Vocal features obtained through automated methods in verbal fluency tasks can aid the identification of mixed episodes in bipolar disorder.

ABSTRACT: There is a lack of consensus on the diagnostic thresholds that could improve the detection accuracy of bipolar mixed episodes in clinical settings. Some studies have shown that voice features could be reliable biomarkers of manic and depressive episodes compared to euthymic states, but none thus far have investigated whether they could aid the distinction between mixed and non-mixed acute bipolar episodes. Here we investigated whether vocal features acquired via verbal fluency tasks could accurately classify mixed states in bipolar disorder using machine learning methods. Fifty-six patients with bipolar disorder were recruited during an acute episode (19 hypomanic, 8 mixed hypomanic, 17 with mixed depression, 12 with depression). Nine different trials belonging to four conditions of verbal

SUBMITTER: Weiner L 

PROVIDER: S-EPMC8329226 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature