Sample entropy analysis of neonatal heart rate variability

Continuous prediction and clinical alarm management of late-onset sepsis in preterm infants using vital signs from a patient monitor

BACKGROUND AND OBJECTIVE

Continuous prediction of late-onset sepsis (LOS) could be helpful for improving clinical outcomes in neonatal intensive care units (NICU). This study aimed to develop an artificial intelligence (AI) model for assisting the bedside clinicians in successfully identifying infants at risk for LOS using non-invasive vital signs monitoring.

METHODS

In a retrospective study from the NICU of the Máxima Medical Center in Veldhoven, the Netherlands, a total of 492 preterm infants less than 32 weeks gestation were included between July 2016 and December 2018. Data on heart rate (HR), respiratory rate (RR), and oxygen saturation (SpO2) at 1 Hz were extracted from the patient monitor. We developed multiple AI models using 102 extracted features or raw time series to provide hourly LOS risk prediction. Shapley values were used to explain the model. For the best performing model, the effect of different vital signs and also the input type of signals on model performance was tested. To further assess the performance of applying the best performing model in a real-world clinical setting, we performed a simulation using four different alarm policies on continuous real-time predictions starting from three days after birth.

RESULTS

A total of 51 LOS patients and 68 controls were finally included according to the patient inclusion and exclusion criteria. When tested by seven-fold cross-validations, the mean (standard deviation) area under the receiver operating characteristic curve (AUC) six hours before CRASH was 0.875 (0.072) for the best performing model, compared to the other six models with AUC ranging from 0.782 (0.089) to 0.846 (0.083). The best performing model performed only slightly worse than the model learning from raw physiological waveforms (0.886 [0.068]), successfully detecting 96.1 % of LOS patients before CRASH. When setting the expected alarm window to 24 h and using a multi-threshold alarm policy, the sensitivity metric was 71.6 %, while the positive predictive value was 9.9 %, resulting in an average of 1.15 alarms per day per patient.

CONCLUSIONS

The proposed AI model, which learns from routinely collected vital signs, has the potential to assist clinicians in the early detection of LOS. Combined with interpretability and clinical alarm management, this model could be better translated into medical practice for future clinical implementation.

Visual feedback and motor memory contributions to sustained motor control deficits in autism spectrum disorder across childhood and into adulthood

Background Autistic individuals show deficits in sustained fine motor control which are associated with an over-reliance on visual feedback. Motor memory deficits also have been reported during sustained fine motor control in autism spectrum disorders (ASD). The development of motor memory and visuomotor feedback processes contributing to sustained motor control issues in ASD are not known. The present study aimed to characterize age-related changes in visual feedback and motor memory processes contributing to sustained fine motor control issues in ASD. Methods Fifty-four autistic participants and 31 neurotypical (NT) controls ages 10-25 years completed visually guided and memory guided sustained precision gripping tests by pressing on force sensors with their dominant hand index finger and thumb. For visually guided trials, participants viewed a stationary target bar and a force bar that moved upwards with increased force for 15s. During memory guided trials, the force bar was visible for 3s, after which participants attempted to maintain their force output without visual feedback for another 12s. To assess visual feedback processing, force accuracy, variability (standard deviation), and regularity (sample entropy) were examined. To assess motor memory, force decay latency, slope, and magnitude were examined during epochs without visual feedback. Results Relative to NT controls, autistic individuals showed a greater magnitude and steeper slope of force decay during memory guided trials. Across conditions, the ASD group showed reduced force accuracy (β = .41, R 2  = 0.043, t 79.3 =2.36, p = 0.021) and greater force variability (β=-2.16, R 2  = .143, t 77.1 =-4.04, p = 0.0001) and regularity (β=-.52, R 2  = .021, t 77.4 =-2.21, p = 0.030) relative to controls at younger ages, but these differences normalized by adolescence (age x group interactions). Lower force accuracy and greater force variability during visually guided trials and steeper decay slope during memory guided trials were associated with overall autism severity. Conclusions Our findings that autistic individuals show a greater rate and magnitude of force decay than NT individuals following the removal of visual feedback indicate that motor memory deficits contribute to fine motor control issues in ASD. Findings that sensorimotor differences in ASD were specific to younger ages suggest delayed development across multiple motor control processes.

Sample Entropy Computation on Signals with Missing Values

Sample entropy embeds time series into m-dimensional spaces and estimates entropy based on the distances between points in these spaces. However, when samples can be considered as missing or invalid, defining distance in the embedding space becomes problematic. Preprocessing techniques, such as deletion or interpolation, can be employed as a solution, producing time series without missing or invalid values. While deletion ignores missing values, interpolation replaces them using approximations based on neighboring points. This paper proposes a novel approach for the computation of sample entropy when values are considered as missing or invalid. The proposed algorithm accommodates points in the m-dimensional space and handles them there. A theoretical and experimental comparison of the proposed algorithm with deletion and interpolation demonstrates several advantages over these other two approaches. Notably, the deviation of the expected sample entropy value for the proposed methodology consistently proves to be lowest one.

