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Objective Unplanned readmissions following a hospitalization remain common despite significant efforts to curtail these. Wearable devices may offer help identify patients at high risk for an unplanned readmission. Materials and Methods We conducted a multi-center retrospective cohort study using data from the All of Us data repository. We included subjects with wearable data and developed a baseline Feedforward Neural Network (FNN) model and a Long Short-Term Memory (LSTM) time-series deep learning model to predict daily, unplanned rehospitalizations up to 90 days from discharge. In addition to demographic and laboratory data from subjects, post-discharge data input features include wearable data and multiscale entropy features based on intraday wearable time series. The most significant features in the LSTM model were determined by permutation feature importance testing. Results In sum, 612 patients met inclusion criteria. The complete LSTM model had a higher area under the receiver operating characteristic curve than the FNN model (0.83 vs 0.795). The 5 most important input features included variables from multiscale entropy (steps) and number of active steps per day. Discussion Data available from wearable devices can improve ability to predict readmissions. Prior work has focused on predictors available up to discharge or on additional data abstracted from wearable devices. Our results from 35 institutions highlight how multiscale entropy can improve readmission prediction and may impact future work in this domain. Conclusion Wearable data and multiscale entropy can improve prediction of a deep-learning model to predict unplanned 90-day readmissions. Prospective studies are needed to validate these findings. [ABSTRACT FROM AUTHOR]

Gamma oscillation regularity (GOR) indicates the synchronization of inhibitory interneurons, while the reactivity of cortico-cortical evoked potentials (CCEPs) is supposed to reflect local cortical excitability. Under the assumption that the early response of CCEP near the stimulation site also indicates excitatory activity primarily mediated by pyramidal cells, we aimed to visualize the cortical inhibitory and excitatory activities using GOR and CCEP in combination and to use them to predict the epileptogenic zone (EZ) in mesial temporal lobe epilepsy (MTLE). In five patients who underwent intracranial electrode implantation, GOR and CCEP reactivity in the vicinity of the stimulation site was quantified. The interictal GOR was calculated using multiscale entropy (MSE), the decrease of which was related to the enhanced GOR. These parameters were compared on an electrode-and-electrode basis, and spatially visualized on the brain surface. As a result, elevated GOR and CCEP reactivities, indicative of enhanced inhibitory and excitatory activities, were observed in the epileptogenic regions. Elevated CCEP reactivity was found to be localized to a restricted area centered on the seizure onset region, whereas GOR elevation was observed in a broader region surrounding it. Although these parameters independently predicted the EZ with high specificity, we combined the two to introduce a novel parameter, the excitatory and inhibitory (EI) index. The EI index predicted EZ with increased specificity compared with GOR or CCEP reactivity alone. Our results demonstrate that GOR and CCEP reactivity provided a quantitative visualization of the distribution of cortical inhibitory and excitatory activities and highlighted the relationship between the two parameters. The combination of GOR and CCEP reactivities are expected to serve as biomarkers for localizing the epileptogenic zone in MTLE from interictal intracranial electroencephalograms. [ABSTRACT FROM AUTHOR]

Compared with the traditional AC grid, the flexible DC grid has the advantages of low wire loss and large transmission capacity, but it is difficult to extract fault signals and diagnose various faults. Therefore, a fault detection method based on complete ensemble empirical mode decomposition with adaptive noise analysis (CEEMDAN) multiscale entropy (MSE) and genetic algorithm optimization support vector machine (GA-SVM) is proposed. Firstly, CEEMDAN is used to decompose the extracted fault line mode voltage signal into several intrinsic mode function (IMF). The IMF containing more fault information is selected to reconstruct the denoising signal. The MSE of the reconstructed signal is calculated and input into the GA-SVM classifier as the fault feature, and the fault line mode voltage signals of different fault types under different operating conditions are classified and recognized. A large number of simulation results prove that the proposed method has strong anti-interference ability and high reliability, and has high classification accuracy in the case of small sample data. Compared with Linear-SVM, PSO-SVM, KNN and Fine Tree intelligent algorithms, the proposed method shows a significantly improved accuracy, 93.8888% on average. [ABSTRACT FROM AUTHOR]

Background The hemodynamics of prefrontal cortex (PFC) oxygenation are regulated by numerous processes operating over multiple temporal scales, producing complex patterns in its output fluctuations. Age may alter this multiscale regulation of PFC oxygenation, leading to diminished physiologic complexity of this important regulatory process. We aimed to characterize the effects of age on such complexity and its relationship to performance of an executive n -back task. Methods Twenty-four younger (aged 28 ± 3 years) and 27 older (aged 78 ± 6 years) adults completed this study. Continuous oxygenation (HbO2) and deoxygenation (HHb) signals of PFC were recorded using functional near-infrared spectroscopy (fNIRS) while participants stood and watched a blank screen (blank), clicked a mouse when an X appeared (IdX), or when a letter was repeated from "2-back" in a sequence shown on a screen (2-back). We used multiscale entropy to quantify the HbO2 and HHb complexity of fNIRS signals. Results Older adults exhibited lower HbO2 and HHb complexity compared to younger adults, regardless of task (p  = .0005–.002). Both groups exhibited greater complexity during the IdX and 2-back than blank task (p  = .02–.04). Across all participants, those with greater HbO2 and/or HHb complexity during the blank task exhibited faster IdX and 2-back reaction time (β = −0.56 to −0.6, p  = .009–.02). Those demonstrating greater increase in HbO2 and/or HHb complexity from IdX to 2-back task had lower percent increase in reaction time from IdX to 2-back task (β = −0.41 to −0.37, p  = .005–.01). Conclusions The complexity of fNIRS-measured PFC oxygenation fluctuations may capture the influence of aging on the regulation of prefrontal hemodynamics involved in executive-function-based task performance. [ABSTRACT FROM AUTHOR]

