Abstract
Tremor is a neurological symptom which may result from Parkinson’s disease (PD). The time series specific to tremor is analyzed using tools derived from chaotic analysis, such as Lyapunov exponent, correlation dimension, and capacity dimension. This project focuses on the importance of the nonlinear dynamic parameters in Parkinson’s and essential tremor. Nonlinear dynamic parameters such as largest Lyapunov exponent, correlation dimension, and capacity dimension are estimated and plotted to compare the chaotic behavior of Parkinson’s and essential tremor.