Age-dependent functional development pattern in neonatal brain: An fMRI-based brain entropy study

The relationship between brain entropy (BEN) and early brain development has been established through animal studies. However, it remains unclear whether the BEN can be used to identify age-dependent functional changes in human neonatal brains and the genetic underpinning of the new neuroimaging marker remains to be elucidated. In this study, we analyzed resting-state fMRI data from the Developing Human Connectome Project, including 280 infants who were scanned at 37.5-43.5 weeks postmenstrual age. The BEN maps were calculated for each subject, and a voxel-wise analysis was conducted using a general linear model to examine the effects of age, sex, and preterm birth on BEN. Additionally, we evaluated the correlation between regional BEN and gene expression levels. Our results demonstrated that the BEN in the sensorimotor-auditory and association cortices, along the 'S-A' axis, was significantly positively correlated with postnatal age (PNA), and negatively correlated with gestational age (GA), respectively. Meanwhile, the BEN in the right rolandic operculum correlated significantly with both GA and PNA. Preterm-born infants exhibited increased BEN values in widespread cortical areas, particularly in the visual-motor cortex, when compared to term-born infants. Moreover, we identified five BEN-related genes (DNAJC12, FIG4, STX12, CETN2, and IRF2BP2), which were involved in protein folding, synaptic vesicle transportation and cell division. These findings suggest that the fMRI-based BEN can serve as an indicator of age-dependent brain functional development in human neonates, which may be influenced by specific genes.

Clinical knowledge-based ECG abnormalities detection using dual-view CNN-Transformer and external attention mechanism

BACKGROUND

Automatic abnormalities detection based on Electrocardiogram (ECG) contributes greatly to early prevention, computer aided diagnosis, and dynamic analysis of cardiovascular diseases. In order to achieve cardiologist-level performance, deep neural networks have been widely utilized to extract abstract feature representations. However, the mechanical stacking of numerous computationally intensive operations makes traditional deep neural networks suffer from inadequate learning, poor interpretability, and high complexity.

METHOD

To address these limitations, a clinical knowledge-based ECG abnormalities detection model using dual-view CNN-Transformer and external attention mechanism is proposed by mimicking the diagnosis of the clinicians. Considering the clinical knowledge that both the detailed waveform changes within a single heartbeat and the global changes throughout the entire recording have complementary roles in abnormalities detection, we presented a dual-view CNN-Transformer to extract and fuse spatial-temporal features from different views. In addition, the locations of the ECG where abnormalities occur provide more information than other areas. Therefore, two external attention mechanisms are designed and added to the corresponding views to help the network learn efficiently.

RESULTS

Experiment results on the 9-class dataset show that the proposed model achieves an average F1-score of 0.854±0.01 with a higher interpretability and a lower complexity, outperforming the state-of-the-art model.

CONCLUSIONS

Combining all these excellent features, this study provides a credible solution for automatic ECG abnormalities detection.

Autonomic modulation by SGLT2i or DPP4i in patients with diabetes favors cardiovascular outcomes as revealed by skin sympathetic nerve activity

Background

Sodium-glucose cotransporter 2 inhibitors (SGLT2i) and dipeptidyl peptidase-4 inhibitors (DPP4i) are important second-line treatments for patients with type 2 diabetes mellitus (T2DM). Patients taking SGLT2i have favorable cardiovascular outcomes via various mechanisms, including autonomic nervous system (ANS) modulation. This study aimed to use neuro-electrocardiography (neuECG) to test the effects of SGLT2i or DPP4i on the ANS.

Methods

Patients with T2DM, who did not reach target hemoglobin (Hb)A1C levels despite metformin treatment, were enrolled. SGLT2i or DPP4i were prescribed randomly unless a compelling indication was present. NeuECG and heart rate were recorded for 10 min before and after a 3-month treatment. The patients were treated according to standard practice and the obtained data for skin sympathetic nerve activity (SKNA) and ANS entropy were analyzed offline.

Results

We enrolled 96 patients, of which 49 received SGLT2i and 47 received DPP4i. The baseline parameters were similar between the groups. No adverse event was seen during the study period. In the burst analysis of SKNA at baseline, all parameters were similar. After the 3-month treatment, the firing frequency was higher in SGLT2i group (0.104 ± 0.045 vs 0.083 ± 0.033 burst/min, p < 0.05), with increased long firing duration (7.34 ± 3.66 vs 5.906 ± 2.921, p < 0.05) in 3-s aSKNA scale; the other parameters did not show any significant change. By symbolic entropy, the most complex patterns (Rank 3) were found to be significantly higher in SGLT2i-treated patients than in DDP4i-treated group (0.084 ± 0.028 vs 0.07 ± 0.024, p = 0.01) and the direction of change in Rank 3, after SGLT2i treatment, was opposite to that observed in the DDP4i group (0.012 ± 0.036 vs. -0.005 ± 0.037, p = 0.024). Our findings demonstrated the favorable autonomic modulation by SGLTi and the detrimental effects of DPP4i on ANS.

