Implications of Aperiodic and Periodic EEG Components in Classification of Major Depressive Disorder from Source and Electrode Perspectives.

Electroencephalography (EEG) is useful for studying brain activity in major depressive disorder (MDD), particularly focusing on theta and alpha frequency bands via power spectral density (PSD). However, PSD-based analysis has often produced inconsistent results due to difficulties in distinguishing between periodic and aperiodic components of EEG signals. We analyzed EEG data from 114 young adults, including 74 healthy controls (HCs) and 40 MDD patients, assessing periodic and aperiodic components alongside conventional PSD at both source and electrode levels.

Beta-to-Theta Entropy Ratio of EEG in Aging, Frontotemporal Dementia, and Alzheimer's Dementia.

Background: Aging, frontotemporal dementia (FTD), and Alzheimer's dementia (AD) manifest electroencephalography (EEG) alterations, particularly in the beta-to-theta power ratio derived from linear power spectral density (PSD). Given the brain's nonlinear nature, the EEG nonlinear features could provide valuable physiological indicators of aging and cognitive impairment. Multiscale dispersion entropy (MDE) serves as a sensitive nonlinear metric for assessing the information content in EEGs across biologically relevant time scales.

NLDyn - An open source MATLAB toolbox for the univariate and multivariate nonlinear dynamical analysis of physiological data.

Background And Objective: We present NLDyn, an open-source MATLAB toolbox tailored for in-depth analysis of nonlinear dynamics in biomedical signals. Our objective is to offer a user-friendly yet comprehensive platform for researchers to explore the intricacies of time series data.

Methods: NLDyn integrates approximately 80 distinct methods, encompassing both univariate and multivariate nonlinear dynamics, setting it apart from existing solutions.

Refined Composite Multiscale Fuzzy Dispersion Entropy and Its Applications to Bearing Fault Diagnosis.

Rotary machines often exhibit nonlinear behavior due to factors such as nonlinear stiffness, damping, friction, coupling effects, and defects. Consequently, their vibration signals display nonlinear characteristics. Entropy techniques prove to be effective in detecting these nonlinear dynamic characteristics.

Assessment and Comparison of Nonlinear Measures in Resting-State Magnetoencephalograms in Alzheimer's Disease and Mild Cognitive Impairment.

Background: Nonlinear dynamical measures, such as fractal dimension (FD), entropy, and Lempel-Ziv complexity (LZC), have been extensively investigated individually for detecting information content in magnetoencephalograms (MEGs) from patients with Alzheimer's disease (AD).

Objective: To compare systematically the performance of twenty conventional and recently introduced nonlinear dynamical measures in studying AD versus mild cognitive impairment (MCI) and healthy control (HC) subjects using MEG.

Methods: We compared twenty nonlinear measures to distinguish MEG recordings from 36 AD (mean age = 74.

Bidimensional ensemble entropy: Concepts and application to emphysema lung computerized tomography scans.

Background And Objective: Bidimensional entropy algorithms provide meaningful quantitative information on image textures. These algorithms have the advantage of relying on well-known one-dimensional entropy measures dedicated to the analysis of time series. However, uni- and bidimensional algorithms require the adjustment of some parameters that influence the obtained results or even findings.

Beta to theta power ratio in EEG periodic components as a potential biomarker in mild cognitive impairment and Alzheimer's dementia.

Background: Alzheimer's dementia (AD) is associated with electroencephalography (EEG) abnormalities including in the power ratio of beta to theta frequencies. EEG studies in mild cognitive impairment (MCI) have been less consistent in identifying such abnormalities. One potential reason is not excluding the EEG aperiodic components, which are less associated with cognition than the periodic components.

EEG Entropy in REM Sleep as a Physiologic Biomarker in Early Clinical Stages of Alzheimer's Disease.

