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Automated Voice Biomarkers for Depression Symptoms Using an Online Cross-Sectional Data Collection Initiative

21 Pages Posted: 27 Jun 2019

See all articles by Larry Zhang

Larry Zhang

NeuroLex Laboratories

Radhika Duvvuri

Johns Hopkins University - Bloomberg School of Public Health

Kiranmayi Konda Lakshmi Chandra

University of Washington

Theresa Nguyen

Mental Health America

Reza Hosseini Ghomi

NeuroLex Laboratories

More...

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 and detection and tracking of symptoms relies on patient self-report.

Objective: To explore and validate features extracted from recorded voice samples of depressed subjects as digital biomarkers for suicidality, psychomotor retardation, and depression severity.

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.

Main Outcomes and Measures: Performance of statistical models using extracted voice features to predict psychomotor retardation, suicidality, and depression severity as indicated by the PHQ9.

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 0.85 and a mean absolute error of 4.7, respectively. Psychomotor retardation prediction was less powerful with an area under the curve of 0.61.

Conclusion and Relevance: Automated voice analysis using short recordings of patient speech may be used to augment depression screen and symptom management.

Funding Statement: Funding for this study was provided by NeuroLex Laboratories, Inc. Funding supported study staff time only and there were no honoraria provided to participants. Our study partner, Mental Health America, provided depression survey and demographic data in addition to directing users to the voice survey.

Declaration of Interests: The authors have no conflicts to disclose except for: Dr. Hosseini Ghomi is a stockholder of NeuroLex Laboratories, Inc.

Ethics Approval Statement: Ethical oversight of the study was provided by the Western Institutional Review Board (WIRB #1174369). Subjects who participated were required to complete an online consent process.

Keywords: Depression; Digital Biomarkers; Voice Biomarkers; PHQ9; Online Data Collection

Suggested Citation

Zhang, Larry and Duvvuri, Radhika and Chandra, Kiranmayi Konda Lakshmi and Nguyen, Theresa and Hosseini Ghomi, Reza, Automated Voice Biomarkers for Depression Symptoms Using an Online Cross-Sectional Data Collection Initiative (06/20/2019 18:16:34). Available at SSRN: https://ssrn.com/abstract=3408093 or http://dx.doi.org/10.2139/ssrn.3408093

Larry Zhang

NeuroLex Laboratories

Newnan, GA
United States

Radhika Duvvuri

Johns Hopkins University - Bloomberg School of Public Health

India

Kiranmayi Konda Lakshmi Chandra

University of Washington

Seattle, WA 98195
United States

Theresa Nguyen

Mental Health America

United States

Reza Hosseini Ghomi (Contact Author)

NeuroLex Laboratories ( email )

Newnan, GA
United States

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