Abstract
The nature of mood variation in bipolar disorder has been the subject of relatively little research because detailed time series data has been difficult to obtain until recently. However some papers have addressed the subject and claimed the presence of deterministic chaos and of stochastic nonlinear dynamics. This study uses mood data collected from eight outpatients using a telemonitoring system. The nature of mood dynamics in bipolar disorder is investigated using surrogate data techniques and nonlinear forecasting. For the surrogate data analysis, forecast error and time reversal asymmetry statistics are used. The original time series cannot be distinguished from their linear surrogates when using nonlinear test statistics, nor is there an improvement in forecast error for nonlinear over linear forecasting methods. Nonlinear sample forecasting methods have no advantage over linear methods in out-of-sample forecasting for time series sampled on a weekly basis. These results can mean that either the original series have linear dynamics, the test statistics for distinguishing linear from nonlinear behaviour do not have the power to detect the kind of nonlinearity present, or the process is nonlinear but the sampling is inadequate to represent the dynamics. We suggest that further studies should apply similar techniques to more frequently sampled data.Electronic supplementary materialThe online version of this article (doi:10.1186/s40345-014-0011-z) contains supplementary material, which is available to authorized users.
Highlights
The increase in digital communication and measurement has generated new medical data sets for analysis
In the present study, we search for evidence of nonlinearity using linear surrogates, compare the expected prediction error of linear and nonlinear forecasting methods on eight selected patients
Full details of the test and results are given in Additional file 1: Section III. These results show that the eight depression time series used in this study cannot be distinguished from their linear surrogates using nonlinear and linear in-sample forecasting methods
Summary
The increase in digital communication and measurement has generated new medical data sets for analysis. These have in turn provided the opportunity to analyse biosignals which have hitherto been hard to measure. This study uses a new data set of depression time series recorded from outpatients who have bipolar disorder. Past work has claimed both deterministic chaos and stochastic nonlinearity for mood in bipolar disorder (Gottschalk et al 1995; Bonsall et al 2012). We address the latter claim directly and search for evidence of nonlinearity in eight selected time series. The challenges involved in collecting mood data from patients with bipolar disorder has influenced the kinds of
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