduced informativeness of their descriptions, suggesting that measures of content may be useful in differentiating depression from AD ( 51 ). Two recent studies suggested that certain acoustic speech features may help differentiate depression and dementia, or dementia with and without comorbid depression ( 66 , 71 ), but this topic requires further research.…”