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.

中文翻译：

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利用特征工程和卡尔曼滤波改进帕金森静止性震颤的检测

目的 准确可靠地检测帕金森病 (PD) 震颤发作对于适应性深部脑刺激 (aDBS) 治疗的成功至关重要。在这里，我们研究了特征工程和机器学习方法的潜在用途，以更准确地检测 PD 中的静止性震颤。方法 我们分析了 12 名 PD 患者丘脑底核区域的局部场电位 (LFP) 记录（16 个记录）。为了探索最佳生物标志物和性能最佳的分类器，对最先进的机器学习 (ML) 算法的性能和丘脑底 LFP 的各种特征进行了比较。我们进一步在特征域中使用卡尔曼滤波技术来降低误报率。结果与我们研究中的其他特征相比，Hjorth 复杂度与震颤具有更高的相关性。此外，通过顺序特征选择方法优化最多五个特征，并使用梯度提升决策树作为分类器，系统可以实现高达 88.7% 的平均 F1 分数和 0.52 s 的检测领先时间。在特征空间中使用卡尔曼滤波将特异性显着提高了 17.0% (p = 0.002)，从而有可能减少传统 DBS 系统不必要的功耗。结论 使用相关特征结合卡尔曼滤波和机器学习提高了休息时震颤检测的准确性。意义 该方法为 PD 震颤的有效按需刺激提供了潜在的解决方案。