Nonlinear Indices with Applications to Schizophrenia and Bipolar Disorder.

In this article we study the application of nonlinear indices (sometimes called complexity indices) to univariate time series data arising from studies of schizophrenia and bipolar disorder. Specifically, we consider time series arising from EEG studies, ECG studies, and self-report mood data. As part of our analysis, we empirically examine the claim in the literature that complexity tends to be higher in the EEG of schizophrenia patients than controls and that this tendency is dampened or even inverted by medication, increasing age, and reduced symptomatology.