Speech-based markers for posttraumatic stress disorder in US veterans
Speech-based markers for posttraumatic stress disorder in US veterans
复制标题
DOI：
10.1002/da.22890
复制
发表时间：
2019-07-01
影响因子：
7.4
通讯作者：
Vergyri, Dimitra
中科院分区：
文献类型：
--
作者：
Marmar, Charles R.;Brown, Adam D.;Vergyri, Dimitra
背景创伤后应激障碍(PTSD)的诊断通常基于临床访谈或自我报告方法。这两种方法都会受到症状报告不足和过度的影响。缺乏一项客观的测试。我们开发了一种基于客观语音标记特征的创伤后应激障碍分类器，该特征可以区分创伤后应激障碍患者和对照组。方法采用临床医生应用的创伤后应激障碍评定量表，对52例战区退伍军人、52例创伤后应激障碍患者和77例正常对照的语音样本进行评定。患有严重抑郁障碍(MDD)的个体被排除在外。临床访谈的录音被用来获得40,526个语音特征，这些特征被输入到随机森林(RF)算法。结果选择的RF使用18个语音特征，接收器工作特性曲线的曲线下面积(AUC)为0.954。在创伤后应激障碍分界点为0.423的概率下，Youden‘s指数为0.787，总体正确分类率为89.1%。如果标记物表明说话速度较慢、单调，音调变化较小，且较少激活，则患创伤后应激障碍的概率较高。抑郁症状、酒精使用障碍和脑损伤不符合被认为是混杂因素的统计测试。结论本研究表明，一种基于语音的算法可以客观地区分PTSD患者和对照组。RF分级机具有较高的AUC。需要在独立样本中进一步验证，并对分类器进行评估，以识别仅与创伤后应激障碍和MDD并存的患者相比的MDD患者。
Background The diagnosis of posttraumatic stress disorder (PTSD) is usually based on clinical interviews or self-report measures. Both approaches are subject to under- and over-reporting of symptoms. An objective test is lacking. We have developed a classifier of PTSD based on objective speech-marker features that discriminate PTSD cases from controls. Methods Speech samples were obtained from warzone-exposed veterans, 52 cases with PTSD and 77 controls, assessed with the Clinician-Administered PTSD Scale. Individuals with major depressive disorder (MDD) were excluded. Audio recordings of clinical interviews were used to obtain 40,526 speech features which were input to a random forest (RF) algorithm. Results The selected RF used 18 speech features and the receiver operating characteristic curve had an area under the curve (AUC) of 0.954. At a probability of PTSD cut point of 0.423, Youden's index was 0.787, and overall correct classification rate was 89.1%. The probability of PTSD was higher for markers that indicated slower, more monotonous speech, less change in tonality, and less activation. Depression symptoms, alcohol use disorder, and TBI did not meet statistical tests to be considered confounders. Conclusions This study demonstrates that a speech-based algorithm can objectively differentiate PTSD cases from controls. The RF classifier had a high AUC. Further validation in an independent sample and appraisal of the classifier to identify those with MDD only compared with those with PTSD comorbid with MDD is required.