Entropy measures are increasingly being used to analyze the structure of neural activity observed by functional magnetic resonance imaging (fMRI), with resting‐state networks (RSNs) being of interest for their reproducible descriptions of the brain's functional architecture. Temporal correlations have shown a dichotomy among these networks: those that engage with the environment, known as extrinsic, which include the visual and sensorimotor networks; and those associated with executive control and self‐referencing, known as intrinsic, which include the default mode network and the frontoparietal control network. While these inter‐voxel temporal correlations enable the assessment of synchrony among the components of individual networks, entropic measures introduce an intra‐voxel assessment that quantifies signal features encoded within each blood oxygen level‐dependent (BOLD) time series. As a result, this framework offers insights into comprehending the representation and processing of information within fMRI signals. Multiscale entropy (MSE) has been proposed as a useful measure for characterizing the entropy of neural activity across different temporal scales. This measure of temporal entropy in BOLD data is dependent on the length of the time series; thus, high‐quality data with fine‐grained temporal resolution and a sufficient number of time frames is needed to improve entropy precision. We apply MSE to the Midnight Scan Club, a highly sampled and well‐characterized publicly available dataset, to analyze the entropy distribution of RSNs and evaluate its ability to distinguish between different functional networks. Entropy profiles are compared across temporal scales and RSNs. Our results have shown that the spatial distribution of entropy at infra‐slow frequencies (0.005–0.1 Hz) reproduces known parcellations of RSNs. We found a complexity hierarchy between intrinsic and extrinsic RSNs, with intrinsic networks robustly exhibiting higher entropy than extrinsic networks. Finally, we found new evidence that the topography of entropy in the posterior cerebellum exhibits high levels of entropy comparable to that of intrinsic RSNs. [ABSTRACT FROM AUTHOR]

The main objective of the present study was to investigate combustion instability in a multi-cylinder radial piston engine with electronic fuel injection to ensure safety and reliability, maintain fuel efficiency, reduce harmful emissions, which has a positive impact on the environment. Multi-fractal analysis showed that IMEP (mean effective pressure) fluctuations are an anti-persistent process, meaning that there is no correlation between successive events. The return plots showed significant cyclic variability and instability in neighboring cylinders. The wavelet method and multiscale entropy analysis confirmed the results of the multifractal analysis, showing changes in the characteristics of the combustion process after the introduction of perturbations. The research-based on the first return map has shown an impact on the non-uniform and complex nature of the operation of the neighboring cylinders. The statistical analysis has shown that more than 60 % is slightly negatively skewness in most of the cases, and about 67 % of the cases indicate a leptokurtic distribution of IMEP. Finally, it is concluded that, the combustion of failure of one cylinder in a nine-cylinder engine affects the operation of the other cylinders, as demonstrated through advanced multifractal analysis techniques, return plots, return maps and multiscale entropy analysis.

Background: Signal complexity (i.e. entropy) describes the level of order within a system. Low physiological signal complexity predicts unfavorable outcome in a variety of diseases and is assumed to reflect increased rigidity of the cardio/cerebrovascular system leading to (or reflecting) autoregulation failure. Aneurysmal subarachnoid hemorrhage (aSAH) is followed by a cascade of complex systemic and cerebral sequelae. In aSAH, the value of entropy has not been established yet. Methods: aSAH patients from 2 prospective cohorts (Zurich—derivation cohort, Aachen—validation cohort) were included. Multiscale Entropy (MSE) was estimated for arterial blood pressure, intracranial pressure, heart rate, and their derivatives, and compared to dichotomized (1–4 vs. 5–8) or ordinal outcome (GOSE—extended Glasgow Outcome Scale) at 12 months using uni- and multivariable (adjusted for age, World Federation of Neurological Surgeons grade, modified Fisher (mFisher) grade, delayed cerebral infarction), and ordinal methods (proportional odds logistic regression/sliding dichotomy). The multivariable logistic regression models were validated internally using bootstrapping and externally by assessing the calibration and discrimination. Results: A total of 330 (derivation: 241, validation: 89) aSAH patients were analyzed. Decreasing MSE was associated with a higher likelihood of unfavorable outcome independent of covariates and analysis method. The multivariable adjusted logistic regression models were well calibrated and only showed a slight decrease in discrimination when assessed in the validation cohort. The ordinal analysis revealed its effect to be linear. MSE remained valid when adjusting the outcome definition against the initial severity. Conclusions: MSE metrics and thereby complexity of physiological signals are independent, internally and externally valid predictors of 12-month outcome. Incorporating high-frequency physiological data as part of clinical outcome prediction may enable precise, individualized outcome prediction. The results of this study warrant further investigation into the cause of the resulting complexity as well as its association to important and potentially preventable complications including vasospasm and delayed cerebral ischemia. [ABSTRACT FROM AUTHOR]

Introduction: Stroke is one of the most debilitating diseases among adults worldwide and leads to persistent rehabilitation needs even at the chronic stage. Achieving good postural control is a critical requirement for daily activities which enhances quality of life (QoL) in patients with stroke. There is increasing evidence that transcranial direct current stimulation (tDCS) can be considered a promising adjunct technique to improve motor recovery after stroke. Evidence of augmented neuroplasticity after tDCS suggests that paired rehabilitation followed by consecutive use of tDCS may optimize recovery outcomes. Although a few randomized controlled trials have been conducted on upper limb rehabilitation in chronic stroke using tDCS, no study focused on balance training in chronic stroke patients. The present randomized, sham-controlled, double-blinded clinical study addresses brain stimulation targeting postural control using tDCS in chronic stroke. Methods: The study participants included chronic ischemic stroke individuals with postural control impairments who passed the exclusion criteria. Active or sham anodal tDCS was delivered to the lesioned leg motor cortex combined with balance training. The experimental group received active anodal tDCS stimulation (2 mA) for 20 min, daily for 5 days paired with balance training. Linear and nonlinear approaches were used to analyze postural sway changes pre-and post-intervention. Postural sway fluctuation, functional balance assessment using the Berg balance scale, and timed up-and-go test were conducted to compare the active and sham groups. Conclusion: This trial could have significant implications for balance rehabilitation after stroke in the ambulatory setting. If effective, this novel approach may improve rehabilitation protocol in this population. [ABSTRACT FROM AUTHOR]

Multiscale entropy-based methods have made great progress in the field of health condition monitoring and fault diagnosis of machines due to their powerful feature representation capabilities. However, existing multiscale entropy methods suffer from three major obstacles: high fluctuation under large scale-factor, loss of high-frequency information, and poor robustness to noises. Thus, this work proposes a symbol-scale analysis method to deal with the above problems. In one aspect, to capture fault features from the time series over multiple time scales, time-delay process of different intervals is utilized to obtain long-term features and short-term features. In the other aspect, symbol-scale analysis introduces a symbolization procedure and maps time series into a corresponding sequence of symbols to overcome the limitation of weak fault extraction under a low-signal-to-noise ratio environment. Moreover, the symbol-scale entropy approach is developed by integrating with diversity entropy, called symbol-scale diversity entropy. The effectiveness of the proposed strategy is intensively validated using two simulated signals and experimental cases. Results demonstrate its advantages in dynamic change tracking ability and calculation efficiency by comparing it with other state-of-the-art entropy methods. Apart from diversity entropy, the versatility of incorporating the proposed symbol-scale analysis and other entropy methods is also verified using experimental data. [ABSTRACT FROM AUTHOR]

