QQWeiboFeedback 抑郁癥的輔助診斷研究——基于語音特征的探索 Alternative TitleAn Exploratory Study on Auxiliary Diagnosis of Depression Based on Speech 汪靜瑩 2017-04 Abstract

抑郁癥是一種以抑郁情緒為核心并伴隨多種癥狀的嚴(yán)重心理疾病。找到能夠有效反應(yīng)抑郁癥疾病狀態(tài)的客觀指標(biāo)有利于輔助抑郁癥的診斷。作為一種常見易得的行為線索，已有研究表明抑郁被試的語音和抑郁癥狀存在顯著相關(guān)，并且語音能夠用于區(qū)分抑郁癥和健康人。但是，通過語音自動檢測抑郁癥的方法當(dāng)前還有兩個重要問題有待探究：一是語音識別抑郁癥的診斷效力問題；二是語音的區(qū)分效果是否具備跨情境的穩(wěn)定性問題。診斷效力問題將通過逐層加深區(qū)分難度的方式，利用語音分別區(qū)分抑郁癥與不同癥狀相似程度的人群，考察最終可以利用語音區(qū)分抑郁和哪種類型的人群。研究中利用機(jī)器學(xué)習(xí)中的分類算法來考察語音特征識別抑郁癥的效果，通過分類算法中的如F值等指標(biāo)評價模型預(yù)測結(jié)果的好壞?？缜榫撤€(wěn)定性問題將通過比較不同實(shí)驗(yàn)情境、任務(wù)和情緒下的語音的預(yù)測結(jié)果來探討語音的區(qū)分效果是否具備跨情境的穩(wěn)定性。
研究一考察語音納入疾病之能力，旨在利用語音區(qū)分抑郁癥和非心理疾患，包括3個實(shí)驗(yàn)。實(shí)驗(yàn)1為重復(fù)測量設(shè)計(jì)，比較了39名健康人、26名雙相障礙和46名抑郁患者在不同情緒誘導(dǎo)下的情緒主觀體驗(yàn)之間的差異。該實(shí)驗(yàn)的結(jié)果說明通過自陳提供的情緒信息無法區(qū)分抑郁癥、雙相障礙和健康人。實(shí)驗(yàn)2為重復(fù)測量設(shè)計(jì)，收集了58名健康人和45名抑郁患者在不同任務(wù)和情緒下的語音用于分類預(yù)測。結(jié)果顯示利用語音區(qū)分抑郁癥患者和健康人的F值能達(dá)到78.2%的中等預(yù)測水平，且在不同任務(wù)和情緒下的預(yù)測結(jié)果都達(dá)到了中等預(yù)測水平。實(shí)驗(yàn)3利用語音特征區(qū)分759名抑郁患者和996名軀體疾病患者，結(jié)果顯示語音區(qū)分抑郁癥和軀體疾患的F值能達(dá)到75.6%。研究一的結(jié)果表明，語音特征能夠有效的用于區(qū)分抑郁癥患者和健康人、抑郁癥患者和軀體疾病患者，且區(qū)分效果具備跨情境的穩(wěn)定性。
研究二考察語音排除疾病之能力，旨在利用語音區(qū)分抑郁癥和其它心理疾患，包括2個實(shí)驗(yàn)。實(shí)驗(yàn)4利用語音特征區(qū)分抑郁癥患者內(nèi)部的不同子型，結(jié)果顯示語音區(qū)分抑郁癥中有無恐懼癥的F值能達(dá)到75%。實(shí)驗(yàn)5為重復(fù)測量設(shè)計(jì)，利用語音特征區(qū)分45名抑郁癥和17名雙相障礙、58名健康人，結(jié)果顯示：（1）語音區(qū)分抑郁癥和雙相障礙的F值能達(dá)到80.9%；（2）語音區(qū)分抑郁癥、雙相障礙和健康人的F值能達(dá)60.8%；（2）在不同任務(wù)和情緒下，語音的預(yù)測結(jié)果都達(dá)到了中等預(yù)測水平。研究二的結(jié)果表明，語音特征能夠有效的用于區(qū)分抑郁癥和與其癥狀相近的其它心理疾病，且區(qū)分效果具備跨情境的穩(wěn)定性。
本文采用柵欄式的研究方式，逐步探索語音區(qū)分不同人群的效果，最終發(fā)現(xiàn)通過語音能夠從抑郁癥和雙相障礙的混合人群中有效地識別出抑郁癥且區(qū)分效果具備跨情境的穩(wěn)定性。未來需要嘗試在更大規(guī)模的樣本上利用語音區(qū)分不同的抑郁程度和抑郁的不同子型。

