A deep learning-based model for detecting depression in senior population

Abstract

ObjectivesWith the attention paid to the early diagnosis of depression, this study tries to use the biological information of speech, combined with deep learning to build a rapid binary-classification model of depression in the elderly who use Mandarin and test its effectiveness.MethodsDemographic information and acoustic data of 56 Mandarin-speaking older adults with major depressive disorder (MDD), diagnosed with the Mini-International Neuropsychiatric Interview (MINI) and the fifth edition of Diagnostic and Statistical Manual of Mental Disorders (DSM-5), and 47 controls was collected. Acoustic data were recorded using different smart phones and analyzed by deep learning model which is developed and tested on independent validation set. The accuracy of the model is shown by the ROC curve.ResultsThe quality of the collected speech affected the accuracy of the model. The initial sensitivity and specificity of the model were respectively 82.14% [95%CI, (70.16–90.00)] and 80.85% [95%CI, (67.64–89.58)].ConclusionThis study provides a new method for rapid identification and diagnosis of depression utilizing deep learning technology. Vocal biomarkers extracted from raw speech signals have high potential for the early diagnosis of depression in older adults.

A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder

“…In another study conducted in 2022, researchers collected demographic information and acoustic data from 56 Mandarin-speaking elderly individuals with Major Depressive Disorder (MDD). They developed a deep learning model to diagnose early-onset late-life depression using raw voice signals recorded by patients on their smartphones (Lin et al, 2022).…”

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“…Shin et al ( 9 ) suggested a speech biomarker machine learning model for the identification of moderate and serious depression in 2021. Lin et al ( 10 ) proposed a deep learning method for diagnosing depressive orders; Punithavathi et al ( 11 ) conducted an empirical investigation that demonstrated the potential of machine learning-based voice recognition techniques for depression prediction; and Shen et al ( 12 ) proposed a GRU/BiLSTM-based model for depression detection. The advantages and disadvantages of these studies are shown in Table 1 .…”

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