Voice Analyses Using Smartphone-based Data in Patients with Bipolar Disorder, Unaffected Relatives and Healthy Control Individuals, and During Different Affective States

Overview

Journal Int J Bipolar Disord

Date 2021 Dec 1

PMID 34850296

Citations 4

Authors

Maria Faurholt-Jepsen

Darius Adam Rohani

Jonas Busk

Maj Vinberg

Jakob Eyvind Bardram

Lars Vedel Kessing

Affiliations

Soon will be listed here.

Abstract

Background: Voice features have been suggested as objective markers of bipolar disorder (BD).

Aims: To investigate whether voice features from naturalistic phone calls could discriminate between (1) BD, unaffected first-degree relatives (UR) and healthy control individuals (HC); (2) affective states within BD.

Methods: Voice features were collected daily during naturalistic phone calls for up to 972 days. A total of 121 patients with BD, 21 UR and 38 HC were included. A total of 107.033 voice data entries were collected [BD (n = 78.733), UR (n = 8004), and HC (n = 20.296)]. Daily, patients evaluated symptoms using a smartphone-based system. Affective states were defined according to these evaluations. Data were analyzed using random forest machine learning algorithms.

Results: Compared to HC, BD was classified with a sensitivity of 0.79 (SD 0.11)/AUC = 0.76 (SD 0.11) and UR with a sensitivity of 0.53 (SD 0.21)/AUC of 0.72 (SD 0.12). Within BD, compared to euthymia, mania was classified with a specificity of 0.75 (SD 0.16)/AUC = 0.66 (SD 0.11). Compared to euthymia, depression was classified with a specificity of 0.70 (SD 0.16)/AUC = 0.66 (SD 0.12). In all models the user dependent models outperformed the user independent models. Models combining increased mood, increased activity and insomnia compared to periods without performed best with a specificity of 0.78 (SD 0.16)/AUC = 0.67 (SD 0.11).

Conclusions: Voice features from naturalistic phone calls may represent a supplementary objective marker discriminating BD from HC and a state marker within BD.

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