@article{ref1,
title="Automated voice biomarkers for depression symptoms using an online cross-sectional data collection initiative",
journal="Depression and anxiety",
year="2020",
author="Zhang, Larry and Duvvuri, Radhika and Chandra, Kiranmayi K. L. and Nguyen, Theresa and Ghomi, Reza H.",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="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>",
language="en",
issn="1091-4269",
doi="10.1002/da.23020",
url="http://dx.doi.org/10.1002/da.23020"
}