Conclusion

We demonstrated the autonomic modulation by SGLTi and DPP4i using SKNA in patients with DM, which might provide insights into the favorable outcomes of SGLT2i. Furthermore, we refined the analytical methods of neuECG, which uses SKNA to evaluate autonomic function.

Kalman filtering to reduce measurement noise of sample entropy: An electroencephalographic study

In the analysis of electroencephalography (EEG), entropy can be used to quantify the rate of generation of new information. Entropy has long been known to suffer from variance that arises from its calculation. From a sensor's perspective, calculation of entropy from a period of EEG recording can be treated as physical measurement, which suffers from measurement noise. We showed the feasibility of using Kalman filtering to reduce the variance of entropy for simulated signals as well as real-world EEG recordings. In addition, we also manifested that Kalman filtering was less time-consuming than moving average, and had better performance than moving average and exponentially weighted moving average. In conclusion, we have treated entropy as a physical measure and successfully applied the conventional Kalman filtering with fixed hyperparameters. Kalman filtering is expected to be used to reduce measurement noise when continuous entropy estimation (for example anaesthesia monitoring) is essential with high accuracy and low time-consumption.

Sample entropy correlates with intraventricular hemorrhage and mortality in premature infants early in life

BACKGROUND

Mortality and intraventricular hemorrhage (IVH) are common adverse outcomes in preterm infants and are challenging to predict clinically. Sample entropy (SE), a measure of heart rate variability (HRV), has shown predictive power for sepsis and other morbidities in neonates. We evaluated associations between SE and mortality and IVH in the first week of life.

METHODS

Participants were 389 infants born before 32 weeks of gestation for whom bedside monitor data were available. A total of 29 infants had IVH grade 3 or 4 and 31 infants died within 2 weeks of life. SE was calculated with the PhysioNet open-source benchmark. Logistic regressions assessed associations between SE and IVH and/or mortality with and without common clinical covariates over various hour of life (HOL) censor points.

RESULTS

Lower SE was associated with mortality by 4 HOL, but higher SE was very strongly associated with IVH and mortality at 24-96 HOL. Bootstrap testing confirmed SE significantly improved prediction using clinical variables at 96 HOL.

CONCLUSION

SE is a significant predictor of IVH and mortality in premature infants. Given IVH typically occurs in the first 24-72 HOL, affected infants may initially have low SE followed by a sustained period of high SE.

IMPACT

SE correlates with IVH and mortality in preterm infants early in life. SE combined with clinical factors yielded ROC AUCs well above 0.8 and significantly outperformed the clinical model at 96 h of life. Previous studies had not shown predictive power over clinical models. First study using the PhysioNet Cardiovascular Toolbox benchmark in young infants. Relative to the generally accepted timing of IVH in premature infants, we saw lower SE before or around the time of hemorrhage and a sustained period of higher SE after. Higher SE after acute events has not been reported previously.

Statistical Interdependence between Daily Precipitation and Extreme Daily Temperature in Regions of Mexico and Colombia

We study the statistical interdependence between daily precipitation and daily extreme temperature for regions of Mexico (14 climatic stations, period 1960-2020) and Colombia (7 climatic stations, period 1973-2020) using linear (cross-correlation and coherence) and nonlinear (global phase synchronization index, mutual information, and cross-sample entropy) synchronization metrics. The information shared between these variables is relevant and exhibits changes when comparing regions with different climatic conditions. We show that precipitation and temperature records from La Mojana are characterized by high persistence, while data from Mexico City exhibit lower persistence (less memory). We find that the information exchange and the level of coupling between the precipitation and temperature are higher for the case of the La Mojana region (Colombia) compared to Mexico City (Mexico), revealing that regions where seasonal changes are almost null and with low temperature gradients (less local variability) tend to display higher synchrony compared to regions where seasonal changes are very pronounced. The interdependence characterization between precipitation and temperature represents a robust option to characterize and analyze the collective dynamics of the system, applicable in climate change studies, as well as in changes not easily identifiable in future scenarios.

Multi-scale entropy assessment of magnetoencephalography signals in schizophrenia

Schizophrenia is a severe disruption in cognition and emotion, affecting fundamental human functions. In this study, we applied Multi-Scale Entropy analysis to resting-state Magnetoencephalography data from 54 schizophrenia patients and 98 healthy controls. This method quantifies the temporal complexity of the signal across different time scales using the concept of sample entropy. Results show significantly higher sample entropy in schizophrenia patients, primarily in central, parietal, and occipital lobes, peaking at time scales equivalent to frequencies between 15 and 24 Hz. To disentangle the contributions of the amplitude and phase components, we applied the same analysis to a phase-shuffled surrogate signal. The analysis revealed that most differences originate from the amplitude component in the δ, α, and β power bands. While the phase component had a smaller magnitude, closer examination reveals clear spatial patterns and significant differences across specific brain regions. We assessed the potential of multi-scale entropy as a schizophrenia biomarker by comparing its classification performance to conventional spectral analysis and a cognitive task (the n-back paradigm). The discriminative power of multi-scale entropy and spectral features was similar, with a slight advantage for multi-scale entropy features. The results of the n-back test were slightly below those obtained from multi-scale entropy and spectral features.

