Improved detection of Parkinsonian resting tremor with feature engineering and Kalman filtering. Issue 1 (January 2020)

• Record Type: Journal Article
• Authors: Yao, Lin; Brown, Peter; Shoaran, Mahsa
• Is Part Of: Clinical neurophysiology. Volume 131:Issue 1(2020:Jan.)
• Page Start: 274
• Page End: 284
• Publication Date: 2020-01
• Subjects: Parkinson's disease (PD) -- Kalman filtering -- Machine learning (ML) -- Local field potential (LFP) -- Tremor detection -- Adaptive deep-brain stimulation; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2019.09.021
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: Multiple features of local field potentials in subthalamic nucleus were investigated to detect resting tremor in Parkinson's disease. The use of relevant features, machine learning, and Kalman filter improves the tremor detection performance. The Kalman filter in feature space significantly improves the specificity of detection by 17%. Abstract: Objective: Accurate 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. Methods: We 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. Results: The 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. Conclusion: The use of relevant features combined with Kalman filtering and machine learning improves the accuracy of tremor detection during rest. Significance: The proposed method offers a potential solution for efficient on-demand stimulation for PD tremor.