Objective: To investigate the possibility of tremor detection based on deep brain activity.
Methods: We re-analyzed recordings of local field potentials (LFPs) from the subthalamic nucleus in 10
PD patients (12 body sides) with spontaneously fluctuating rest tremor. Power in several frequency bands
was estimated and used as input to Hidden Markov Models (HMMs) which classified short data segments
as either tremor-free rest or rest tremor. HMMs were compared to direct threshold application to individual
power features.
Results: Applying a threshold directly to band-limited power was insufficient for tremor detection (mean
area under the curve [AUC] of receiver operating characteristic: 0.64, STD: 0.19). Multi-feature HMMs, in
contrast, allowed for accurate detection (mean AUC: 0.82, STD: 0.15), using four power features obtained
from a single contact pair. Within-patient training yielded better accuracy than across-patient training
(0.84 vs. 0.78, p = 0.03), yet tremor could often be detected accurately with either approach. High frequency
oscillations (>200 Hz) were the best performing individual feature.
Conclusions: LFP-based markers of tremor are robust enough to allow for accurate tremor detection in
short data segments, provided that appropriate statistical models are used.
Significance: LFP-based markers of tremor could be useful control signals for closed-loop deep brain
stimulation.