Other Abstract

Major depression disorder (MDD) is a kind of mental illness which is accompany with a core symptom of depressive mood and various other symptoms. To improve the diagnostic effect, we are eager to find an objective indicator that could effectively reflect the real status of depression. Speech is one kind of easily available behavioral clue. Many studies indicated that there are significant correlations between acoustic features and depressive symptoms, and it is possible to identify depression via acoustic features. Nevertheless, there are still two questions need to be figured out: how about the diagnostic power of speech when used it to identify depression? Are the predictive effects of speech cross-situational consistency?
To figure out the question about diagnostic power of speech, we designed a series of experiments to identify depression for finding out the utmost of identification in our case. In our studies, we applied classification algorithms of machine learning to analyze the predictive effects of acoustic features, and used metrics like F-measure to estimate the predictive models. The question about cross-situational consistency of speech’ predictive effects was investigated by comparing predictive effects under different experimental situations, tasks and emotions.
The main aim of study 1 is discriminate depressed people from non-mental patients. Study 1 includes three experiments. Experiment 1 compared the subjective emotional experiences among depressed, bipolar and healthy people, the results suggested that it is hard to distinguish these three groups while used self-report emotions only. Experiment 2 found that acoustic features can be used to differentiate depression and healthy people, the F-measure was reach 78.2%. Besides, the predictive effects can reach moderate levels under different situations. Experiment 3 indicated that acoustic features can be applied to distinguish depressed people and physical patients, the best F-measure was 75.6%.
The main aim of study 2 is distinguish depressed people from other kinds of mental patients. Study 2 includes two experiments. Experiment 4 shown that acoustic features can be applied to differentiate depressed people with one kind of comorbidity and depressed people without this comorbidity, the F-measure reached 75%. The goal of experiment 5 is to distinguish depression from bipolar disorder. The results reported:
(1) acoustic features can be used to differentiate depression and bipolar disorder, the best F-measure was 80.9%; (2) acoustic identification was able to discriminate healthy, bipolar and depressed people, the best F-measure was 60.8%; (3) the predictive effects of acoustic features can reach moderate levels under different situations.
This paper explores the diagnostic power of speech, and find that depression could be effectively identified from the mixture crowd of depressed and bipolar people. And this diagnostic power has cross-situational consistency. In the future studies, researchers should try to identify different degrees of MDD or subtypes within MDD in a larger sample.

Keyword抑郁癥 輔助診斷 語音 分類 預(yù)測 Subtype博士 Language中文 Degree Discipline應(yīng)用心理學(xué) Degree Grantor中國科學(xué)院研究生院 Place of Conferral北京 Document Type學(xué)位論文 Identifierhttp://ir.psych.ac.cn/handle/311026/21403 Collection社會與工程心理學(xué)研究室
Affiliation中國科學(xué)院心理研究所
Recommended Citation
GB/T 7714 汪靜瑩. 抑郁癥的輔助診斷研究——基于語音特征的探索[D]. 北京. 中國科學(xué)院研究生院,2017. Files in This Item: File Name/Size DocType Version Access License 汪靜瑩-博士畢業(yè)論文.pdf（2989KB）學(xué)位論文 限制開放CC BY-NC-SAApplication Full Text Related ServicesRecommend this itemBookmarkUsage statisticsExport to EndnoteGoogle ScholarSimilar articles in Google Scholar[汪靜瑩]'s ArticlesBaidu academicSimilar articles in Baidu academic[汪靜瑩]'s ArticlesBing ScholarSimilar articles in Bing Scholar[汪靜瑩]'s ArticlesTerms of UseNo data!Social Bookmark/Share

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