Background and Objective: Parkinson's disease developed from essential tremor (ET-PD) is a distinct clinical syndrome that is different from essential tremor (ET) and Parkinson's disease (PD). There is currently a lack of research on ET-PD. Tremor characteristics (amplitude and frequency) are primary quantitative indexes for diagnosing and monitoring of tremors. In this study, we aimed to explore specific clinical and electrophysiological biomarkers for the identification of ET-PD. Methods: The study included patients with ET-PD (n = 22), ET (n = 42), and tremor-dominant PD (t-PD, n = 47). We collected demographic data, clinical characteristics (including motor and non-motor symptoms), and tremor analysis. The frequency, amplitude, contracting patterns of resting tremor and postural tremor were collected. The analysis of ET-PD and ET/t-PD was compared. The receiver operating characteristic (ROC) curve was used to analyze the electrophysiological features in distinguishing ET-PD from ET or t-PD. Results: Compared with ET, hyposmia, bradykinesia, rigidity, postural abnormality, and resting tremor were more common in the ET-PD group (P = 0.01, 0.003, 0.001, 0.001, 0.019, respectively). The postural tremor frequencies of the head, upper limbs, and lower limbs were significantly lower in the ET-PD than in the ET (P = 0.007, 0.003, 0.035, respectively), which were the most appropriate variables for distinguishing ET-PD from ET (AUC: 0.775, 0.727, and 0.701, respectively). Compared with t-PD, bradykinesia, rigidity, postural abnormality (both P < 0.001), and resting tremor (P = 0.024) were less common in the ET-PD. The postural tremor amplitudes of the head and upper limbs were significantly higher in the ET-PD than in the t-PD (P = 0.022, 0.001, respectively), which were the most appropriate variables for distinguishing ET-PD from t-PD (AUC: 0.793 and 0.716). Conclusions: Hyposmia and electrophysiological biomarkers (postural tremor frequencies and amplitudes) help early recognition of ET-PD. © Copyright © 2020 Wang, Cao, Liu, Liu, Jiang, Ma, Wang, Yang, Chen and Feng.