Analysis of Hand Tremor in Parkinson’s Disease: Frequency Domain Approach

Abstract

Purpose: Parkinson's Disease (PD) is a neuro-degenerative interminable issue causing dynamic loss of dopamine-creating synapses, which is one of the most far reaching ailments after Alzheimer's infection. In this paper, a system for the classification of Parkinson’s disease tremor using noninvasive measurement and frequency domain features is represented. Materials and Methods: Tremor time-series of Parkinson's disease patients were recorded via a smartphone’s accelerometer sensor. Short-Time Fourier Transform (STFT) was applied to transform the time-domain signal into the frequency domain with high time-frequency resolution. Several frequency features, including mean, max of power spectral density and side frequency have been extracted and by using the FDR algorithm combinations of features carried enough information to reliably assess the severity of tremor in Parkinson patients were determined. Results: Four different classifiers were implemented to estimate the severity of tremors based on the Unified Parkinson's Disease Rating Scale (UPDRS) in Parkinson's disease patients. Conclusion: Classifiers’ estimation was compared to clinical scores derived via neurologist UPDRS annotation on Parkinson's disease patients’ tremor. The best accuracy achieved was 95.91±1.51.