Complexity decline of hippocampal CA1 circuit model due to cholinergic deficiency associated with Alzheimer's disease

A hallmark of Alzheimer's disease (AD) is cholinergic system dysfunction, directly affecting the hippocampal neurons. Previous experiments have demonstrated that reduced complexity is one significant effect of AD on electroencephalography (EEG). Motivated by these, this study explores reduced EEG complexity of cholinergic deficiency in AD by neurocomputation. We first construct a new hippocampal CA1 circuit model with cholinergic action. M-current I M and calcium-activated potassium current I AHP are newly introduced in the model to describe cholinergic input from the medial septum. Then, by enhancing I M and I AHP to mimic cholinergic deficiency, how cholinergic deficiency influences the model complexity is investigated by sample entropy (SampEn) and approximate entropy (ApEn). Numerical results show a more severe cholinergic deficit with lower model complexity. Furthermore, we conclude that the decline of SampEn and ApEn is due to the greatly diminished excitability of model neurons. These suggest that decreased neuronal excitability due to cholinergic impairment may contribute to reduced EEG complexity in AD. Subsequently, statistical analysis between simulated AD patients and normal control (NC) groups demonstrates that SampEn and auto-mutual-information (AMI) decrease rates significantly differ. Compared to NC, AD patients have a lower SampEn and a less negative AMI decline rate. These imply a low rate of new-generation information in AD brains with cholinergic deficits. Interestingly, the statistical correlation between SampEn and AMI is analyzed, and they have a large negative Pearson correlation coefficient. Thus, AMI reduction rates may be a complementary tool for complex analysis. Our modeling and complex analysis are expected to provide a deeper understanding of the reduced EEG complexity resulting from cholinergic deficiency.

Association between antihypertensive treatment, blood pressure variability, and stroke severity and outcomes in acute ischemic stroke

OBJECTIVES

The management of blood pressure (BP) and the role of antihypertensive medications (AHT) in acute ischemic stroke (AIS) remain uncertain. This study aimed to investigate the impact of pre- and intra-stroke AHT use on systolic (SBP), diastolic (DBP), and blood pressure variability (BPV).

MATERIALS AND METHODS

A post-hoc analysis was conducted on 228 AIS patients from the PREVISE study. All patients underwent 24-hour ambulatory blood pressure monitoring within 48 h of symptom onset. Clinical and laboratory data, as well as AHT details, were recorded. Mean BP parameters and BPV for SBP and DBP were computed. The study endpoint was 3-month mortality.

RESULTS

The majority of stroke patients (84.2%) were already taking AHTs. Beta blockers and ACE inhibitors use before and after stroke were linked to higher DBP variability. Prior angiotensin receptor blockers (ARBs) and vasodilators use correlated with increased SBP variability and lower daytime SBP/DBP levels, respectively. The continuation, discontinuation, or change of AHTs after stroke onset did not significantly affect outcomes. Patients under AHTs during AIS exhibited reduced mortality, with those previously using calcium channel blockers experiencing less severe strokes, and those previously using ARBs showing better outcomes at three months.

CONCLUSIONS

These findings advocate for personalized BP management in AIS, based on a patient's antihypertensive history. These insights could enhance treatment efficacy, guide research, and improve care for acute ischemic stroke patients.

Reduction in limb-movement complexity at term-equivalent age is associated with motor developmental delay in very-preterm or very-low-birth-weight infants

Reduced complexity during the writhing period can be crucial in the spontaneous movements of high-risk infants for neurologic impairment. This study aimed to verify the association between quantified complexity of upper and lower-limb movements at term-equivalent age and motor development in very-preterm or very-low-birth-weight infants. Video images of spontaneous movements at term-equivalent age were collected from very-preterm or very-low-birth-weight infants. A pretrained pose-estimation model and sample entropy (SE) quantified the complexity of the upper- and lower-limb movements. Motor development was evaluated at 9 months of corrected age using Bayley Scales of Infant and Toddler Development, Third Edition. The SE measures were compared between infants with and without motor developmental delay (MDD). Among 90 infants, 11 exhibited MDD. SE measures at most of the upper and lower limbs were significantly reduced in infants with MDD compared to those without MDD (p < 0.05). Composite scores in the motor domain showed significant positive correlations with SE measures at most upper and lower limbs (p < 0.05). The results show that limb-movement complexity at term-equivalent age is reduced in infants with MDD at 9 months of corrected age. SE of limb movements can be a potentially useful kinematic parameter to detect high-risk infants for MDD.

