Bayesian time-history modeling enhances Parkinsonian motor state classification for adaptive deep brain stimulation.

Abstract

<i>Objective.</i>Adaptive deep brain stimulation (aDBS) for Parkinson's disease is a recently-approved therapy that adjusts stimulation in response to neurophysiologic biomarkers of motor-symptom state. Most real-time implementations of aDBS rely on instantaneous, noise-susceptible classifiers that apply simple thresholds to neurophysiologic biomarkers. We examined whether incorporating temporal history through Bayesian state-space modeling improved motor-state classification compared to instantaneous discriminant classifiers.<i>Approach</i>. We analyzed naturalistic neural data from three patients with Parkinson's disease chronically implanted with investigational sensing-enabled DBS systems, recording from both the subthalamic nucleus (STN) and sensorimotor cortex. Biomarkers were extracted across multiple window lengths and labeled using wearable-derived bradykinesia and dyskinesia scores. Classifier behavior was evaluated using two biomarkers (cortical stimulation-entrained gamma and STN beta oscillations) across a factorial combination of two conditions: (1) instantaneous discriminant analysis vs Bayesian time-history modeling via hidden Markov models (HMMs), and (2) single Gaussian vs Gaussian mixture modeling of each motor state's biomarker distribution. Performance metrics included<i>F</i>1 scores, accuracy, prediction smoothness, latency, and computational load.<i>Main Results</i>. Using entrained-gamma biomarkers, incorporating time history via HMMs significantly improved hyperkinetic-state detection (<i>F</i>1: +12.9 ± 1.8%; accuracy: +30.0 ± 2.7%; both<i>p</i>_adj< 0.001) with modest decreases in hypokinetic-state performance, yielding a net increase in average<i>F</i>1 (+4.7 ± 0.9%,<i>p</i>< 0.001). HMMs also yielded smoother and more accurate predictions for a given latency compared to simply increasing the window length used to extract neurophysiologic biomarkers. Entrained-gamma biomarkers outperformed STN beta biomarkers across all classifiers (average<i>F</i>1: +12.9% ± 0.5%,<i>p</i>< 0.001). All methods operated within sub-millisecond prediction times and demonstrated sublinear empirical computational scaling.<i>Significance</i>. Bayesian time-history modeling enhanced motor-state classification while preserving the low latency and computational efficiency required for real-time aDBS. These findings, derived from chronic at-home recordings, support the translational potential of Bayesian state-space models for next-generation aDBS systems.