Continuous Accelerometry-Based Tremor Detection During Daily Living.

As a step towards advancing adaptive DBS control for Parkinson's disease, we have developed an automated algorithm that detects tremor continuously on a seconds-resolution time scale from a wearable accelerometer and present the feasibility study test results. Triaxial acceleration data were wirelessly streamed from an Apple Watch as well as acquired from an internal accelerometer in the implanted DBS device itself. The algorithm first determines if there is any high-power voluntary activity, such as walking, using the arm, or transitioning from sitting to standing; then, it identifies peaks in the 4-7 Hz Parkinsonian tremor frequency band. Peak detection for tremor activity was more accurate using the Apple Watch than the IPG. Peak and harmonic detection were also more accurate using continuous wavelet transforms than short-time Fourier transform. According to the repeated measures correlation, our detection algorithm correlated strongly with DBS intensity (Subject RZCH: r = -0.93, p = 3.6 × 10-5; 6KOZ: r = -0.97, p = 1.6 × 10-5, NU5U: r = -0.94, p = 0.057). Pearson's correlation coefficient between our tremor detection algorithm and the currently accepted industry metric was found to be 0.57 (t-value = 8.5, dof = 148, p < 1 × 10-6). Our algorithm is distinctive in the capability to detect Parkinsonian tremor, with a high degree of clinical relevance, during daily living activities and is able to discriminate tremor from walking, using a convenient, commercial wrist-worn accelerometer.

Duke Scholars

Author Dennis Alan Turner Neurosurgery