From physiological complexity to data interactions-A case study of recordings from exercise monitoring

The popularity of nonlinear analysis has been growing simultaneously with the technology of effort monitoring. Therefore, considering the simple methods of physiological data collection and the approaches from the information domain, we proposed integrating univariate and bivariate analysis for the rest and effort comparison. Two sessions separated by an intensive training program were studied. Nine subjects participated in the first session (S1) and seven in the second session (S2). The protocol included baseline (BAS), exercise, and recovery phase. During all phases, electrocardiogram (ECG) was recorded. For the analysis, we selected corresponding data lengths of BAS and exercise usually lasting less than 5 min. We found the utility of the differences between original data and their surrogates for sample entropy Sdiff and Kullback-Leibler divergence KLDdiff. Sdiff of heart rate variability was negative in BAS and exercise but its sensitivity for phases discrimination was not satisfactory. We studied the bivariate analysis of RR intervals and corresponding QT peaks by Interlayer Mutual Information (IMI) and average edge overlap (AVO) markers. While the IMI parameter decreases in exercise conditions, AVO increased in effort compared to BAS. These findings conclude that researchers should consider a bivariate analysis of extracted RR intervals and corresponding QT datasets, when only ECG is recorded during tests.

Effects of environmental and task related conditions on postural control under concurrent visual feedback

BACKGROUND

Previous studies have yielded conflicting results on the facilitative effects of concurrent visual feedback (CVFB) on postural control. In addition, these effects have generally been assessed only during a single postural task, and their scope has been limited to training sessions in the acquisition phase but not in the later retention phase. One explanation for these conflicting results is that assessing postural control during a single postural task, such as quiet stance, may not be sufficiently challenging for the postural control system to infer balance abilities. We aimed to address these shortcomings by diversifying postural tasks and environmental conditions and by including acquisition and retention phases in the experiments.

RESEARCH QUESTIONS

Does the provision of CVFB of the instantaneous COP position improve performance on a variety of postural tasks compared to no-CVFB controls? Are the effects of the CVFB retained the following day? Do the observed effect sizes differ in magnitude between the environmental and task conditions under CVFB?

METHODS

Forty healthy young adults were randomly assigned to CVFB and no-CVFB control groups. The subjects performed three postural tasks: quiet, tandem, and single-leg stance, under two environmental conditions based on the stiffness of the supporting ground surface. Seven measures of postural sway, including ellipse area, mean speed, and sample entropy, were examined.

RESULTS

The provision of CVFB significantly increased sample entropy and sway-path length of the normalized posturogram. In addition, ellipse area, standard deviation of resultant distance, and range in the anteroposterior direction were significantly reduced in the CVFB group compared to the no-CVFB controls; however, these effects were not retained the following day without the addition of CVFB augmentation. The postural sway measures under CVFB were affected by task and environmental constraints, with varying effect sizes.

SIGNIFICANCE

This study demonstrated environment and task-specific changes in postural sway measures under CVFB, which facilitated postural control in a variety of postural tasks. Providing CVFB significantly increased sample entropy, indicating less regular postural sway. The features of CVFB that attract external attention and reduce cognitive overload are possible explanations for these findings.

Ground reaction forces better than center of pressure differentiate postural control between young female volleyball players and untrained peers

A comprehensive explanation of the relationship between postural control and athletic performance requires compare body balance in athletes with their never training counterparts. To fill this gap in relation to volleyball, the aim of this study was to compare the balance of intermediate adolescent female players (VOL, n = 61) with inactive peers (CON, n = 57). The participants were investigated in normal quiet stance during 20 s trials on a Kistler force plate. The traditional spatial (amplitude and mean speed) and temporal (frequency and entropy) indices were computed for ground reaction forces (GRF) and center-of-pressure (COP) time-series. The spatial parameters of the both time-series did not discriminate the two groups. However, the temporal GRF parameters revealed much lower values in VOL than in CON (p < .0001). This leads to three important conclusions regarding posturography applications. First, GRF and COP provide different information regarding postural control. Second, measures based on GRF are more sensitive to changes in balance related to volleyball training and perhaps to similar training and sports activity regimens. And third, the indicators calculated based on these two time series can complement each other and thus enrich the insight into the relationship between balance and sports performance level.

The influence of fear of falling on the control of upright stance across the lifespan

BACKGROUND

Standing at height, and subsequent changes in emotional state (e.g., fear of falling), lead to robust alterations in balance in adults. However, little is known about how height-induced postural threat affects balance performance in children. Children may lack the cognitive capability necessary to inhibit the processing of threat and fear-related stimuli, and as a result, may show more marked (and perhaps detrimental) changes in postural control compared to adults. This work explored the emotional and balance responses to standing at height in children, and compared responses to young and older adults.

METHODS

Children (age: 9.7 ± 0.8 years, n = 38), young adults (age: 21.8 ± 4.0 years, n = 45) and older adults (age: 73.3 ± 5.0 years, n = 15) stood in bipedal stance in two conditions: at ground level and 80 cm above ground. Centre of pressure (COP) amplitude (RMS), frequency (MPF) and complexity (sample entropy) were calculated to infer postural performance and strategy. Emotional responses were quantified by assessing balance confidence, fear of falling and perceived instability.