Background: Alzheimer's disease (AD) is associated with EEG changes across the sleep-wake cycle. As the brain is a non-linear system, non-linear EEG features across behavioral states may provide an informative physiologic biomarker of AD. Multiscale fluctuation dispersion entropy (MFDE) provides a sensitive non-linear measure of EEG information content across a range of biologically relevant time-scales.

Detection of Oculomotor Dysmetria From Mobile Phone Video of the Horizontal Saccades Task Using Signal Processing and Machine Learning Approaches.

Eye movement assessments have the potential to help in diagnosis and tracking of neurological disorders. Cerebellar ataxias cause profound and characteristic abnormalities in smooth pursuit, saccades, and fixation. Oculomotor dysmetria (i.

Bearing Fault Diagnosis Using Refined Composite Generalized Multiscale Dispersion Entropy-Based Skewness and Variance and Multiclass FCM-ANFIS.

Bearing vibration signals typically have nonlinear components due to their interaction and coupling effects, friction, damping, and nonlinear stiffness. Bearing faults affect the signal complexity at various scales. Hence, measuring signal complexity at different scales is helpful to diagnosis of bearing faults.

Measures of resting state EEG rhythms for clinical trials in Alzheimer's disease: Recommendations of an expert panel.

The Electrophysiology Professional Interest Area (EPIA) and Global Brain Consortium endorsed recommendations on candidate electroencephalography (EEG) measures for Alzheimer's disease (AD) clinical trials. The Panel reviewed the field literature. As most consistent findings, AD patients with mild cognitive impairment and dementia showed abnormalities in peak frequency, power, and "interrelatedness" at posterior alpha (8-12 Hz) and widespread delta (< 4 Hz) and theta (4-8 Hz) rhythms in relation to disease progression and interventions.

A pilot protocol to assess the feasibility of a virtual multiple crossover, randomized controlled trial design using methylphenidate in mild cognitive impairment.

Background: The conventional clinical trial design in Alzheimer's disease (AD) and AD-related disorders (ADRDs) is the parallel-group randomized controlled trial. However, in heterogeneous disorders like AD/ADRDs, this design requires large sample sizes to detect meaningful effects in an "average" patient. They are very costly and, despite many attempts, have not yielded new treatments for many years.

Bidimensional Multiscale Fuzzy Entropy and Its Application to Pseudoxanthoma Elasticum.

Objective: We propose a new bidimensional entropy measure and its multiscale form and evaluate their behavior using various synthetic and real images. The bidimensional multiscale measure finds application in helping clinicians for pseudoxanthoma elasticum (PXE) detection in dermoscopic images.

Method: We developed bidimensional fuzzy entropy ( FuzEn) and its multiscale extension ( MSF) and then evaluated them on a set of synthetic images and texture datasets.

Amplitude- and Fluctuation-Based Dispersion Entropy.

Dispersion entropy (DispEn) is a recently introduced entropy metric to quantify the uncertainty of time series. It is fast and, so far, it has demonstrated very good performance in the characterisation of time series. It includes a mapping step, but the effect of different mappings has not been studied yet.

Coarse-Graining Approaches in Univariate Multiscale Sample and Dispersion Entropy.

The evaluation of complexity in univariate signals has attracted considerable attention in recent years. This is often done using the framework of Multiscale Entropy, which entails two basic steps: coarse-graining to consider multiple temporal scales, and evaluation of irregularity for each of those scales with entropy estimators. Recent developments in the field have proposed modifications to this approach to facilitate the analysis of short-time series.

The Influence of Synthesis Parameters on Vertically Aligned CNT Sheets: Empirical Modeling and Process Optimization Using Response Surface Methodology.

In the present work, vertically aligned carbon nanotube (VA-CNT) sheets were synthesized via pyrolysis of polybenzimidazole (PBI)-Kapton inside the pores of anodized aluminum oxide (AAO). The synthesized VA-CNT sheets were then evaluated for the desalination of salty water. The results indicated that the VA-CNT sheets were effective for application as an adsorbent for desalination of salty water due to their high adsorption capacity, with no loss of CNTs in the treated water.

