Department of Psychiatry, New York University School of Medicine, New York, New York.
Steven and Alexandra Cohen Veterans Center for the Study of Post-Traumatic Stress and Traumatic Brain Injury, New York, New York.
Depress Anxiety. 2019 Jul;36(7):607-616. doi: 10.1002/da.22890. Epub 2019 Apr 22.
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.
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.
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.
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.
创伤后应激障碍（PTSD）的诊断通常基于临床访谈或自我报告的测量方法。这两种方法都存在症状报告不足和过度报告的问题。目前缺乏客观的测试方法。我们已经开发了一种基于 PTSD 客观言语标记特征的分类器，可以将 PTSD 病例与对照区分开来。
从战区暴露的退伍军人中获取了语音样本，共有 52 例 PTSD 病例和 77 例对照，使用临床医生管理的 PTSD 量表进行评估。排除了患有重度抑郁症（MDD）的个体。使用临床访谈的音频记录来获取 40526 个语音特征，并将其输入随机森林（RF）算法。
所选的 RF 使用了 18 个语音特征，接收者操作特征曲线的曲线下面积（AUC）为 0.954。在 PTSD 概率切点为 0.423 时，Youden 的指数为 0.787，整体正确分类率为 89.1%。表明语速较慢、语调较单调、音调变化较小、活跃度较低的标记物的 PTSD 概率更高。抑郁症状、酒精使用障碍和 TBI 没有达到统计学检验标准，不能被认为是混杂因素。
这项研究表明，基于语音的算法可以客观地区分 PTSD 病例和对照。RF 分类器具有较高的 AUC。需要在独立样本中进一步验证，并评估该分类器以识别仅患有 MDD 的个体与患有 PTSD 合并 MDD 的个体。