RESULTS

Young and older adults demonstrated a postural adaptation characterised by increased frequency and decreased amplitude of the COP, in conjunction with increased COP complexity (sample entropy). In contrast, children demonstrated opposite patterns of changes: they exhibited an increase in COP amplitude and decrease in both frequency and complexity when standing at height.

SIGNIFICANCE

Children and adults adopted different postural control strategies when standing at height. Whilst young and older adults exhibited a potentially protective "stiffening" response to a height-induced threat, children demonstrated a potentially maladaptive and ineffective postural adaptation strategy. These observations expand upon existing postural threat related research in adults, providing important new insight into understanding how children respond to standing in a hazardous situation.

Comprehensive linear and nonlinear analysis of the effects of spinning on dynamic balancing ability in Hungarian folk dancers

PURPOSE

In the case of Hungarian folk dancers, it is crucial to maintain correct posture and promptly respond to imbalances. However, traditional dances often lack specific training to develop these skills.

METHODS

In this present study, twelve dancers (8 male, 4 female, age: 21.7 ± 3.6 years) and ten non-dancers subjects forming a control group (6 male, 4 female, age: 21.6 ± 2.87 years) participated. During the measurements a 60-second long bipedal balancing test on the balance board was completed two times, and a spinning intervention was inserted in between the two sessions. The balance capabilities of the two groups were assessed through the characterization of motion on an unstable board, and the analysis of subject's center of mass and head movements.

RESULTS

Dancers applied a more sophisticated and resource-intensive strategy to address the balancing task, yielding a better balancing performance in terms of balance board parameters. By preferring a solid stability in the medio-lateral direction, a greater fluctuation in the anterior-posterior direction can be observed (e.g., significantly lower SampEn values). The overall more successful performance is further evidenced by within-subject comparison since significant differences were observed mostly within the control group. Based on the results, the advanced balancing ability of the folk dancer group is more likely to be acquired through years of experience.

CONCLUSION

The results indicate that additional specialized training could further enhance this ability, encouraging the reliance on poorly memorized corrective movements and reducing the risk of injury.

Optimizing the measurement of sample entropy in resting-state fMRI data

Introduction

The complexity of brain signals may hold clues to understand brain-based disorders. Sample entropy, an index that captures the predictability of a signal, is a promising tool to measure signal complexity. However, measurement of sample entropy from fMRI signals has its challenges, and numerous questions regarding preprocessing and parameter selection require research to advance the potential impact of this method. For one example, entropy may be highly sensitive to the effects of motion, yet standard approaches to addressing motion (e.g., scrubbing) may be unsuitable for entropy measurement. For another, the parameters used to calculate entropy need to be defined by the properties of data being analyzed, an issue that has frequently been ignored in fMRI research. The current work sought to rigorously address these issues and to create methods that could be used to advance this field.

Methods

We developed and tested a novel windowing approach to select and concatenate (ignoring connecting volumes) low-motion windows in fMRI data to reduce the impact of motion on sample entropy estimates. We created utilities (implementing autoregressive models and a grid search function) to facilitate selection of the matching length m parameter and the error tolerance r parameter. We developed an approach to apply these methods at every grayordinate of the brain, creating a whole-brain dense entropy map. These methods and tools have been integrated into a publicly available R package ("powseR"). We demonstrate these methods using data from the ABCD study. After applying the windowing procedure to allow sample entropy calculation on the lowest-motion windows from runs 1 and 2 (combined) and those from runs 3 and 4 (combined), we identified the optimal m and r parameters for these data. To confirm the impact of the windowing procedure, we compared entropy values and their relationship with motion when entropy was calculated using the full set of data vs. those calculated using the windowing procedure. We then assessed reproducibility of sample entropy calculations using the windowed procedure by calculating the intraclass correlation between the earlier and later entropy measurements at every grayordinate.

Results

When applying these optimized methods to the ABCD data (from the subset of individuals who had enough windows of continuous "usable" volumes), we found that the novel windowing procedure successfully mitigated the large inverse correlation between entropy values and head motion seen when using a standard approach. Furthermore, using the windowed approach, entropy values calculated early in the scan (runs 1 and 2) are largely reproducible when measured later in the scan (runs 3 and 4), although there is some regional variability in reproducibility.

Discussion

We developed an optimized approach to measuring sample entropy that addresses concerns about motion and that can be applied across datasets through user-identified adaptations that allow the method to be tailored to the dataset at hand. We offer preliminary results regarding reproducibility. We also include recommendations for fMRI data acquisition to optimize sample entropy measurement and considerations for the field.

