• Lin Yao, Peter Brown, and Mahsa Shoaran.
• ECE Department, Cornell University, Ithaca, NY, USA. Electronic address: ly329@cornell.edu.
• Clin Neurophysiol. 2020 Jan 1; 131 (1): 274-284.

ObjectiveAccurate and reliable detection of tremor onset in Parkinson's disease (PD) is critical to the success of adaptive deep brain stimulation (aDBS) therapy. Here, we investigated the potential use of feature engineering and machine learning methods for more accurate detection of rest tremor in PD.MethodsWe analyzed the local field potential (LFP) recordings from the subthalamic nucleus region in 12 patients with PD (16 recordings). To explore the optimal biomarkers and the best performing classifier, the performance of state-of-the-art machine learning (ML) algorithms and various features of the subthalamic LFPs were compared. We further used a Kalman filtering technique in feature domain to reduce the false positive rate.ResultsThe Hjorth complexity showed a higher correlation with tremor, compared to other features in our study. In addition, by optimal selection of a maximum of five features with a sequential feature selection method and using the gradient boosted decision trees as the classifier, the system could achieve an average F1 score of up to 88.7% and a detection lead of 0.52 s. The use of Kalman filtering in feature space significantly improved the specificity by 17.0% (p = 0.002), thereby potentially reducing the unnecessary power dissipation of the conventional DBS system.ConclusionThe use of relevant features combined with Kalman filtering and machine learning improves the accuracy of tremor detection during rest.SignificanceThe proposed method offers a potential solution for efficient on-demand stimulation for PD tremor.Copyright © 2019 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

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