Towards Adaptive Deep Brain Stimulation in Parkinson's Disease: Lfp-Based Feature Analysis and Classification

Deep Brain Stimulation (DBS) is an established therapy for advanced Parkinson's disease (PD). Recent studies have applied the closed-loop control (adaptive DBS or aDBS) using feedback from local field potential (LFP) signals. However, current aDBS practices focus on simple feedback like beta band power and thresholding, without optimized control or classification algorithms. In this work, we study the capacity of several classifiers including automatic shrinkage linear discriminant analysis (LDA) to predict motor impairment. We use 20 features extracted from both monopolar and bipolar LFPs in 12 PD patients. In our best setting, we achieve a median accuracy of 70.2%, sensitivity of 81.2% and prediction lead time of 0.1 s across patients. By including relevant features other than beta power, a 13.6% improvement in accuracy is achieved. Moreover, the Hjorth parameters and high-frequency oscillation (HFO) features perform best according to the Analysis of Variance (ANOVA) p-value and classifier weights. These results suggest a great potential to improve current aDBS system for PD, by implementing a classifier with multiple features.