Introduction: Tai Chi has proved to be an effective therapy for balance performance and cognition. However, non-consistency exists in the results of the effect of Tai Chi training on standing balance control in older adults. This study aimed to use traditional and non-traditional methods to investigate the effect of Tai Chi on standing balance in older adults. Methods: Thirty-six Tai Chi practitioners (TC group) and thirty-six older adults with no Tai Chi practice (control group) were recruited in this study. A Nintendo Wii Balance Board was used to record the center of pressure (COP) during standing balance over 20 s in the condition of eyes closed with three repetitions. The wavelet analysis, multiscale entropy, recurrence quantification analysis, and traditional methods were used to evaluate the standing balance control in the anterior-posterior (AP) and mediolateral (ML) directions. Results: (1) Greater sway mean velocity in the AP direction and sway Path length were found in the TC group compared with the control group; (2) lower Very-low frequency band (0.10-0.39Hz) and higher Moderate frequency band (1.56-6.25Hz) in the AP andML directions were found in the TC group compared with the control group; (3) greater complexity index (CI) and lower determinism (DET) in the AP and ML directions were observed in the TC group compared with control group; (4) greater path length linked with smaller Very-low frequency band in the AP and ML directions and higher Moderate frequency band in the AP direction in both groups; (5) greater path length linked with lower DET and higher CI in the AP direction only in the TC group. Conclusion: Long-term Tai Chi practice improved sensory reweighting (more reliance on the proprioception systemand less reliance on the vestibular system) and complexity of standing balance control in older adults. In addition, greater sway velocity may be as an exploratory role in standing balance control of TC older adults, which correlated with greater complexity, but no such significant relationship in the control group. Therefore, the effects of Tai Chi practice on standing balance control in older adults may be attributed to the improvement of sensory reweighting and complexity rather than reduced sway velocity or amplitude. [ABSTRACT FROM AUTHOR]

Physiologic hand tremors are a critical factor affecting the aim of air pistol shooters. However, the extent of the effect of hand tremors on shooting performance is unclear. In this study, we aim to explore the relationship between hand tremors and shooting performance scores as well as investigate potential links between muscle activation and hand tremors. In this study, 17 male air pistol shooters from China's national team and the Air Pistol Sports Center were divided into two groups: the elite group and the sub-elite group. Each participant completed 40 shots during the experiment, with shooters' hand tremors recorded using three-axis digital accelerometers affixed to their right hands. Muscle activation was recorded using surface electromyography on the right anterior deltoid, posterior deltoid, biceps brachii (short head), triceps brachii (long head), flexor carpi radialis, and extensor carpi radialis. Our analysis revealed weak correlations between shooting scores and hand tremor amplitude in multiple directions (middle-lateral, ML: r2 = −0.22, p < 0.001; vertical, VT: r2 = −0.25, p < 0.001), as well as between shooting scores and hand tremor complexity (ML: r2 = −0.26, p < 0.001; VT: r2 = −0.28, p < 0.001), across all participants. Notably, weak correlations between shooting scores and hand tremor amplitude (ML: r2 = −0.27, p < 0.001; VT: r2 = −0.33, p < 0.001) and complexity (ML: r2 = −0.31, p < 0.001) were observed in the elite group but not in the sub-elite group. Moderate correlation were found between the biceps brachii (short head) RMS and hand tremor amplitude in the VT and ML directions (ML: r2 = 0.49, p = 0.010; VT: r2 = 0.44, p = 0.025) in all shooters, with a moderate correlation in the ML direction in elite shooters (ML: r2 = 0.49, p = 0.034). Our results suggest that hand tremors in air pistol shooters are associated with the skill of the shooters, and muscle activation of the biceps brachii (long head) might be a factor affecting hand tremors. By balancing the agonist and antagonist muscles of the shoulder joint, shooters might potentially reduce hand tremors and improve their shooting scores. [ABSTRACT FROM AUTHOR]

Two-phase flow in channels of small dimensions is often a non-stationary process, the nature of such flow is oscillatory. Due to small channel dimensions, high heat flux, parallel channels interactions, pressure and temperature oscillations, the character of the phenomena occurring during boiling is complex. The changes of the measured signals are observed in different time scales. In order to examine in detail two-phase flow parameters changes, many acquisition devices are often installed. This solution becomes challenging concerning mini and microchannel heat-exchangers due to space limitation and modifications of an experimental setup. This paper presents a novel application of multiscale entropies for spatial and temporal analysis of two-phase flow based on only one registered parameter. This analysis is performed based on pixel brightness changes in photo frames registered by a high speed camera during two-phase flow. The spatial changes of pixel brightness are observed on single frames and temporal changes are examined using a set of frames (in time). The Composite Multiscale Sample Entropy is applied to identify two-phase flow patterns and to analyze the complexity of phase distribution. Using Multivariate Multiscale Sample Entropy the most rapid changes of phase distribution in a multichannel heat exchanger are determined. [ABSTRACT FROM AUTHOR]

Aims: Parkinsonian tremor (PT) is regulated by numerous neurophysiological components across multiple temporospatial scales. The dynamics of tremor fluctuation are thus highly complex. This study aimed to explore the effects of different medications on tremor complexity, and how the underlying factors contribute to such tremor complexity. Methods: In this study, 66 participants received a 2‐mg dose of benzhexol or a pre‐determined dose of levodopa at two study visits in a randomized order. Before and after taking the medications, tremor fluctuation was recorded using surface electromyography electrodes and accelerometers in resting, posture, and weighting conditions with and without a concurrent cognitive task. Tremor complexity was quantified using multiscale entropy. Results: Tremor complexity in resting (p = 0.002) and postural condition (p < 0.0001) was lower when participants were performing a cognitive task compared to a task‐free condition. After taking levodopa and benzhexol, participants had increased (p = 0.02–0.03) and decreased (p = 0.03) tremor complexity compared to pre‐medication state, respectively. Tremor complexity and its changes as induced by medications were significantly correlated with clinical ratings and their changes (β = −0.23 to −0.39; p = 0.002–0.04), respectively. Conclusion: Tremor complexity may be a promising marker to capture the pathophysiology underlying the development of PT, aiding the characterization of the effects medications have on PT regulation. [ABSTRACT FROM AUTHOR]

