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Diagnosis of Parkinson's disease (PD) is a challenging problem for medical community. Typically characterized by tremor, PD occurs due to the loss of dopamine in the brain's thalamic region that results in involuntary or oscillatory movement in the body. The early stage of the PD is referred as resting tremors, which appears when the muscles are relaxed. It is well known that surface EMG recording provides clinical information on the neuro-physiological characteristics of the tremors. This paper discusses the detection of resting tremors by extracting power spectral density (PSD) features from EMGs. Two methods namely, PSD by Welch and Burgs are applied by configuring the order of the predictors and are then classified using a recurrent neural network model, Elman Neural Network (REN). Experiments are performed using EMG patterns and statistical measures such as mean and maximum of PSD are used to classify the normal and abnormal PD subjects. It is found from the experimental results that the mean value of power spectral density by Burg with recurrent neural network classifier yields a classification accuracy of 95.6%. The proposed work need to be validated with larger datasets for real -time clinical application.