Abstract
This paper presents the results of a pilot study to assess the feasibility of using accelerometer data to estimate the severity of symptoms and motor complications in patients with Parkinson's disease. A support vector machine (SVM) classifier was implemented to estimate the severity of tremor, bradykinesia and dyskinesia from accelerometer data features. SVM-based estimates were compared with clinical scores derived via visual inspection of video recordings taken while patients performed a series of standardized motor tasks. The analysis of the video recordings was performed by clinicians trained in the use of scales for the assessment of the severity of Parkinsonian symptoms and motor complications. Results derived from the accelerometer time series were analyzed to assess the effect on the estimation of clinical scores of the duration of the window utilized to derive segments (to eventually compute data features) from the accelerometer data, the use of different SVM kernels and misclassification cost values, and the use of data features derived from different motor tasks. Results were also analyzed to assess which combinations of data features carried enough information to reliably assess the severity of symptoms and motor complications. Combinations of data features were compared taking into consideration the computational cost associated with estimating each data feature on the nodes of a body sensor network and the effect of using such data features on the reliability of SVM-based estimates of the severity of Parkinsonian symptoms and motor complications.
Highlights
P ARKINSON’s disease affects about 3% of the population over the age of 65 years and more than 500,000 U.S residents
A clinical expert examined the video recordings and provided clinical scores representing the severity of tremor, dyskinesia, and bradykinesia for each motor task performed by patients during each trial
Estimation error values were obtained by utilizing all feature types and by implementing support vector machine (SVM) using a third-order polynomial kernel
Summary
P ARKINSON’s disease affects about 3% of the population over the age of 65 years and more than 500,000 U.S residents. The characteristic motor features of the disease include tremor, bradykinesia (i.e., slowness of movement), rigidity (i.e., resistance to externally imposed movements), and impaired postural balance. Current therapy is based on augmentation or replacement of dopamine, using the biosynthetic precursor levodopa or drugs that activate dopamine receptors [1]. These therapies are successful for some time, but most patients eventually develop motor complications [2]. Complications include wearing-off, the abrupt loss of efficacy at the end of each dosing interval, and dyskinesias, involuntary and, at times, violent writhing movements. Variations in the severity of symptoms and motor complications (referred to as “motor fluctuations”) are observed during dosing intervals
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