Multimodal physiological signals play a pivotal role in drivers’ perception of work stress. However, the scarcity of labels and the multitude of modalities render the utilization of physiological signals for driving cognitive alertness detection challenging. We thus propose a multimodal physiological signal detection model based on self-supervised learning. First, in order to mine the intrinsic information of data and enable data to highlight effective information, we introduce a multiscale entropy (MSE) evoked attention mechanism. Secondly, the multimodal patches undergo processing through a novel cascaded attention mechanism. This attention mechanism is rooted in patch-level interactions within each modality, progressively integrating and interacting with other modalities in a cascading manner, thereby mitigating computational complexity. Moreover, a multimodal uncertainty-aware module is devised to effectively cope with intricate variations in the data. This module enhances its generalization ability through the incorporation of uncertain resampling. Experiments were conducted on the DriveDB dataset and the CogPilot dataset with both the linear probing and the fine-tuning evaluation protocols. Experimental results in subject-dependent setting show that our model significantly outperforms previous competitive baselines. In the linear probing evaluation, our model achieves on average 6.26%, 6.64%, and 7.75% improvements in Accuracy (Acc), Recall (Rec), and F1 Score. It also outperforms other models by 7.96% in Acc, 9.13% in Rec, and 9.2% in F1 using the fine-tuning evaluation. Furthermore, our model also demonstrates robust performance in subject-independent setting.

In order to precisely diagnose the fault type of rotating machinery, a fault diagnosis method for rotating machinery based on improved multiscale attention entropy and random forests is proposed in this study. Firstly, a nonlinear dynamics technique without hyperparameters namely multiscale attention entropy is proposed for measuring signal complexity by extending attention entropy to multiple time scales. Secondly, aiming at the insufficient coarse-graining of multiscale attention entropy, composite multiscale attention entropy is exploited to extract the features of rotating machinery faults. Then, t-distributed stochastic neighbor embedding is used to overcome the feature redundancy problem by reducing the dimension of the extracted features. Finally, the reduced-dimensional features are inputted into the random forests model to complete fault pattern recognition of rotating machinery. The results of the experiment indicate that the proposed method achieves the optimal diagnostic performance on two different fault datasets respectively, showing an extremely competitive advantage in comparison with conventional diagnosis models. Meanwhile, the proposed method is adopted to the actual hydropower unit without misjudgment, which verifies its strong adaptability. The research proposes a novel method for detecting faults in rotating machinery such as hydropower units. [ABSTRACT FROM AUTHOR]

The fluctuations in resting‐state beat‐to‐beat blood pressure (BP) are physiologically complex, and the degree of such BP complexity is believed to reflect the multiscale regulation of this critical physiologic process. Hypertension (HTN), one common age‐related condition, is associated with altered BP regulation and diminished system responsiveness to perturbations such as orthostatic change. We thus aimed to characterize the impact of HTN on resting‐state BP complexity, as well as the relationship between BP complexity and both adaptive capacity and underlying vascular characteristics. We recruited 392 participants (age: 60–91 years), including 144 that were normotensive and 248 with HTN (140 controlled‐ and 108 uncontrolled‐HTN). Participants completed a 10‐min continuous finger BP recording during supine rest, then underwent measures of lying‐to‐standing BP change, arterial stiffness (i.e., brachial‐ankle pulse wave velocity), and endothelial function (i.e., flow‐mediated vasodilation). The complexity of supine beat‐to‐beat systolic (SBP) and diastolic (DBP) BP was quantified using multiscale entropy. Thirty participants with HTN (16 controlled‐HTN and 14 uncontrolled‐HTN) exhibited orthostatic hypotension. SBP and DBP complexity was greatest in normotensive participants, lower in those with controlled‐HTN, and lowest in those in uncontrolled‐HTN (p < 0.0005). Lower SBP and DBP complexity correlated with greater lying‐to‐standing decrease in SBP and DBP level (β = −0.33 to −0.19, p < 0.01), greater arterial stiffness (β = −0.35 to −0.18, p < 0.01), and worse endothelial function (β = 0.17–0.22, p < 0.01), both across all participants and within the control‐ and uncontrolled‐HTN groups. These results suggest that in older adults, BP complexity may capture the integrity of multiple interacting physiologic mechanisms that regulate BP and are important to cardiovascular health. [ABSTRACT FROM AUTHOR]

The detection of instability inception is favorable to avoid compressor instability. In this paper, a multiscale entropy-based feature extraction is developed for the detection of the instability inception in axial compressors. Nonlinear and statistical features of the short-time instability inception are extracted by generally combining multiscale entropy and statistical features. First, nonlinear features are extracted by refined composite multiscale entropy to avoid the inaccurate estimation or undefined entropy of multiscale entropy for short time series. Second, the time-domain-based statistical features are chosen to capture more information on instability inception, and the dominant statistical features are determined by random forests implemented with the mean decrease accuracy algorithm at each time scale. The obtained refined composite dominant statistical features are regarded as weighting factors and integrated with the refined composite multiscale entropy to generate a combined feature. Finally, numerical simulation results on two synthetic noise datasets and a compressor instability model dataset are presented to demonstrate the effectiveness, efficiency, and robustness of the combined features under different conditions. [ABSTRACT FROM AUTHOR]

This research paper explores the effects of endurance exercise and the intake of hydrogen gas on the complexity of hemodynamics in the prefrontal cortex (PFC). The study involved 24 healthy young men who completed endurance cycling exercise at different physical loads after either taking hydrogen gas or a placebo. The complexity of the PFC hemodynamics was measured using functional near-infrared spectroscopy (fNIRS) and quantified using multiscale entropy. The results showed that the fNIRS complexity was significantly greater during exercise compared to baseline, and the intake of hydrogen gas further increased the complexity. The study suggests that fNIRS complexity could be a marker for the adaptive capacity of the PFC to endurance exercise and interventions. [Extracted from the article]

[This corrects the article DOI: 10.3389/fpsyt.2021.716707.].

Traditional techniques to evaluate the complexity of biological signals forgo the numerous time scales present in such data. When these algorithms were applied to real-world datasets gathered in health and illness states, they produced inconsistent results. Sample entropy (SampEn) has been utilized extensively to evaluate the complexity of RR-interval time series. Using multi-scale Entropy analysis incorporating Sample Entropy, several research on the complexity of physiological signals have been conducted. The primary disadvantage of MSE is that coarse-graining results in a noticeable and considerable loss of information. For small scales, the method proves to be ineffective. So, for the MSE analysis of physiological data, we have employed the FDM rather than coarse-graining. This FDM, which is based on the Fourier theory, is excellent for studying nonlinear and nonstationary time series. This approach generates a TFE distribution that displays the data’s fundamental premise. This will provide a number of frequency bands with varying cut-off frequencies but consistent data across all of them. To further validate our suggested technique, we used “ANOVA analysis” to compare our results to the entropy analysis of a synthetic simulated database containing white noise (WN) and power noise (PN) signals.

