Linear predictive coding distinguishes spectral EEG features of Parkinson's disease

Abstract

Objective: We have developed and validated a novel EEG-based signal processing approach to distinguish PD and control patients: Linear-predictive-coding EEG Algorithm for PD (LEAPD). This method efficiently encodes EEG time series into features that can detect PD in a computationally fast manner amenable to real time applications.

Methods: We included a total of 41 PD patients and 41 demographically-matched controls from New Mexico and Iowa. Data for all participants from New Mexico (27 PD patients and 27 controls) were used to evaluate in-sample LEAPD performance, with extensive cross-validation. Participants from Iowa (14 PD patients and 14 controls) were used for out-of-sample tests. Our method utilized data from six EEG leads which were as little as 2 min long.

Results: For the in-sample dataset, LEAPD differentiated PD patients from controls with 85.3 ± 0.1% diagnostic accuracy, 93.3 ± 0.5% area under the receiver operating characteristics curve (AUC), 87.9 ± 0.9% sensitivity, and 82.7 ± 1.1% specificity, with multiple cross-validations. After head-to-head comparison with state-of-the-art methods using our dataset, LEAPD showed a 13% increase in accuracy and a 15.5% increase in AUC. When the trained classifier was applied to a distinct out-of-sample dataset, LEAPD showed reliable performance with 85.7% diagnostic accuracy, 85.2% AUC, 85.7% sensitivity, and 85.7% specificity. No statistically significant effect of levodopa-ON and levodopa-OFF sessions were found.

Conclusion: We describe LEAPD, an efficient algorithm that is suitable for real time application and captures spectral EEG features using few parameters and reliably differentiates PD patients from demographically-matched controls.

Keywords: Classifier; Diagnosis; EEG; Parkinson's disease.

Figure 1.

Comparison of PSD shapes and band power between PD and control: (A) Comparison of EEG recordings (eyes open + closed) highlighting the difference in PSD shapes at 2.5–14 Hz between PD (n = 27) and control (n = 27) subjects (UNM data). (B) Comparison of theta (4–8 Hz), alpha (8–12 Hz) and beta (12–32 Hz) band power for the same data set. All plots: mean ± standard deviation.

Figure 2.

Classification method:(A) Detailed flow chart of a single-channel PD vs. control classifier. (B) Simplified diagram for the multi-channel classification with six single-classifier collaboration. (C) Performance comparison of 62 channels (UNM data). (D) Comparison of the 10 top-performing channels. (E) Channel positions of the 6 top-performing channels; red indicates selected or multi-channel classification.

Figure 3.

Classifier performance: (A) Performance evaluation of the proposed method with receiver-operative characteristic (ROC) curve (top left), accuracy (top right), and boxplots (bottom). (B) Performance comparison of the proposed method (LEAPD) with traditional approaches (Yuvaraj 2018 [8], Vanneste 2018 [10] and Chaturvedi 2017 [7]) using ROC curve (top left), accuracy (top right), and boxplots (bottom).