Nonlinear Analysis of the Hand and Foot Force-Time Profiles in the Four Competitive Swimming Strokes

Human locomotion on water depends on the force produced by the swimmer to propel the body forward. Performance of highly complex motor tasks like swimming can yield minor variations that only nonlinear analysis can be sensitive enough to detect. The purpose of the present study was to examine the nonlinear properties of the hand/feet forces and describe their variations across the four competitive swimming strokes performing segmental and full-body swimming. Swimmers performed all-out bouts of 25 m in the four swimming strokes, swimming the full-body stroke, with the arm-pull only and with the leg kicking only. Hand/foot force and swimming velocity were measured. The Higuchi's fractal dimension (HFD) and sample entropy (SampEn) were used for the nonlinear analysis of force and velocity. Both the arm-pull and leg kicking alone were found to produce similar peak and mean hand/foot forces as swimming the full-body stroke. Hand force was more complex in breaststroke and butterfly stroke; conversely, kicking conditions were more complex in front crawl and backstroke. Moreover, the arm-pull and kicking alone tended to be more complex (higher HFD) but more predictable (lower SampEn) than while swimming the full-body stroke. There was no loss of force production from segmental swimming to the full-body counterpart. In conclusion, the number of segments in action influences the nonlinear behavior of the force produced and, when combining the four limbs, the complexity of the hand/foot force tends to decrease.

Perceived cognitive fatigue has only marginal effects on static balance control in healthy young adults

We examined the influence of perceived cognitive fatigue on static balance control in healthy young adults to gain greater clarity about this issue than provided in previous research. Based on the prevailing assumption in pertinent literature, we hypothesized that the influence of cognitive fatigue on balance control depends on the attentional effort required by the balance tasks being performed. To test this hypothesis, 44 young adults (24 women and 20 men) were alternately assigned to either the experimental group that was cognitively fatigued (using the 16-min TloadDback-task with individualized settings) or the control group (who watched a documentary). Before and after the intervention, the participants performed six balance tasks that differed in (attentional) control requirements, while recording the center of pressure (COP). From these time series, sway variability, mean speed, and sample entropy were calculated and analyzed statistically. Additionally, perceived cognitive fatigue was assessed using VAS scales. Statistical analyses confirmed that the balance tasks differed in control characteristics and that cognitive fatigue was elevated in the experimental group, but not in the control group. Nevertheless, no significant main effects of cognitive fatigue were found on any of the COP measures of interest, except for some non-robust interaction effects related primarily to sample entropy. These results suggest that, in young adults, postural control in static balance tasks is largely automatic and unaffected by task-induced state fatigue.

Bradycardia May Decrease Cardiorespiratory Coupling in Preterm Infants

Bradycardia, frequently observed in preterm infants, presents significant risks due to the immaturity of their autonomic nervous system (ANS) and respiratory systems. These infants may face cardiorespiratory events, leading to severe complications like hypoxemia and neurodevelopmental disorders. Although neonatal care has advanced, the influence of bradycardia on cardiorespiratory coupling (CRC) remains elusive. This exploratory study delves into CRC in preterm infants, emphasizing disparities between events with and without bradycardia. Using the Preterm Infant Cardio-Respiratory Signals (PICS) database, we analyzed interbeat (R-R) and inter-breath intervals (IBI) from 10 preterm infants. The time series were segmented into bradycardic (B) and non-bradycardic (NB) segments. Employing information theory measures, we quantified the irregularity of cardiac and respiratory time series. Notably, B segments had significantly lower entropy values for R-R and IBI than NB segments, while mutual information was higher in NB segments. This could imply a reduction in the complexity of respiratory and cardiac dynamics during bradycardic events, potentially indicating weaker CRC. Building on these insights, this research highlights the distinctive physiological characteristics of preterm infants and underscores the potential of emerging non-invasive diagnostic tools.

Alternative to Reduced Stresses on the Upper Extremity in a Standing Workstation

OBJECTIVE

This study evaluated a standing armrest to provide more acceptable ergonomic guidelines that may reduce the cost of standing computer workstations.

BACKGROUND

Of the many advantages of standing workstations, there have been no efforts to minimize the biomechanical cost, such as larger wrist extension and greater forearm muscle activity than sitting.

METHOD

Sixteen participants were asked to perform a typing task under a combination of the following factors: (1) desk shape (rectangular and concave); (2) desk height (0, +5, -5 cm from 90° elbow flexion); and (3) monitor height (0, -10 cm from the eyes). During the trials, the trunk kinematics, muscle activation levels, and CoP were recorded.

RESULTS

Both arms were further away from the upper body under the concave and +5 desk height than under the normal condition, but significant decreases in the extensor carpi radialis (8.6%), anterior deltoid (28.8%), and L4 paraspinals (5.5%) were observed. Similarly, the wrist extension angle decreased by 10.5° (42%) under this condition, but the posture required a 2.2° (19%) increase in wrist adduction angle. The CoP irregularity was greater under the concave workstation, indicating more complex motion.

CONCLUSION

A higher and concave desk can provide an armrest effect while engaged in a standing workstation by reducing the wrist extension and related muscle activation level, but at the cost of a larger wrist adduction angle.

APPLICATION

Providing a standing armrest (+5 cm height and concave desk) could reduce the stresses on the upper extremities, but a split keyboard should be considered to minimize wrist adduction.