This study analyses the spontaneous electroencephalogram (EEG) brain activity of 14 children diagnosed with Autism Spectrum Disorder (ASD) compared to 18 children with normal development, aged 5–11 years. (i) Power Spectral Density (PSD), (ii) variability across trials (coefficient of variation: CV), and (iii) complexity (multiscale entropy: MSE) of the brain signal analysis were computed on the resting state EEG. PSD (0.5–45 Hz) and CV were averaged over different frequency bands (low-delta, delta, theta, alpha, low-beta, high-beta and gamma). MSE were calculated with a coarse-grained procedure on 67 time scales and divided into fine, medium and coarse scales. In addition, significant neurophysiological variables were correlated with behavioral performance data (Kaufman Brief Intelligence Test (KBIT) and Autism Spectrum Quotient (AQ)). Results show increased PSD fast frequency bands (high-beta and gamma), higher variability (CV) and lower complexity (MSE) in children with ASD when compared to typically developed children. These results suggest a more variable, less complex and, probably, less adaptive neural networks with less capacity to generate optimal responses in ASD children. [ABSTRACT FROM AUTHOR]

Introduction: Fragile X syndrome (FXS) is a genetic disorder caused by a mutation of the fragile X mental retardation 1 gene (FMR1). FXS is associated with neurophysiological abnormalities, including cortical hyperexcitability. Alterations in electroencephalogram (EEG) resting-state power spectral density (PSD) are well-defined in FXS and were found to be linked to neurodevelopmental delays. Whether non-linear dynamics of the brain signal are also altered remains to be studied. Methods: In this study, resting-state EEG power, including alpha peak frequency (APF) and theta/beta ratio (TBR), as well as signal complexity using multi-scale entropy (MSE) were compared between 26 FXS participants (ages 5-28 years), and 7 neurotypical (NT) controls with a similar age distribution. Subsequently a replication study was carried out, comparing our cohort to 19 FXS participants independently recorded at a different site. Results: PSD results confirmed the increased gamma, decreased alpha power and APF in FXS participants compared to NT controls. No alterations in TBR were found. Importantly, results revealed reduced signal complexity in FXS participants, specifically in higher scales, suggesting that altered signal complexity is sensitive to brain alterations in this population. The replication study mostly confirmed these results and suggested critical points of stagnation in the neurodevelopmental curve of FXS. Conclusion: Signal complexity is a powerful feature that can be added to the electrophysiological biomarkers of brain maturation in FXS.

Physiologic hand tremors are a critical factor affecting the aim of air pistol shooters. However, the extent of the effect of hand tremors on shooting performance is unclear. In this study, we aim to explore the relationship between hand tremors and shooting performance scores as well as investigate potential links between muscle activation and hand tremors. In this study, 17 male air pistol shooters from China’s national team and the Air Pistol Sports Center were divided into two groups: the elite group and the sub-elite group. Each participant completed 40 shots during the experiment, with shooters’ hand tremors recorded using three-axis digital accelerometers affixed to their right hands. Muscle activation was recorded using surface electromyography on the right anterior deltoid, posterior deltoid, biceps brachii (short head), triceps brachii (long head), flexor carpi radialis, and extensor carpi radialis. Our analysis revealed weak correlations between shooting scores and hand tremor amplitude in multiple directions (middle-lateral, ML: r2 = −0.22, p < 0.001; vertical, VT: r2 = −0.25, p < 0.001), as well as between shooting scores and hand tremor complexity (ML: r2 = −0.26, p < 0.001; VT: r2 = −0.28, p < 0.001), across all participants. Notably, weak correlations between shooting scores and hand tremor amplitude (ML: r2 = −0.27, p < 0.001; VT: r2 = −0.33, p < 0.001) and complexity (ML: r2 = −0.31, p < 0.001) were observed in the elite group but not in the sub-elite group. Moderate correlation were found between the biceps brachii (short head) RMS and hand tremor amplitude in the VT and ML directions (ML: r2 = 0.49, p = 0.010; VT: r2 = 0.44, p = 0.025) in all shooters, with a moderate correlation in the ML direction in elite shooters (ML: r2 = 0.49, p = 0.034). Our results suggest that hand tremors in air pistol shooters are associated with the skill of the shooters, and muscle activation of the biceps brachii (long head) might be a factor affecting hand tremors. By balancing the agonist and antagonist muscles of the shoulder joint, shooters might potentially reduce hand tremors and improve their shooting scores.

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. Participants were recorded up to six times over a 26-month period, offering a longitudinal perspective on eye movement behavior in VR. MSE’s application in this context aims to offer a deeper understanding of user behavior in VR, highlighting potential avenues for interface optimization and user experience enhancement. The results suggest that MSE can be a valuable tool in creating more intuitive and immersive VR environments by adapting to users’ gaze behaviors. This paper discusses the implications of these findings for the future of VR technology development, emphasizing the need for intuitive design and the potential for MSE to contribute to more personalized and comfortable VR experiences.

This study proposes a wavelet leaders method with multiscale entropy measures to analyze multiscale complexities in electrocardiogram (ECG) signals to characterize arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR). The statistical results show evidence of multiscale fractal and multiscale entropy in all health conditions. In addition, ECG signals under NSR conditions display the largest complexity compared to ARR and CHF. Further, statistical tests confirm the presence of differences in terms of multifractals between health conditions in ECG signals. Finally, multiscale entropy increases with scale. The results from statistical analyses indicate that healthy ECG signals are more complex than abnormal ones. Hence, abnormality alters and reduces complexity in arrhythmia and congestive heart failure signals.

