multiscale entropy

INTRODUCTION: To date, no robust electroencephalography (EEG) markers of antidepressant treatment response have been identified. Variable findings may arise from the use of group analyses, which neglect individual variation. Using a combination of group and single-participant analyses, we explored individual variability in EEG characteristics of treatment response. METHODS: Resting-state EEG data and Montgomery-Åsberg Depression Rating Scale (MADRS) symptom scores were collected from 43 patients with depression before, at 1 and 12 weeks of pharmacotherapy...

Analyzing EEG complexity may help to elucidate complex brain dynamics in individuals with psychiatric disorders and provide insight into neural connectivity and its relationship with deficits such as emotion-related impulsivity. EEG complexity was calculated through multiscale entropy and compared between a heterogeneous psychiatric patient group and a healthy control group during the emotion conflict resolution task. Twenty-eight healthy adults and ten psychiatric patients were recruited and compared on the multiscale entropy of EEG acquired in the task...

BACKGROUND: Balance deficits are a major concern for people with multiple sclerosis (pwMS). Measuring complexity of motor behaviour can offer an insight into MS-related changes in adaptability of the balance control system when dealing with increasingly complex tasks. QUESTION: Does postural behaviour complexity differ between pwMS at early stages of the disease and healthy controls (HC)? Does postural behaviour complexity change across increasingly complex tasks? METHODS: Forty-eight pwMS and 24 HC performed four increasingly complex postural tasks with eyes open (EO), eyes closed (EC), on firm (FS) and compliant surface (CS)...

This study explores the efficacy of various EEG complexity measures in detecting mind wandering during video-based learning. Employing a modified probe-caught method, we recorded EEG data from participants engaged in viewing educational videos and subsequently focused on the discrimination between mind wandering (MW) and non-MW states. We systematically investigated various EEG complexity metrics, including metrics that reflect a system's regularity like multiscale permutation entropy (MPE), and metrics that reflect a system's dimensionality like detrended fluctuation analysis (DFA)...

Cognitive Behavioral Therapy (CBT) is believed to be an effective treatment for chronic pain due to its association with cognitive and emotional factors. Nevertheless, there is a paucity of magnetoencephalography (MEG) investigations elucidating its underlying mechanisms. This study investigated the neurophysiological effects of CBT employing MEG and analytical techniques. We administered resting-state MEG scans to 30 patients with chronic pain and 31 age-matched healthy controls. Patients engaged in a 12-session group CBT program...

Parents play a significant role in directing infant's attention to environmental stimuli via joint attention. We hypothesized that infants whose parents provide more bids for joint attention will display a more complex neural response when viewing social scenes. Sixty-one 8-month-old infants underwent electroencephalography (EEG) while viewing videos of joint-and parallel-play and participated in a parent-infant free play interaction. EEG data was analyzed using multiscale entropy, which quantifies moment-to-moment neural variability...

Autonomic nervous dysfunction is a known cardiac sequalae in patients with end-stage liver disease and is associated with a poor prognosis. Heart rate analysis using nonlinear models such as multiscale entropy (MSE) or complexity may identify marked changes in these patients where conventional heart rate variability (HRV) measurements do not. To investigate the application of heart rate complexity (HRC) based on MSE in liver transplantation settings. Thirty adult recipients of elective living donor liver transplantation were enrolled...

Optical coherence tomography (OCT) has been widely used for the diagnosis of dental demineralization. Most methods rely on extracting optical features from OCT echoes for evaluation or diagnosis. However, due to the diversity of biological samples and the complexity of tissues, the separability and robustness of extracted optical features are inadequate, resulting in a low diagnostic efficiency. Given the widespread utilization of entropy analysis in examining signals from biological tissues, we introduce a dental demineralization diagnosis method using OCT echoes, employing multiscale entropy analysis...

Plant electrophysiology has unveiled the involvement of electrical signals in the physiology and behavior of plants. Spontaneously generated bioelectric activity can be altered in response to changes in environmental conditions, suggesting that a plant's electrome may possess a distinct signature associated with various stimuli. Analyzing electrical signals, particularly the electrome, in conjunction with Machine Learning (ML) techniques has emerged as a promising approach to classify characteristic electrical signals corresponding to each stimulus...

This study employs Multiscale Entropy (MSE) to analyze 5020 binocular eye movement recordings from 407 college-aged participants, as part of the GazeBaseVR dataset, across various virtual reality (VR) tasks to understand the complexity of user interactions. By evaluating the vertical and horizontal components of eye movements across tasks such as vergence, smooth pursuit, video viewing, reading, and random saccade, collected at 250 Hz using an ET-enabled VR headset, this research provides insights into the predictability and complexity of gaze patterns...

