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

University of Cologne, Köln, North Rhine-Westphalia, Germany
Bipolar Disorders (Impact Factor: 4.89). 11/2006; 8(5 Pt 1):424-9. DOI: 10.1111/j.1399-5618.2006.00373.x
Source: PubMed

ABSTRACT 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.
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.
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.
Non-linear measures may complement traditional linear measures in the analysis of longitudinal data in bipolar disorder. A larger study is indicated.

1 Follower
  • [Show abstract] [Hide abstract]
    ABSTRACT: Objectives We sought to study the underlying dynamic processes involved in mood regulation in subjects with bipolar disorder and healthy control subjects using time-series analysis and to then analyze the relation between anxiety and mood using cross-correlation techniques.Methods We recruited 30 healthy controls and 30 euthymic patients with bipolar disorder. Participants rated their mood, anxiety, and energy levels using a paper-based visual analog scale; and they also recorded their sleep and any life events. Information on these variables was provided over a three-month period on a daily basis, twice per day. We analyzed the data using Box–Jenkins time series analysis to obtain information on the autocorrelation of the series (for mood) and cross-correlation (mood and anxiety series).ResultsThroughout the study, we analyzed 10,170 data points. Self-ratings for mood, anxiety, and energy were normally distributed in both groups. Autocorrelation functions for mood in both groups were governed by the autoregressive integrated moving average (ARIMA) (1,1,0) model, which means that current values in the series were related to one previous point only. We also found a negative cross-correlation between mood and anxiety.Conclusions Mood can be considered a memory stochastic process; it is a flexible, dynamic process that has a ‘short memory’ both in healthy controls and euthymic patients with bipolar disorder. This process may be quite different in untreated patients or in those acutely ill. Our results suggest that nonlinear measures can be applied to the study of mood disorders.
    Bipolar Disorders 08/2014; 17(2). DOI:10.1111/bdi.12246 · 4.89 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The problem of automated theorem finding is one of 33 basic research problems in automated reasoning which was originally proposed by Wos in 1988, and it is still an open problem. To solve the problem, a forward deduction approach based on the strong relevant logics was proposed. To verify the effectiveness of the approach, this paper presents a case study of automated theorem finding in NBG set theory by forward deduction based on the strong relevant logics. The ultimate goal of automated theorem finding in NBG set theory is to find new and interesting theorems. As the first step, this case study tries to do “rediscovery” in NBG set theory, i.e., to deduce already proved theorems from axioms, definitions and /or other theorems of NBG set theory. However, from the viewpoint of the mechanism of deducing theorems, “re-discovery” is as same as “discovery”. The paper shows several known theorems rediscovered successfully by the approach. The paper also shows issues of the approach for real “discovery”.
    Machine Learning and Cybernetics (ICMLC), 2012 International Conference on; 01/2012
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: OBJECTIVE: The aim of this study was to identify psychopathological factors associated with long-term functional outcome in euthymic bipolar disorder patients and to test new measures of mood instability and symptoms intensity. METHOD: Fifty-five patients with more than 12 months of follow-up were included. In addition to traditional clinical variables, the time spent ill was documented using a modified life-charting technique based on NIHM life-charting method. New measures, Mood Instability Factor, and Mood Intensity Factor were defined and assessed. Functioning Assessment Short Test (FAST) was used to assess disability. RESULTS: The follow-up period was 3.00 ± 1.51 years. Weeks with subsyndromal depressive symptoms (β = 0.133, t = 2.556, P = 0.014), weeks with mild manic symptoms (β = 1.441, t = 3.10, P = 0.003), and the Mood Instability Factor (β = 0.105, t = 3.593, P = 0.001) contributed to approximately 46% of the FAST total score variance. CONCLUSION: New methodologies including subsyndromal symptoms and mood instability parameters might contribute to understand the worse long-term functional outcome that affects a considerable percentage of BD patients even after episode remission. Concerns about therapeutic approaches are discussed.
    Acta Psychiatrica Scandinavica 01/2013; DOI:10.1111/acps.12065 · 5.55 Impact Factor