This present study aims to investigate neural mechanisms underlying ADHD compared to healthy children through the analysis of the complexity and the variability of the EEG brain signal using multiscale entropy (MSE), EEG signal standard deviation (SDs), as well as the mean, standard deviation (SDp) and coefficient of variation (CV) of absolute spectral power (PSD). For this purpose, a sample of children diagnosed with attention-deficit/hyperactivity disorder (ADHD) between 6 and 17 years old were selected based on the number of trials and diagnostic agreement, 32 for the open-eyes (OE) experimental condition and 25 children for the close-eyes (CE) experimental condition. Healthy control subjects were age- and gender-matched with the ADHD group. The MSE and SDs of resting-state EEG activity were calculated on 34 time scales using a coarse-grained procedure. In addition, the PSD was averaged in delta, theta, alpha, and beta frequency bands, and its mean, SDp, and CV were calculated. The results show that the MSE changes with age during development, increases as the number of scales increases and has a higher amplitude in controls than in ADHD. The absolute PSD results show CV differences between subjects in low and beta frequency bands, with higher variability values in the ADHD group. All these results suggest an increased EEG variability and reduced complexity in ADHD compared to controls. [ABSTRACT FROM AUTHOR]

In this paper, we explore the use of multiscale bubble entropy and power metric for feature extraction procedure and extend it with MLA and stability analysis to design a reliable multichannel seizure detection technique. First, we represent the multichannel EEG signal in 2D matrix form and then apply AM FM model to exploit the decomposed form of EEG. Thereafter, we construct the complexity coefficient using multiscale bubble entropy analysis from decomposed EEG wave. Then, second feature set is formed by using simple and efficient power procedure to obtain absolute power index and relative power index. Using two machine learning approaches, classification performance of proposed approach is explored to correctly identify the epileptic seizures. To show the robustness of multiscale bubble entropy, the stability analysis is performed with normal EEG dataset. Experimental results demonstrate that our proposed technique can effectively detect the epileptic seizures and achieve a superior classification performance with the ANN classifier compared to KNN classifier. This method provides higher discriminating capability with greater stability, so that they could detect wider range of seizure and thus help advance the current diagnosis system. [ABSTRACT FROM AUTHOR]

Postural control is usually assessed by examining the fluctuations of the center of pressure (COP). Balance maintenance is based on sensory feedback and neural interactions, deployed over multiple temporal scales and producing less complex outputs with aging and disease. This paper aims to investigate postural dynamics and complexity on diabetic patients, since diabetic neuropathy (DN) affects the somatosensory system and impairs postural steadiness. A multiscale fuzzy entropy (MSFEn) analysis, over a wide range of temporal scales, was performed on COP timeseries during unperturbed stance in a group of diabetic individuals without neuropathy and two groups of DN patients, with and without symptoms. A parameterization of the MSFEn curve is also proposed. A significant loss of complexity was recognized for the medial-lateral direction in DN groups with respect to non-neuropathic population. For the anterior-posterior direction, symptomatic DN group showed a lowered sway complexity for longer time scales with respect to non neuropathic and asymptomatic patients. The MSFEn approach and the related parameters highlighted that the loss of complexity might be attributed to different factors depending on sway direction, i.e. related to the presence of neuropathy along the medial-lateral axis and to a symptomatic state on the anterior-posterior direction. Results of this study support the use of the MSFEn for gaining insights into balance control mechanisms for diabetic patients, in particular when comparing non neuropathic with neuropathic asymptomatic patients, whose identification by posturographic analysis would be of great value.

The mechanisms underlying the altered postural control and risk of falling in patients with osteoporosis are not yet fully understood. The aim of the present study was to investigate postural sway in women with osteoporosis and a control group. The postural sway of 41 women with osteoporosis (17 fallers and 24 non-fallers) and 19 healthy controls was measured in a static standing task with a force plate. The amount of sway was characterized by traditional (linear) center-of-pressure (COP) parameters. Structural (nonlinear) COP methods include spectral analysis by means of a 12-level wavelet transform and a regularity analysis via multiscale entropy (MSE) with determination of the complexity index. Patients showed increased body sway in the medial–lateral (ML) direction (standard deviation in mm: 2.63 ± 1.00 vs. 2.00 ± 0.58, p = 0.021; range of motion in mm: 15.33 ± 5.58 vs. 10.86 ± 3.14, p = 0.002) and more irregular sway in the anterior–posterior (AP) direction (complexity index: 13.75 ± 2.19 vs. 11.18 ± 4.44, p = 0.027) relative to controls. Fallers showed higher-frequency responses than non-fallers in the AP direction. Thus, postural sway is differently affected by osteoporosis in the ML and AP directions. Clinically, effective assessment and rehabilitation of balance disorders can benefit from an extended analysis of postural control with nonlinear methods, which may also contribute to the improvement of risk profiles or a screening tool for the identification of high-risk fallers, thereby prevent fractures in women with osteoporosis. [ABSTRACT FROM AUTHOR]

Spinal cord injury (SCI) causes a disruption of autonomic nervous regulation to the cardiovascular system, leading to various cardiovascular and microvascular diseases. Exercise training is an effective intervention for reducing risk for microvascular diseases in healthy people. However, the effectiveness of exercise training on improving microvascular function in people with SCI is largely unknown. The purpose of this study was to compare blood flow oscillations in people with spinal cord injury and different physical activity levels to determine if such a lifestyle might influence skin blood flow. A total of 37 participants were recruited for this study, including 12 athletes with SCI (ASCI), 9 participants with SCI and a sedentary lifestyle (SSCI), and 16 healthy able-bodied controls (AB). Sacral skin blood flow (SBF) in response to local heating at 42 °C for 50 min was measured using laser Doppler flowmetry. The degree of the regularity of blood flow oscillations (BFOs) was quantified using a multiscale entropy approach. The results showed that BFO was significantly more irregular in ASCI and AB compared to SSCI during the maximal vasodilation period. Our results also demonstrate that the difference in the regularity of BFOs between original SBF signal and phase-randomized surrogate time series was larger in ASCI and AB compared to SSCI. Our findings indicate that SCI causes a loss of complexity of BFOs and exercise training may improve complexity in people with SCI. This study demonstrates that multiscale entropy is a sensitive method for detecting differences between different categories of people with SCI and might be able to detect effects of exercise training related to skin blood flow. [ABSTRACT FROM AUTHOR]

Atypical facial expression is one of the early symptoms of autism spectrum disorder (ASD) characterized by reduced regularity and lack of coordination of facial movements. Automatic quantification of these behaviors can offer novel biomarkers for screening, diagnosis, and treatment monitoring of ASD. In this work, 40 toddlers with ASD and 396 typically developing toddlers were shown developmentally-appropriate and engaging movies presented on a smart tablet during a well-child pediatric visit. The movies consisted of social and non-social dynamic scenes designed to evoke certain behavioral and affective responses. The front-facing camera of the tablet was used to capture the toddlers’ face. Facial landmarks’ dynamics were then automatically computed using computer vision algorithms. Subsequently, the complexity of the landmarks’ dynamics was estimated for the eyebrows and mouth regions using multiscale entropy. Compared to typically developing toddlers, toddlers with ASD showed higher complexity (i.e., less predictability) in these landmarks’ dynamics. This complexity in facial dynamics contained novel information not captured by traditional facial affect analyses. These results suggest that computer vision analysis of facial landmark movements is a promising approach for detecting and quantifying early behavioral symptoms associated with ASD. [ABSTRACT FROM AUTHOR]

