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. 2022 Apr 20;12(5):1033.
doi: 10.3390/diagnostics12051033.

Parkinson's Disease Detection from Resting-State EEG Signals Using Common Spatial Pattern, Entropy, and Machine Learning Techniques

Majid Aljalal  1 , Saeed A Aldosari  1 , Khalil AlSharabi  1 , Akram M Abdurraqeeb  1 , Fahd A Alturki  1
Affiliations

Parkinson's Disease Detection from Resting-State EEG Signals Using Common Spatial Pattern, Entropy, and Machine Learning Techniques

Majid Aljalal et al. Diagnostics (Basel). .

Abstract

Parkinson's disease (PD) is a very common brain abnormality that affects people all over the world. Early detection of such abnormality is critical in clinical diagnosis in order to prevent disease progression. Electroencephalography (EEG) is one of the most important PD diagnostic tools since this disease is linked to the brain. In this study, novel efficient common spatial pattern-based approaches for detecting Parkinson's disease in two cases, off-medication and on-medication, are proposed. First, the EEG signals are preprocessed to remove major artifacts before spatial filtering using a common spatial pattern. Several features are extracted from spatially filtered signals using different metrics, namely, variance, band power, energy, and several types of entropy. Machine learning techniques, namely, random forest, linear/quadratic discriminant analysis, support vector machine, and k-nearest neighbor, are investigated to classify the extracted features. The impacts of frequency bands, segment length, and reduction number on the results are also investigated in this work. The proposed methods are tested using two EEG datasets: the SanDiego dataset (31 participants, 93 min) and the UNM dataset (54 participants, 54 min). The results show that the proposed methods, particularly the combination of common spatial patterns and log energy entropy, provide competitive results when compared to methods in the literature. The achieved results in terms of classification accuracy, sensitivity, and specificity in the case of off-medication PD detection are around 99%. In the case of on-medication PD, the results range from 95% to 98%. The results also reveal that features extracted from the alpha and beta bands have the highest classification accuracy.

Keywords: Parkinson’s detection; common spatial pattern; discriminant analysis; electroencephalogram; entropy; k-nearest neighbor; machine learning; random forest; support vector machines.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Block diagram of the proposed PD CSP-based classification method.
Figure 2
Figure 2
The placement of the electrodes for the 32 EEG channels used in SanDiego dataset.
Figure 3
Figure 3
Power spectral density and electrode map for (a) Off−PD EEG (b) On−PD EEG (c) HC EEG.
Figure 4
Figure 4
Summary of the processing and classification stages for the training and testing phases.
Figure 5
Figure 5
Average classification accuracy (off–PD vs. HC) using FR, LDA, QDA, SVM, and KNN.
Figure 6
Figure 6
ROC and AUC of off–PD vs. HC classification based on features extracted from (a) CSP+Var, (b) CSP+Eng, (c) CSP+BP, (d) CSP+LogEn, (e) CSP+ShEn and (f) CSP+NoEn.
Figure 7
Figure 7
Classification accuracy of different EEG frequency bands using KNN (off–PD vs. HC).
Figure 8
Figure 8
Effect of reduction number on KNN classification accuracy applied to features extracted using CSP+Var (top) and CSP+LogEn (bottom).
Figure 9
Figure 9
Average classification accuracy (on–PD vs. HC) using LDA, SVM, KNN, and LR.
Figure 10
Figure 10
ROC and AUC of on–PD vs. HC classification based on features extracted from (a) CSP+Var, (b) CSP+Eng, (c) CSP+BP, (d) CSP+LogEn, (e) CSP+ShEn and (f) CSP+NoEn.
Figure 11
Figure 11
Classification accuracy of different EEG frequency bands using KNN of on–PD vs. HC.
Figure 12
Figure 12
The complete method that provides the best performance.
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