Speech‐based markers for posttraumatic stress disorder in US veterans

• Published in: Depression and anxiety Vol. 36; no. 7; pp. 607 - 616
• Main Authors: Marmar, Charles R., Brown, Adam D., Qian, Meng, Laska, Eugene, Siegel, Carole, Li, Meng, Abu‐Amara, Duna, Tsiartas, Andreas, Richey, Colleen, Smith, Jennifer, Knoth, Bruce, Vergyri, Dimitra
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.07.2019
• Subjects: Adult; Algorithms; Area Under Curve; biomarkers; diagnostics; feature extraction; Female; Humans; Male; Medical diagnosis; Mental depression; military; Post traumatic stress disorder; posttraumatic stress disorder; ROC Curve; Speech; Speech - physiology; speech‐based assessment; Statistical analysis; Stress Disorders, Post-Traumatic - complications; Stress Disorders, Post-Traumatic - diagnosis; Stress Disorders, Post-Traumatic - physiopathology; Veterans; speech-based assessment; diagnostics; veterans; feature extraction; biomarkers; military; posttraumatic stress disorder
• ISSN: 1091-4269; 1520-6394; 1520-6394
• DOI: 10.1002/da.22890

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.