ArticleScientific reports2022
Detection of Parkinson's disease from EEG signals using discrete wavelet transform, different entropy measures, and machine learning techniques.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Houston, We Have AI Problem! Quality Issues with Neuroimaging-Based Artificial Intelligence in Parkinson's Disease: A Systematic Review.Movement disorders : official journal of the Movement Disorder Society · 2024Pooled it
- Semi-supervised ensemble learning with interval type-2 fuzzy-rough sets for Parkinson's disease prediction from multi-omics.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
- Self-Explaining Neural Networks for Transparent Parkinson's Disease Screening.Sensors (Basel, Switzerland) · 2026Article
- Nonlinear Feature-Based MI Detection Supported by DWT and EMD on ECG: A High-Performance Decision Support Approach.Biosensors · 2026Article
- Discrete wavelet transform-driven optimized deep learning-based framework for dyslexia detection using EEG signals.Frontiers in neuroinformatics · 2026Article
- Topological resilience of EEG-based functional networks during virtual reality-induced emotional states.Frontiers in human neuroscience · 2026Article
- EEG classification for neurological disorders using frequency band deciles.Scientific reports · 2025Article
- Brain region localization: a rapid Parkinson's disease detection method based on EEG signals.Medical & biological engineering & computing · 2025Article
- Automatic differentiation of voluntary and tremulous motion using ensemble empirical mode decomposition and convolutional Bi-directional LSTM.Scientific reports · 2025Article
- Gait analysis for Parkinson's disease using multiscale entropy.Neurodegenerative disease management · 2025Review
- Recognition of EEG Features in Autism Disorder Using SWT and Fisher Linear Discriminant Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Knowledge, attitudes and practices of the general population towards Parkinson's disease in Meizhou, China: a cross-sectional study.BMJ open · 2025Article
- Independent component analysis of oddball EEG recordings to detect Parkinson's disease.Scientific reports · 2025Article
- A Hybrid Convolutional-Transformer Approach for Accurate Electroencephalography (EEG)-Based Parkinson's Disease Detection.Bioengineering (Basel, Switzerland) · 2025Article
- A Novel Fusion Framework Combining Graph Embedding Class-Based Convolutional Recurrent Attention Network with Brown Bear Optimization Algorithm for EEG-Based Parkinson's Disease Recognition.Journal of molecular neuroscience : MN · 2025Article
- Optimization of the Entropy-Based Wavelet Method for Removing Strong RF and AC Interferences in a Charge Detection Linear Ion Trap Mass Spectrometer.Analytical chemistry · 2025Article
- Development of Neurodegenerative Disease Diagnosis and Monitoring from Traditional to Digital Biomarkers.Biosensors · 2025Review
- Why so slow? Models of parkinsonian bradykinesia.Nature reviews. Neuroscience · 2024Review
- EEG-Based Detection of Mild Cognitive Impairment Using DWT-Based Features and Optimization Methods.Diagnostics (Basel, Switzerland) · 2024Article
- Selecting EEG channels and features using multi-objective optimization for accurate MCI detection: validation using leave-one-subject-out strategy.Scientific reports · 2024Article
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Abstract
Early detection of Parkinson's disease (PD) is very important in clinical diagnosis for preventing disease development. In this study, we present efficient discrete wavelet transform (DWT)-based methods for detecting PD from health control (HC) in two cases, namely, off-and on-medication. First, the EEG signals are preprocessed to remove major artifacts before being decomposed into several EEG sub-bands (approximate and details) using DWT. The features are then extracted from the wavelet packet-derived reconstructed signals using different entropy measures, namely, log energy entropy, Shannon entropy, threshold entropy, sure entropy, and norm entropy. Several machine learning techniques are investigated to classify the resulting PD/HC features. The effects of DWT coefficients and brain regions on classification accuracy are being investigated as well. Two public datasets are used to verify the proposed methods: the SanDiego dataset (31 subjects, 93 min) and the UNM dataset (54 subjects, 54 min). The results are promising and show that four entropy measures: log energy entropy, threshold entropy, sure entropy, and modified-Shannon entropy (TShEn) lead to high classification accuracy, indicating they are good biomarkers for PD detection. With the SanDiego dataset, the classification results of off-medication PD versus HC are 99.89, 99.87, and 99.91 for accuracy, sensitivity, and specificity, respectively, using the combination of DWT + TShEn and KNN classifier. Using the same combination, the results of on-medication PD versus HC are 94.21, 93.33, and 95%. With the UNM dataset, the obtained classification accuracy is around 99.5% in both cases of off-and on-medication PD using DWT + TShEn + SVM and DWT + ThEn + KNN, respectively. The results also demonstrate the importance of all DWT coefficients and that selecting a suitable small number of EEG channels from several brain regions could improve the classification accuracy.
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