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.
Highlights
Observer-based clinical rating scales such as the Hamilton Depression Rating Scale 17-item (HAMD)[1] and the Young Mania Rating Scale (YMRS)[2] are used as golden standards to assess the severity of depressive and manic symptoms when treating patients with bipolar disorder
We evaluated the ability to classify affective states building four different models including (1) voice features exclusively, (2) voice features combined with automatically generated objective data, (3) voice features combined with daily electronic self-monitoring data, and (4) voice features combined with automatically generated objective data and daily electronic self-monitoring data
In accordance with our hypotheses, we found that affective states in patients with bipolar disorder were classified by models based exclusively on voice features extracted during real-life phone calls in naturalistic settings
Summary
Observer-based clinical rating scales such as the Hamilton Depression Rating Scale 17-item (HAMD)[1] and the Young Mania Rating Scale (YMRS)[2] are used as golden standards to assess the severity of depressive and manic symptoms when treating patients with bipolar disorder. Studies analyzing the spoken language in affective disorders date back as early as 1938.5 A number of clinical observations suggest that reduced speech activity and changes in voice features such as pitch may be sensitive and valid measures of prodromal symptoms of depression and effect of treatment.[6,7,8,9,10,11,12] it has been suggested that increased speech activity may predict a switch to hypomania.[13] Item number eight on the HAMD (psychomotor retardation) and item number six on the YMRS (speech amount and rate) are both related to changes in speech, illustrating that factors related to speech activity are important aspects to evaluate in the assessment of symptoms’ severity in bipolar disorder. Based on these clinical observations there is an increasing interest in electronic systems for speech emotion recognition that can be used to extract useful semantics from speech and thereby provide information on the emotional state of the speaker (for example, information on pitch of the voice).[14]
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