Approximate entropy of self-reported mood prior to episodes in bipolar disorder

Background: Approximate entropy (ApEn) measures regularity in time series data, while traditional linear statistics measure variability. Using self-reported mood data from patients with bipolar disorder, this preliminary study addressed whether ApEn could distinguish (i) the 60 days prior to the start of a manic or depressed episode from the 60 days prior to a month of euthymia, and (ii) the 60 days prior to a manic episode from the 60 days prior to a depressed episode. Methods: Self-reported mood data from 49 outpatients with bipolar disorder receiving standard treatment were analysed. The data contained 27 episodes (12 manic and 15 depressed), and 43 periods of 1 month of euthymia. For the 60 days prior to episode or euthymia, the ApEn, linear statistics and the correlation between linear and non-linear measures were calculated. Results: ApEn was significantly greater in the 60 days prior to a manic or depressive episode than the 60 days prior to a month of euthymia. The onset of an episode was associated with greater irregularity in mood. Variability was also significantly larger and correlated with ApEn. ApEn was significantly greater in the 60 days prior to a manic episode than in the 60 days prior to a depressed episode, whereas measures of variability were not significantly different. Mood in the 60 days prior to mania was more irregular than prior to depression. Conclusions: Non-linear measures may complement traditional linear measures in the analysis of longitudinal data in bipolar disorder. A larger study is indicated.