The development of high-performance lead-free K0.5 Na0.5 NbO3 -based piezoceramics for replacing commercial lead-containing counterparts is crucial for achieving environmentally sustainable society. Although the proposed new phase boundaries (NPB) can effectively improve the piezoelectricity of KNN-based ceramics, the difficulty of achieving saturated poling and the underlying multiscale structures resolution of their complex microstructures are urgent issues. Here, we employ a medium entropy strategy to design NPB and utilize texture engineering to induce crystal orientation...

SIGNIFICANCE: Prefrontal cortex (PFC) hemodynamics are regulated by numerous underlying neurophysiological components over multiple temporal scales. The pattern of output signals, such as functional near-infrared spectroscopy fluctuations (i.e., fNIRS), is thus complex. We demonstrate first-of-its-kind evidence that this fNIRS complexity is a marker that captures the influence of endurance capacity and the effects of hydrogen gas (<mml:math xmlns:mml="https://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>H</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math>) on PFC regulation...

The MSCG/FM (multiscale coarse-graining via force-matching) approach is an efficient supervised machine learning method to develop microscopically informed coarse-grained (CG) models. We present a theory based on the principle of maximum entropy (PME) enveloping the existing MSCG/FM approaches. This theory views the MSCG/FM method as a special case of matching the thermodynamic forces from the extended ensemble described by the set of thermodynamic (relevant) system coordinates. This set may include CG coordinates, the stress tensor, applied external fields, and so forth, and may be characterized by nonequilibrium conditions...

Autism spectrum disorder is an increasingly prevalent and debilitating neurodevelopmental condition and an electroencephalogram (EEG) diagnostic challenge. Despite large amounts of electrophysiological research over many decades, an EEG biomarker for autism spectrum disorder (ASD) has not been found. We hypothesized that reductions in complex dynamical system behaviour in the human central nervous system as part of the macroscale neuronal function during cognitive processes might be detectable in whole EEG for higher-risk ASD adults...

Extracting repeated patterns from our surroundings plays a crucial role in contextualizing information, making predictions and guiding our behavior implicitly. Previous research showed that contextual cueing enhances visual search performance in younger adults. In this study, we investigated whether contextual cueing could also improve older adults' performance and whether age-related differences in the neural processes underlying implicit contextual learning could be detected. Twenty-four younger and 25 older participants performed a visual search task with contextual cueing...

Research has shown multiscale entropy, brain signal behavior across time scales, to reliably increase at lower time scales with time-on-task fatigue. However, multiscale entropy has not been examined in short vigilance tasks (i.e., ≤ 10 min). Addressing this gap, we examine multiscale entropy during a 10-minute Psychomotor Vigilance Test (PVT). Thirty-four participants provided neural data while completing the PVT. We compared the first 2 min of the task to the 7th and 8th minutes to avoid end-spurt effects...

To effectively detect low-altitude small targets under complex sea surface environment, an innovative method has been developed. This method harnesses the chaotic characteristics of sea clutter and employs a combination of Adaptive Noise Complete Ensemble Empirical Modal Decomposition (CEEMDAN), Adaptive Wavelet Thresholding (AWT), and Polynomial Fitting Filtering (SG) for denoising sea clutter data. Subsequently, the Improved Zebra Optimization Algorithm-Extreme Learning Machine (IZOA-ELM) detector is utilized to identify low-altitude small targets amidst the sea clutter background...

A novel type of a multiscale approach, called Relative Resolution (RelRes), can correctly retrieve the behavior of various nonpolar liquids while speeding up molecular simulations by almost an order of magnitude. In this approach in a single system, molecules switch their resolution in terms of their relative separation, with near neighbors interacting via fine-grained potentials, yet far neighbors interacting via coarse-grained potentials; notably, these two potentials are analytically parametrized by a multipole approximation...

As a promising alternative to conventional antibiotic drugs in the biomedical field, functional peptide has been widely used in disease treatment owing to its low toxicity, high absorption rate, and biological activity. Recently, several machine learning methods have been developed for functional peptide prediction. However, the main research heavily relies on statistical features and few consider multifunctional peptide identification. So, we propose SME-MFP, a novel predictor in the imbalanced multi-label functional peptide datasets...

Continuous adaptations of the movement system to changing environments or task demands rely on superposed fractal processes exhibiting power laws, that is, multifractality. The estimators of the multifractal spectrum potentially reflect the adaptive use of perception, cognition, and action. To observe time-specific behavior in multifractal dynamics, a multiscale multifractal analysis based on DFA (MFMS-DFA) has been recently proposed and applied to cardiovascular dynamics. Here we aimed at evaluating whether MFMS-DFA allows identifying multiscale structures in the dynamics of human movements...