Dysphoric patients show symptoms associated with Major Depression, although within a narrowed symptomatology spectrum. In prevailing practice, clinicians assess Dysphoria through psychological scores and questionnaires exclusively, therefore without taking into account objective biomarkers. In this study, we investigated heartbeat linear and nonlinear dynamics aiming to an objective assessment of Dysphoria. To this end, we derived standard and nonlinear measures from heart rate variability (HRV) series gathered from dysphoric (n=14) and nondysphoric (n=17) undergraduate students during 5 minutes of resting state. We performed both statistical and pattern recognition analyses in order to discern the two groups. Results showed significant group-wise differences in HRV nonlinear metrics exclusively, suggesting a crucial role of nonlinear sympatho-vagal dynamics in Dysphoria. Furthermore, we achieved a classification accuracy of 77.52% for the automatic identification of Dysphoria at a single-subject level.