Multiscale dispersion entropy for the regional analysis of resting-state magnetoencephalogram complexity in Alzheimer's disease.

Alzheimer's disease (AD) is a progressive and irreversible brain disorder of the nervous system affecting memory, thinking, and emotion. It is the most important cause of dementia and an influential social problem in all the world. The complexity of brain recordings has been successfully used to help to characterize AD.

Refined multiscale fuzzy entropy based on standard deviation for biomedical signal analysis.

Multiscale entropy (MSE) has been a prevalent algorithm to quantify the complexity of biomedical time series. Recent developments in the field have tried to alleviate the problem of undefined MSE values for short signals. Moreover, there has been a recent interest in using other statistical moments than the mean, i.

Refined Composite Multiscale Dispersion Entropy and its Application to Biomedical Signals.

Objective: We propose a novel complexity measure to overcome the deficiencies of the widespread and powerful multiscale entropy (MSE), including, MSE values may be undefined for short signals, and MSE is slow for real-time applications.

Methods: We introduce multiscale dispersion entropy (DisEn-MDE) as a very fast and powerful method to quantify the complexity of signals. MDE is based on our recently developed DisEn, which has a computation cost of O(N), compared with O(N) for sample entropy used in MSE.

Dispersion entropy for the analysis of resting-state MEG regularity in Alzheimer's disease.

Alzheimer's disease (AD) is a progressive degenerative brain disorder affecting memory, thinking, behaviour and emotion. It is the most common form of dementia and a big social problem in western societies. The analysis of brain activity may help to diagnose this disease.

MEMD-enhanced multivariate fuzzy entropy for the evaluation of complexity in biomedical signals.

Multivariate multiscale entropy (mvMSE) has been proposed as a combination of the coarse-graining process and multivariate sample entropy (mvSE) to quantify the irregularity of multivariate signals. However, both the coarse-graining process and mvSE may not be reliable for short signals. Although the coarse-graining process can be replaced with multivariate empirical mode decomposition (MEMD), the relative instability of mvSE for short signals remains a problem.

Inspection of short-time resting-state electroencephalogram functional networks in Alzheimer's disease.

Functional connectivity has proven useful to characterise electroencephalogram (EEG) activity in Alzheimer's disease (AD). However, most current functional connectivity analyses have been static, disregarding any potential variability of the connectivity with time. In this pilot study, we compute short-time resting state EEG functional connectivity based on the imaginary part of coherency for 12 AD patients and 11 controls.

Amplitude-aware permutation entropy: Illustration in spike detection and signal segmentation.

Background And Objective: Signal segmentation and spike detection are two important biomedical signal processing applications. Often, non-stationary signals must be segmented into piece-wise stationary epochs or spikes need to be found among a background of noise before being further analyzed. Permutation entropy (PE) has been proposed to evaluate the irregularity of a time series.

Evaluation of resting-state magnetoencephalogram complexity in Alzheimer's disease with multivariate multiscale permutation and sample entropies.

Alzheimer's disease (AD) is one of the fastest growing neurological diseases in the world. We evaluate multivariate multiscale sample entropy (mvMSE) and multivariate multiscale permutation entropy (mvMPE) approaches to distinguish resting-state magnetoencephalogram (MEG) signals of 36 AD patients from those of 26 normal controls. We also discuss about choosing the appropriate embedding dimension value as an effective parameter for mvMPE and MPE for the first time.

Cluster-span threshold: An unbiased threshold for binarising weighted complete networks in functional connectivity analysis.

We propose a new unbiased threshold for network analysis named the Cluster-Span Threshold (CST). This is based on the clustering coefficient, C, following logic that a balance of `clustering' to `spanning' triples results in a useful topology for network analysis and that the product of complementing properties has a unique value only when perfectly balanced. We threshold networks by fixing C at this balanced value, rather than fixing connection density at an arbitrary value, as has been the trend.