ArticleFrontiers in aging neuroscience2022
Evaluating the Different Stages of Parkinson's Disease Using Electroencephalography With Holo-Hilbert Spectral Analysis.
Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 30 citations in OpenAlex.
- Parkinson's disease classification using optimized attention-based deep learning from EEG signals with interpretable sub-band topography.Brain informatics · 2026Article
- A Hybrid Random Forest-SARSA Framework for Resting-State EEG-Based Parkinson's Disease Detection With Temporal Decision Refinement.Brain and behavior · 2026Article
- Digital Therapeutics for Alzheimer's and Parkinson's Diseases: Current Trends and Future Perspectives.Medicinal research reviews · 2026Review
- Independent component analysis of oddball EEG recordings to detect Parkinson's disease.Scientific reports · 2025Article
- Article
- Alterations in non-REM sleep and EEG spectra precede REM-sleep deficits in a model of synucleinopathy.Journal of Parkinson's disease · 2025Article
- Attentional dysfunction arises from right frontocentral and occipital network connectivity in Parkinson's disease.Neuroimage. Reports · 2025Article
- Electroencephalogram (EEG) Based Fuzzy Logic and Spiking Neural Networks (FLSNN) for Advanced Multiple Neurological Disorder Diagnosis.Brain topography · 2025Article
- Development of Neurodegenerative Disease Diagnosis and Monitoring from Traditional to Digital Biomarkers.Biosensors · 2025Review
- Exploring the complexity of EEG patterns in Parkinson's disease.GeroScience · 2025Article
- Article
- Hook Fabric Electroencephalography Electrode for Brain Activity Measurement without Shaving the Head.Polymers · 2023Article
- Amplitude modulated gamma oscillations as electrophysiological markers for repetitive transcranial magnetic stimulation efficacy in treatment-resistant depression: a randomized sham-controlled study.International journal of clinical and health psychology : IJCHPArticle
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11 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electroencephalography (EEG) can reveal the abnormalities of dopaminergic subcortico-cortical circuits in patients with Parkinson's disease (PD). However, conventional time-frequency analysis of EEG signals cannot fully reveal the non-linear processes of neural activities and interactions. A novel Holo-Hilbert Spectral Analysis (HHSA) was applied to reveal non-linear features of resting state EEG in 99 PD patients and 59 healthy controls (HCs). PD patients demonstrated a reduction of β bands in frontal and central regions, and reduction of γ bands in central, parietal, and temporal regions. Compared with early-stage PD patients, late-stage PD patients demonstrated reduction of β bands in the posterior central region, and increased θ and δ2 bands in the left parietal region. θ and β bands in all brain regions were positively correlated with Hamilton depression rating scale scores. Machine learning algorithms using three prioritized HHSA features demonstrated "Bag" with the best accuracy of 0.90, followed by "LogitBoost" with an accuracy of 0.89. Our findings strengthen the application of HHSA to reveal high-dimensional frequency features in EEG signals of PD patients. The EEG characteristics extracted by HHSA are important markers for the identification of depression severity and diagnosis of PD.
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