Photoplethysmogram (PPG) signals obtained from wearable sensors have been utilized for monitoring health conditions in both clinical and non-clinical environments, mostly concerning with heart-rate events. This paper shows the potential use of short-time PPG signals for differentiating patients with Parkinsons disease (PD) from healthy control (HC) subjects with nonlinear dynamics analysis. Multiscale entropy, time-shift multiscale entropy, and fuzzy recurrence plots were applied for extracting features from PPG signals of PD patients and HC subjects. Least-square support vector machine based cross-validations of the features extracted from the three nonlinear dynamics analysis methods achieve high classification rates, where those obtained from fuzzy recurrence plots are the highest.