Beginning in 1998, a network of electronic random number generators located around the world has continuously recorded samples of truly random bits. The resulting data were used to explore a hypothesis that predicts the emergence of anomalous structure in randomness correlated with events that attract widespread human attention. A formal experiment testing this hypothesis from 1998 to 2015 found a highly significant deviation from chance expectation. However, the duration of the selected events comprised less than 5% of all data available through 2022, so the present analysis examined the full database to see if emergence of nonrandom structure was limited to those events, or if it was reflective of a persistent, if subtle, relationship between collective mind and matter. Two analytical methods were used to study emergent structure in time-series data: Multiscale entropy and a novel deconvolution technique. Both methods provided evidence consistent with the hypothesis, suggesting that some aspect of collective consciousness appears to be anomalously associated with aspects of the physical world. [ABSTRACT FROM AUTHOR]

The detection of instability inception is favorable to avoid compressor instability. In this paper, a multiscale entropy-based feature extraction is developed for the detection of the instability inception in axial compressors. Nonlinear and statistical features of the short-time instability inception are extracted by generally combining multiscale entropy and statistical features. First, nonlinear features are extracted by refined composite multiscale entropy to avoid the inaccurate estimation or undefined entropy of multiscale entropy for short time series. Second, the time-domain-based statistical features are chosen to capture more information on instability inception, and the dominant statistical features are determined by random forests implemented with the mean decrease accuracy algorithm at each time scale. The obtained refined composite dominant statistical features are regarded as weighting factors and integrated with the refined composite multiscale entropy to generate a combined feature. Finally, numerical simulation results on two synthetic noise datasets and a compressor instability model dataset are presented to demonstrate the effectiveness, efficiency, and robustness of the combined features under different conditions.

Inverters and converters contain more and more power electronics switches which may subsequently affect their reliability. Therefore, fault detection and location are essential to improve their reliability and to ensure continuous operation. In this paper, an  A C − D C − A C  converter with three-phase inverter is investigated under permanent, single and multiple open-circuit fault scenarios. Many entropies and multiscale entropies are then proposed to evaluate the complexity of the output currents by quantifying their entropies over a range of temporal scales. Among the multitude of entropies, only some entropies are able to differentiate healthy from open-circuit faulty conditions. Moreover, the simulation results show that these entropies are able to detect and locate the arms of the bridge with open-circuit faults. [ABSTRACT FROM AUTHOR]

Introduction: Infantile epileptic spasms syndrome (IESS) is an age-specific and severe epileptic encephalopathy. Although adrenocorticotropic hormone (ACTH) is currently considered the preferred first-line treatment, it is not always effective and may cause side effects. Therefore, seeking a reliable biomarker to predict the treatment response could benefit clinicians in modifying treatment options. Methods: In this study, the complexities of electroencephalogram (EEG) recordings from 15 control subjects and 40 patients with IESS before and after ACTH therapy were retrospectively reviewed using multiscale entropy (MSE). These 40 patients were divided into responders and nonresponders according to their responses to ACTH. Results: The EEG complexities of the patients with IESS were significantly lower than those of the healthy controls. A favorable response to treatment showed increasing complexity in the γ band but exhibited a reduction in the β/α-frequency band, and again significantly elevated in the δ band, wherein the latter was prominent in the parieto-occipital regions in particular. Greater reduction in complexity was significantly linked with poorer prognosis in general. Occipital EEG complexities in the γ band revealed optimized performance in recognizing response to the treatment, corresponding to the area under the receiver operating characteristic curves as 0.8621, while complexities of the δ band served as a fair predictor of unfavorable outcomes globally. Conclusion: We suggest that optimizing frequency-specific complexities over critical brain regions may be a promising strategy to facilitate predicting treatment response in IESS. [ABSTRACT FROM AUTHOR]

An abnormality in neural connectivity is linked to autism spectrum disorder (ASD). There is no way to test the concept of neural connectivity empirically. According to recent network theory and time series analysis findings, electroencephalography (EEG) can assess neural network architecture, a sign of activity in the brain. This systematic review aims to evaluate functional connectivity and spectral power using EEG signals. EEG records the brain activity of an individual by displaying wavy lines that depict brain cells' communication through electrical impulses. EEG can diagnose various brain disorders, including epilepsy and related seizure illness, brain dysfunction, tumors, and damage. We found 21 studies using two of the most common EEG analysis methods: functional connectivity and spectral power. ASD and non-ASD individuals were found to differ significantly in all selected papers. Due to high heterogeneity in the outcomes, generalizations cannot be drawn, and no single method is currently beneficial as a diagnostic tool. For ASD subtype delineation, the lack of research prevented the evaluation of these techniques as diagnostic tools. These findings confirm the presence of abnormalities in the EEG in ASD, but they are insufficient to diagnose. Our study suggests that EEG is useful in diagnosing ASD by evaluating entropy in the brain. Researchers may be able to develop new diagnostic methods for ASD which focuses on particular stimuli and brainwaves if they conduct more extensive studies with higher numbers and more rigorous study designs. [ABSTRACT FROM AUTHOR]

Distribution Entropy (DistEn) has been introduced as an alternative to Sample Entropy (SampEn) to assess the heart rate variability (HRV) on much shorter series without the arbitrary definition of distance thresholds. However, DistEn, considered a measure of cardiovascular complexity, differs substantially from SampEn or Fuzzy Entropy (FuzzyEn), both measures of HRV randomness. This work aims to compare DistEn, SampEn, and FuzzyEn analyzing postural changes (expected to modify the HRV randomness through a sympatho/vagal shift without affecting the cardiovascular complexity) and low-level spinal cord injuries (SCI, whose impaired integrative regulation may alter the system complexity without affecting the HRV spectrum). We recorded RR intervals in able-bodied (AB) and SCI participants in supine and sitting postures, evaluating DistEn, SampEn, and FuzzyEn over 512 beats. The significance of "case" (AB vs. SCI) and "posture" (supine vs. sitting) was assessed by longitudinal analysis. Multiscale DistEn (mDE), SampEn (mSE), and FuzzyEn (mFE) compared postures and cases at each scale between 2 and 20 beats. Unlike SampEn and FuzzyEn, DistEn is affected by the spinal lesion but not by the postural sympatho/vagal shift. The multiscale approach shows differences between AB and SCI sitting participants at the largest mFE scales and between postures in AB participants at the shortest mSE scales. Thus, our results support the hypothesis that DistEn measures cardiovascular complexity while SampEn/FuzzyEn measure HRV randomness, highlighting that together these methods integrate the information each of them provides. [ABSTRACT FROM AUTHOR]

