Automated Voice Biomarkers for Depression Symptoms Using an Online Cross-Sectional Data Collection Initiative
21 Pages Posted: 27 Jun 2019More...
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
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