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.
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.
Authors: Ellen E Lee; John Torous; Munmun De Choudhury; Colin A Depp; Sarah A Graham; Ho-Cheol Kim; Martin P Paulus; John H Krystal; Dilip V Jeste Journal: Biol Psychiatry Cogn Neurosci Neuroimaging Date: 2021-02-08
Authors: Adam S Miner; Albert Haque; Jason A Fries; Scott L Fleming; Denise E Wilfley; G Terence Wilson; Arnold Milstein; Dan Jurafsky; Bruce A Arnow; W Stewart Agras; Li Fei-Fei; Nigam H Shah Journal: NPJ Digit Med Date: 2020-06-03
Authors: Junjie Zhang; Qingning Su; William G Loudon; Katherine L Lee; Jane Luo; Brent A Dethlefs; Shengwen Calvin Li Journal: J Funct Morphol Kinesiol Date: 2019-12-03
Authors: Anas Belouali; Samir Gupta; Vaibhav Sourirajan; Jiawei Yu; Nathaniel Allen; Adil Alaoui; Mary Ann Dutton; Matthew J Reinhard Journal: BioData Min Date: 2021-02-02 Impact factor: 2.522
Authors: Carole E Siegel; Eugene M Laska; Ziqiang Lin; Mu Xu; Duna Abu-Amara; Michelle K Jeffers; Meng Qian; Nicholas Milton; Janine D Flory; Rasha Hammamieh; Bernie J Daigle; Aarti Gautam; Kelsey R Dean; Victor I Reus; Owen M Wolkowitz; Synthia H Mellon; Kerry J Ressler; Rachel Yehuda; Kai Wang; Leroy Hood; Francis J Doyle; Marti Jett; Charles R Marmar Journal: Transl Psychiatry Date: 2021-04-20 Impact factor: 6.222