Background: Cognitive impairment disrupts postural control, particularly when standing while performing an unrelated cognitive task (i.e., dual-tasking). The temporal dynamics of standing postural sway are "complex," and such complexity may reflect the capacity of the postural control system to adapt to task demands. We aimed to characterize the impact of cognitive impairment on such sway complexity in older adults. Methods: Forty-nine older adult males (Alzheimer's disease (AD): n = 21; mild cognitive impairment (MCI): n = 13; cognitively-intact: n=15) completed two 60-s standing trials in each of single-task and visual-search dual-task conditions. In the dualtask condition, participants were instructed to count the frequency of a designated letter in a block of letters projected on screen. The sway complexity of center-ofpressure fluctuations in anterior-posterior (AP) and medial-lateral (ML) direction was quantified using multiscale entropy. The dual-task cost to complexity was obtained by calculating the percent change of complexity from single-to dual-task condition. Results: Repeated-measures ANOVAs revealed significant main effects of group (F > 4.8, p < 0.01) and condition (F = 7.7, p < 0.007) on both AP and ML sway complexity; and significant interaction between group and condition for ML sway complexity (F = 3.7, p = 0.03). The AD group had the lowest dual-task ML complexity, as well as greater dual-task cost to ML (p = 0.03) compared to the other two groups. Visualsearch task accuracy was correlated with ML sway complexity in the dual-task condition (r = 0.42, p = 0.007), and the dual-task cost to ML sway complexity (r = 0.39, p = 0.01) across all participants. Conclusion: AD-related cognitive impairment was associated with a greater relative reduction in postural sway complexity from single- to dual-tasking. Sway complexity appears to be sensitive to the impact of cognitive impairment on standing postural control. [ABSTRACT FROM AUTHOR]

A physiological system encompasses numerous components that function at various time scales. To characterize the scale-dependent feature, the multiscale entropy (MSE) analysis has been proposed to describe the complex processes on multiple time scales. However, MSE analysis uses the relative scale factors to reveal the time-related dynamics, which may cause in-comparability of results from diverse studies with inconsistent sampling rates. In this study, in addition to the conventional MSE with relative scale factors, we also expressed MSE with absolute time scales (MaSE). We compared the effects of sampling rates on MSE and MaSE of simulated and real EEG time series. The results show that the previously found phenomenon (down-sampling can increase sample entropy) is just the projection of the compressing effect of down-sampling on MSE. And we have also shown the compressing effect of down-sampling on MSE does not change MaSE's profile, despite some minor right-sliding. In addition, by analyzing a public EEG dataset of emotional states, we have demonstrated improved classification rate after choosing appropriate sampling rate. We have finally proposed a working strategy to choose an appropriate sampling rate, and suggested using MaSE to avoid confusion caused by sampling rate inconsistency. This novel study may apply to a broad range of studies that would traditionally utilize sample entropy and MSE to analyze the complexity of an underlying dynamic process. [ABSTRACT FROM AUTHOR]

Background: Beat-to-beat blood pressure (BP) is an important cardiovascular output and regulated by neurophysiologic elements over multiple temporal scales. The multiscale dynamics of beat-to-beat BP fluctuation can be characterized by "BP complexity" and has been linked to age-related adverse health outcomes. We here aimed to examine whether BP complexity mediates the association between arterial stiffness and frailty.Method: This cross-sectional study was completed between January and October 2021. A total of 350 older adults completed assessments for frailty, arterial stiffness ((i.e., average brachial-ankle pulse wave velocity), and beat-to-beat finger BP. The complexity of beat-to-beat systolic (SBP) and diastolic (DBP) BP series were measured using multiscale entropy. The relationships between frailty, BP complexity and arterial stiffness were examined using ANOVA and linear regression models. The effects of BP complexity on the association between arterial stiffness and frailty were examined using mediation analyses.Results: Compared to non-frail, pre-frail and frail groups had significantly elevated lower SBP and DBP complexity (F>11, p<0.001), and greater arterial stiffness (F=16, p<0.001). Greater arterial stiffness was associated with lower BP complexity (β<-0.42, p<0.001). SBP and DBP complexity mediated the association between arterial stiffness and frailty (indirect effects>0.28), account for at least 47% of its total effects on frailty (Mediated proportion: SBP: 50%, DBP: 47%).Conclusion: This study demonstrates the association between BP complexity and frailty in older adults, and BP complexity mediates the association between arterial stiffness and frailty, suggesting that this metric would serve as a marker to help characterize important functions in older adults. [ABSTRACT FROM AUTHOR]

Classic attachment theory emphasizes the sensitivity of the parent to perceive and appropriately respond to the infant's cues. However, parent-child attachment is a dyadic interaction that is also dependent upon the sensitivity of the child to the early caregiving environment. Individual differences in infant sensitivity to parental cues is likely shaped by both the early caregiving environment as well as the infant's neurobiology, such as perceptual sensitivity to social stimuli. Here, we investigated associations between maternal postpartum depression and infant neurological sensitivity to affective touch using brain signal entropy – a metric of the brain's moment-to-moment variability related to signal processing. We recruited two independent samples of infants aged 0–5 months. In Sample 1 (n = 79), we found increased levels of maternal postpartum depression were associated with diminished perceptual sensitivity – i.e. lower entropy – to affective tactile stimulation specifically within the primary somatosensory cortex. In Sample 2 (n = 36), we replicated this finding and showed that this effect was not related to characteristics of the touch administered during the experiment. These results suggest that decreased affective touch early in life – a common consequence of postpartum depression – likely impacts the infant's perceptual sensitivity to affective touch and ultimately the formation of experience-dependent neural networks that support the successful formation of attachment relationships. • Maternal postpartum depression is associated with lower brain entropy in infancy. • PPD associations with entropy are specific to affective touch processing in S1. • An engineered brush demonstrates PPD associations with entropy are unrelated to acute touch properties. • Results highlight the importance of infant perceptual sensitivity to affective touch for attachment formation. [ABSTRACT FROM AUTHOR]