ArticleCognitive neurodynamics2017
Multivariate multi-scale weighted permutation entropy analysis of EEG complexity for Alzheimer's disease.
Article in Cognitive neurodynamics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Task-specific dynamical entropy variations in EEG as a biomarker for Parkinson's disease progression.GeroScience · 2026Article
- Quantitative EEG signatures of power and functional connectivity alterations in Alzheimer's disease and frontotemporal dementia.Scientific reports · 2026Article
- The different impacts of functional network centrality and connectivity on the complexity of brain signals in healthy control and first-episode drug-naïve patients with major depressive disorder.Brain imaging and behavior · 2025Article
- Predicting progression from amnestic mild cognitive impairment to Alzheimer's disease using longitudinal EEG data: a 12-month cohort study.Frontiers in aging neuroscience · 2025Article
- Generalized Gaussian Distribution Improved Permutation Entropy: A New Measure for Complex Time Series Analysis.Entropy (Basel, Switzerland) · 2024Article
- Neuroimaging markers of aberrant brain activity and treatment response in schizophrenia patients based on brain complexity.Translational psychiatry · 2024Article
- Multi-Frequency Entropy for Quantifying Complex Dynamics and Its Application on EEG Data.Entropy (Basel, Switzerland) · 2024Article
- Is EEG Entropy a Useful Measure for Alzheimer's Disease?Actas espanolas de psiquiatria · 2024Review
- An in-depth analysis of parameter settings and probability distributions of specific ordinal patterns in the Shannon permutation entropy during different states of consciousness in humans.Journal of clinical monitoring and computing · 2024Article
- Comparison analysis between standard polysomnographic data and in-ear-electroencephalography signals: a preliminary study.Sleep advances : a journal of the Sleep Research Society · 2024Article
- An EEG-based framework for automated discrimination of conversion to Alzheimer's disease in patients with amnestic mild cognitive impairment: an 18-month longitudinal study.Frontiers in aging neuroscience · 2024Article
- TMS-EEG perturbation biomarkers for Alzheimer's disease patients classification.Scientific reports · 2023Article
- Comprehensive HRV estimation pipeline in Python using Neurokit2: Application to sleep physiology.MethodsX · 2022Article
- Alzheimer's Disease Analysis Algorithm Based on No-threshold Recurrence Plot Convolution Network.Frontiers in aging neuroscience · 2022Article
- Double Feature Extraction Method of Ship-Radiated Noise Signal Based on Slope Entropy and Permutation Entropy.Entropy (Basel, Switzerland) · 2021Article
- Coupling feature extraction method of resting state EEG Signals from amnestic mild cognitive impairment with type 2 diabetes mellitus based on weight permutation conditional mutual information.Cognitive neurodynamics · 2021Article
- Refined Composite Multi-Scale Reverse Weighted Permutation Entropy and Its Applications in Ship-Radiated Noise.Entropy (Basel, Switzerland) · 2021Article
- Novel channel selection method based on position priori weighted permutation entropy and binary gravity search algorithm.Cognitive neurodynamics · 2021Article
- The Significance of EEG Alpha Oscillation Spectral Power and Beta Oscillation Phase Synchronization for Diagnosing Probable Alzheimer Disease.Frontiers in aging neuroscience · 2021Article
- A New Feature Extraction Method Based on Improved Variational Mode Decomposition, Normalized Maximal Information Coefficient and Permutation Entropy for Ship-Radiated Noise.Entropy (Basel, Switzerland) · 2020Article
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7 authors.
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Abstract
The complexity change of brain activity in Alzheimer's disease (AD) is an interesting topic for clinical purpose. To investigate the dynamical complexity of brain activity in AD, a multivariate multi-scale weighted permutation entropy (MMSWPE) method is proposed to measure the complexity of electroencephalograph (EEG) obtained in AD patients. MMSWPE combines the weighted permutation entropy and the multivariate multi-scale method. It is able to quantify not only the characteristics of different brain regions and multiple time scales but also the amplitude information contained in the multichannel EEG signals simultaneously. The effectiveness of the proposed method is verified by both the simulated chaotic signals and EEG recordings of AD patients. The simulation results from the Lorenz system indicate that MMSWPE has the ability to distinguish the multivariate signals with different complexity. In addition, the EEG analysis results show that in contrast with the normal group, the significantly decreased complexity of AD patients is distributed in the temporal and occipitoparietal regions for the theta and the alpha bands, and also distributed from the right frontal to the left occipitoparietal region for the theta, the alpha and the beta bands at each time scale, which may be attributed to the brain dysfunction. Therefore, it suggests that the MMSWPE method may be a promising method to reveal dynamic changes in AD.
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