ArticleGeroScience2022
Analysis of complexity in the EEG activity of Parkinson's disease patients by means of approximate entropy.
Article in GeroScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- Frequency-Specific EEG burst dynamics in Parkinson's Disease and freezing of gait.Journal of neurology · 2026Article
- A Hybrid Random Forest-SARSA Framework for Resting-State EEG-Based Parkinson's Disease Detection With Temporal Decision Refinement.Brain and behavior · 2026Article
- Scalable biomarkers of Parkinson's disease: insights from mobile EEG in Peru.Scientific reports · 2026Article
- Task-specific dynamical entropy variations in EEG as a biomarker for Parkinson's disease progression.GeroScience · 2026Article
- Complexity Analysis of Skin Nerve Activity for Quantitative Assessment of Acute Sympathetic Nervous System Activation.Sensors (Basel, Switzerland) · 2026Article
- Neural Complexity of Implicit Attitudes Predicts Exercise Behavior in Hypertensive Patients: An EEG Entropy Study.Brain sciences · 2026Article
- Rapid Profiling of EEG Responses to Non-Invasive Brain Stimulation in Parkinson's Disease: A Biomarker-Driven Screening Framework.Biomedicines · 2026Article
- Entropy as a Tool for Quantifying Biomedical Signals: Gaps and Opportunities in Clinical Methodological Approaches-A Scoping Review.Biomedical engineering and computational biology · 2026Review
- A Hybrid Convolutional-Transformer Approach for Accurate Electroencephalography (EEG)-Based Parkinson's Disease Detection.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Significance of gender, brain region and EEG band complexity analysis for Parkinson's disease classification using recurrence plots and machine learning algorithms.Physical and engineering sciences in medicine · 2025Article
- From data to decisions: AI and functional connectivity for diagnosis, prognosis, and recovery prediction in stroke.GeroScience · 2025Article
- EEG entropy insights in the context of physiological aging and Alzheimer's and Parkinson's diseases: a comprehensive review.GeroScience · 2024Review
- Decoding influences of indoor temperature and light on neural activity: entropy analysis of electroencephalographic signals.Pflugers Archiv : European journal of physiology · 2024Article
- Why so slow? Models of parkinsonian bradykinesia.Nature reviews. Neuroscience · 2024Review
- Investigating Cortical Complexity in Mixed Dementia through Nonlinear Dynamic Analyses: A Resting-State EEG Study.Iranian journal of psychiatry · 2024Article
- Brain complexity in stroke recovery after bihemispheric transcranial direct current stimulation in mice.Brain communications · 2024Article
- The Role of Methionine-Rich Diet in Unhealthy Cerebrovascular and Brain Aging: Mechanisms and Implications for Cognitive Impairment.Nutrients · 2023Review
- Automatic detection of Parkinson's disease from power spectral density of electroencephalography (EEG) signals using deep learning model.Physical and engineering sciences in medicine · 2023Article
- A Bio-Inspired Chaos Sensor Model Based on the Perceptron Neural Network: Machine Learning Concept and Application for Computational Neuro-Science.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The objective of the present study is to explore the brain resting state differences between Parkinson's disease (PD) patients and age- and gender-matched healthy controls (elderly) in terms of complexity of electroencephalographic (EEG) signals. One non-linear approach to determine the complexity of EEG is the entropy. In this pilot study, 28 resting state EEGs were analyzed from 13 PD patients and 15 elderly subjects, applying approximate entropy (ApEn) analysis to EEGs in ten regions of interest (ROIs), five for each brain hemisphere (frontal, central, parietal, occipital, temporal). Results showed that PD patients presented statistically higher ApEn values than elderly confirming the hypothesis that PD is characterized by a remarkable modification of brain complexity and globally modifies the underlying organization of the brain. The higher-than-normal entropy of PD patients may describe a condition of low order and consequently low information flow due to an alteration of cortical functioning and processing of information. Understanding the dynamics of brain applying ApEn could be a useful tool to help in diagnosis, follow the progression of Parkinson's disease, and set up personalized rehabilitation programs.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.