TY  - JOUR
PY  - 2020//
TI  - Automated voice biomarkers for depression symptoms using an online cross-sectional data collection initiative
JO  - Depression and anxiety
A1  - Zhang, Larry
A1  - Duvvuri, Radhika
A1  - Chandra, Kiranmayi K. L.
A1  - Nguyen, Theresa
A1  - Ghomi, Reza H.
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - IMPORTANCE: Depression is an illness affecting a large percentage of the world's population throughout the lifetime. To date, there is no available biomarker for depression detection and tracking of symptoms relies on patient self-report. <br><br>OBJECTIVE: To explore and validate features extracted from recorded voice samples of depressed subjects as digital biomarkers for suicidality, psychomotor disturbance, and depression severity. <br><br>DESIGN: We conducted a cross-sectional study over the course of 12 months using a frequently visited web form version of the PHQ9 hosted by Mental Health America (MHA) to ask subjects for anonymous voice samples via a separate web form hosted by NeuroLex Laboratories. Subjects were asked to provide demographics, answers to the PHQ9, and two voice samples. SETTING: Online only. PARTICIPANTS: Users of the MHA website. <br><br>MAIN OUTCOMES AND MEASURES: Performance of statistical models using extracted voice features to predict psychomotor disturbance, suicidality, and depression severity as indicated by the PHQ9. <br><br>RESULTS: Voice features extracted from recorded audio of depressed subjects were able to predict PHQ9 question 9 and total scores with an area under the curve of 0.821 and a mean absolute error of 4.7, respectively. Psychomotor Disturbance prediction was less powerful with an area under the curve of 0.61. <br><br>CONCLUSION AND RELEVANCE: Automated voice analysis using short recordings of patient speech may be used to augment depression screen and symptom management.<br><br>© 2020 Wiley Periodicals, Inc.<p />  <p>Language: en</p>
LA  - en
SN  - 1091-4269
UR  - http://dx.doi.org/10.1002/da.23020
ID  - ref1
ER  -