Voice analysis as an objective state marker in bipolar disorder

Abstract

Changes in speech have been suggested as sensitive and valid measures of depression and mania in bipolar disorder. The present study aimed at investigating (1) voice features collected during phone calls as objective markers of affective states in bipolar disorder and (2) if combining voice features with automatically generated objective smartphone data on behavioral activities (for example, number of text messages and phone calls per day) and electronic self-monitored data (mood) on illness activity would increase the accuracy as a marker of affective states. Using smartphones, voice features, automatically generated objective smartphone data on behavioral activities and electronic self-monitored data were collected from 28 outpatients with bipolar disorder in naturalistic settings on a daily basis during a period of 12 weeks. Depressive and manic symptoms were assessed using the Hamilton Depression Rating Scale 17-item and the Young Mania Rating Scale, respectively, by a researcher blinded to smartphone data. Data were analyzed using random forest algorithms. Affective states were classified using voice features extracted during everyday life phone calls. Voice features were found to be more accurate, sensitive and specific in the classification of manic or mixed states with an area under the curve (AUC)=0.89 compared with an AUC=0.78 for the classification of depressive states. Combining voice features with automatically generated objective smartphone data on behavioral activities and electronic self-monitored data increased the accuracy, sensitivity and specificity of classification of affective states slightly. Voice features collected in naturalistic settings using smartphones may be used as objective state markers in patients with bipolar disorder.

High-Definition Medicine

“…Voice (Faurholt-Jepsen et al, 2016) and facial (Mone, 2015) analysis can be utilized to detect mood and psychiatric disturbances and has been used to diagnose Parkinson’s disease and heart disease (Maor et al, 2016), and can be used to differentiate simulated versus real pain (Bartlett et al, 2014). Eye tracking during behavioral tasks can identify individuals with mild cognitive impairment and predict future progression to Alzheimer’s disease (Zola et al, 2013).…”

Using artificial intelligence to assess clinicians’ communication skills

“…Some pioneers are already exploring the detection of disease by analysing speech patterns in patients 25. For instance, depressive episodes can be marked by systematic changes in vocal pitch,26 and early identification of heart failure may be feasible by measuring vocal changes arising from oedema in the vocal folds and lungs 27…”

Combining Continuous Smartphone Native Sensors Data Capture and Unsupervised Data Mining Techniques for Behavioral Changes Detection: A Case Series of the Evidence-Based Behavior (eB2) Study (Preprint)

“…Abdullah et al [9] reported that combining self-reported data with data from several smartphone sensors and communication patterns resulted in reliable prediction of Social Rhythm Metric, a clinically-validated marker of stability and rhythmicity for individuals with bipolar disorder. A different study [10] collected voice features that were automatically generated using smartphones from 28 outpatients on a daily basis during a period of 12 weeks.…”