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. 2021 Dec 1;9(1):38.
doi: 10.1186/s40345-021-00243-3.

Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states

Maria Faurholt-Jepsen #  1 , Darius Adam Rohani #  2 , Jonas Busk  3 , Maj Vinberg  4   5 , Jakob Eyvind Bardram  2 , Lars Vedel Kessing  4
Affiliations

Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states

Maria Faurholt-Jepsen et al. Int J Bipolar Disord. .

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.

Keywords: Bipolar disorder; Classification; Random Forest; Voice analysis; openSMILE.

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Conflict of interest statement

MFJ, DR, and JoB have no competing interests. MV has within the last 3 years been a consultant for Lundbeck, Sunovion and Janssen-Cilag. LVK has been a consultant for Lundbeck within the past 3 years. JB is a co-founder and shareholder in Monsenso.

Figures

Fig. 1
Fig. 1
A generated null distribution of AUC values from a permutation test where the class labels (e.g., patients with bipolar disorder and healthy controls) are randomly shuffled 200 times and an AUC value for each permutation is plotted. The light grey region represents the critical area with the 5% largest values. The vertical lines represent the observed AUC values from the true class labels. A Generated null-distribution for the Random Forest classification of patients with bipolar disorder against healthy control individuals. B Generated null-distribution for the Random Forest classification of patients with bipolar disorder against unaffected relatives
Fig. 2
Fig. 2
The association between Hamilton Depression Rating Scale 17-items score (HDRS), Young Mania Rating Scale (YMRS) and patient-reported smartphone-based data on mood. The grey line indicates the linear least square fit for each combination
Fig. 3
Fig. 3
The ROC curve for the classifications of different states based on voice features in patients with bipolar disorder. A The user-independent models; B the user-dependent models. Euphoric defined as combined increased mood and increased activity
See this image and copyright information in PMC

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