Assessment of linear and nonlinear/complex heartbeat dynamics in subclinical depression (dysphoria)

Objective: Depression is one of the leading causes of disability worldwide. Most previous studies have focused on major depression, and studies on subclinical depression, such as those on so-called dysphoria, have been overlooked. Indeed, dysphoria is associated with a high prevalence of somatic disorders, and a reduction of quality of life and life expectancy. In current clinical practice, dysphoria is assessed using psychometric questionnaires and structured interviews only, without taking into account objective pathophysiological indices. To address this problem, in this study we investigated heartbeat linear and nonlinear dynamics to derive objective autonomic nervous system biomarkers of dysphoria. Approach: Sixty undergraduate students participated in the study: according to clinical evaluation, 24 of them were dysphoric. Extensive group-wise statistics was performed to characterize the pathological and control groups. Moreover, a recursive feature elimination algorithm based on a K-NN classifier was carried out for the automatic recognition of dysphoria at a single-subject level. Main results: The results showed that the most significant group-wise differences referred to increased heartbeat complexity (particularly for fractal dimension, sample entropy and recurrence plot analysis) with regards to the healthy controls, confirming dysfunctional nonlinear sympatho-vagal dynamics in mood disorders. Furthermore, a balanced accuracy of 79.17% was achieved in automatically distinguishing dysphoric patients from controls, with the most informative power attributed to nonlinear, spectral and polyspectral quantifiers of cardiovascular variability. Significance: This study experimentally supports the assessment of dysphoria as a defined clinical condition with specific characteristics which are different both from healthy, fully euthymic controls and from full-blown major depression.