Improved detection of Parkinsonian resting tremor with feature engineering and Kalman filtering

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.

Keywords: Adaptive deep-brain stimulation; Kalman filtering; Local field potential (LFP); Machine learning (ML); Parkinson’s disease (PD); Tremor detection.

Fig. 1

Overview of the proposed framework for tremor detection. The output of machine learning-based classifier can be used to activate DBS in an envisioned closed-loop system.

Fig. 2

(a) Tremor labeling based on acceleration signal, (b) the corresponding LFP. The red curve shows the envelope of the filtered acceleration around the tremor frequency, while the two vertical lines define the non-tremor period as baseline for threshold setting. The horizontal black line represents the threshold to separate the tremor and non-tremor periods; (c) Time-frequency decomposition of the acceleration signal, (d) and corresponding LFP (the y-axis is displayed in log scale). The color bars on the right indicate the log of the absolute power.

Fig. 3

Kalman filtering in feature space. The blue curve represents the original feature (low HFO power), while the red curve shows the corresponding feature following Kalman filtering.

Fig. 4

Latency calculation in an example patient. The time difference between the onset of classifier output (t_p) and the onset of labeled tremor (t_r) is defined as detection latency.

Fig. 5

Correlation coefficients of features with tremor. For each feature, the channel with the maximum correlation coefficient has been used. The error bars indicate the standard error.

Fig. 6

Performance of different classifiers in tremor detection, with and without Kalman filtering. The performance is measured by F1 score, sensitivity, and specificity. The error bar indicates the standard error.

Fig. 7

Performance of compact CNN on the training and validation sets across consecutive training epochs. The gray area indicates the standard error across patients.

Fig. 8

Examples of tremor detection on three sample LFP recordings. The bipolar LFP, measured acceleration, labeled tremor, and classifier output are shown. The binary output of XGB classifier that is built upon LFP features successfully tracks the episodes of tremor.

Fig. 9

Performance for different window sizes and overlaps; (a) F1 score, (b) sensitivity, (c) specificity, and (d) latency; (e) Performance for monopolar and bipolar configurations with a 1-s window and half overlap, and the boxplot of the corresponding latency on the right axis.

Fig. 10

The grand-averaged classification performance with respect to number of features using the sequential feature selection method. The arrow shows the setting that leads to the highest performance on average, using the same XGB model for all patients. The gray area indicates the standard error across patients.

Fig. 11

Distribution of the number of times a feature is selected across patients. A subject-specific number of features is used for each patient (min 1, max 5). Features selected from more than one channel in a patient are counted as one.