Evaluation of measurement accuracy of wearable devices for heart rate variability

This paper proposed a method based on heart rate variability (HRV) for evaluating the accuracy of wearable devices in measuring heart rate. HRV refers to the variation in time intervals between successive heartbeats, widely used in many fields such as clinical and sports fields. Wearable devices such as Electrocardiogram (ECG) electrode patches have gained popularity due to their portability and ease of use. However, they can be prone to measurement interference caused by environmental noise, human respiration, etc. The proposed method consists of four main components: selection of "gold standard measurement devices", identification of HRV measurement metrics, construction of an HRV evaluation framework, and quantification of measurement errors. The method is validated through simulated experiments using ECG patches. The evaluation framework and quantification model established in this method have significant implications in establishment of industry standards and diagnosis of diseases in clinical practice.

Remote passive acoustic signal detection using multi-scale correlation networks and network spectrum distance in marine environment

Detecting acoustic signals in the ocean is crucial for port and coastal security, but existing methods often require informative priors. This paper introduces a new approach that transforms acoustic signal detection into network characterization using a MCN construction method. The method constructs a network representation of the acoustic signal by measuring pairwise correlations at different time scales. It proposes a network spectrum distance method that combines information geometry and graph signal processing theory to characterize these complex networks. By comparing the spectra of two networks, the method quantifies their similarity or dissimilarity, enabling comparisons of multi-scale correlation networks constructed from different time series data and tracking changes in nonlinear dynamics over time. The effectiveness of these methods is substantiated through comprehensive simulations and real-world data collected from the South China Sea. The results illustrate that the proposed approach attains a significant detection probability of over 90% when the signal-to-noise ratio exceeds -18 dB, whereas existing methods require a signal-to-noise ratio of at least -15 dB to achieve a comparable detection probability. This innovative approach holds promising applications in bolstering port security, facilitating coastal operations, and optimizing offshore activities by enabling more efficient detection of weak acoustic signals.

Prognostic significance of 24-h blood pressure and variability indices in the outcome of acute ischaemic stroke

BACKGROUND

The association between blood pressure (BP) levels and BP variability (BPV) following acute ischaemic stroke (AIS) and outcome remains controversial.

AIMS

To investigate the predictive value of systolic BP (SBP) and diastolic BP (DBP) and BPV measured using 24-h ambulatory blood pressure monitoring (ABPM) methods during AIS regarding outcome.

METHODS

A total of 228 AIS patients (175 without prior disability) underwent ABPM every 20 min within 48 h from onset using an automated oscillometric device (TM 2430, A&D Company Ltd) during day time (7:00-22:59) and night time (23:00-6:59). Risk factors, stroke subtypes, clinical and laboratory findings were recorded. Mean BP parameters and several BPV indices were calculated. End-points were death and unfavourable functional outcome (disability/death) at 3 months.

RESULTS

A total of 61 (26.7%) patients eventually died. Multivariate logistic regression analysis revealed that only mean night-time DBP (hazard ratio (HR): 1.04; 95% confidence interval (CI): 1.00-1.07) was an independent prognostic factor of death. Of the 175 patients without prior disability, 79 (45.1%) finally met the end-point of unfavourable functional outcome. Mean 24-h SBP (HR: 1.03; 95% CI: 1.00-1.05), day-time SBP (HR: 1.02; 95% CI: 1.00-1.05) and night-time SBP (HR: 1.03; 95% CI: 1.01-1.05), SBP nocturnal decline (HR: 0.93; 95% CI: 0.88-0.99), mean 24-h DBP (HR: 1.08; 95% CI: 1.03-1.13), day-time DBP (HR: 1.07; 95% CI: 1.03-1.12) and night-time DBP (HR: 1.06; 95% CI: 1.02-1.10) were independent prognostic factors of an unfavourable functional outcome.

CONCLUSIONS

In contrast with BPV indices, ABPM-derived BP levels and lower or absence of BP nocturnal decline in the acute phase are prognostic factors of outcome in AIS patients.

Aberrant brain entropy in posttraumatic stress disorder comorbid with major depressive disorder during the coronavirus disease 2019 pandemic

Aim

Previously, neuroimaging studies on comorbid Posttraumatic-Major depression disorder (PTSD-MDD) comorbidity found abnormalities in multiple brain regions among patients. Recent neuroimaging studies have revealed dynamic nature on human brain activity during resting state, and entropy as an indicator of dynamic regularity may provide a new perspective for studying abnormalities of brain function among PTSD-MDD patients. During the COVID-19 pandemic, there has been a significant increase in the number of patients with PTSD-MDD. We have decided to conduct research on resting-state brain functional activity of patients who developed PTSD-MDD during this period using entropy.

Methods

Thirty three patients with PTSD-MDD and 36 matched TCs were recruited. PTSD and depression symptoms were assessed using multiple clinical scales. All subjects underwent functional magnetic resonance imaging (fMRI) scans. And the brain entropy (BEN) maps were calculated using the BEN mapping toolbox. A two-sample t-test was used to compare the differences in the brain entropy between the PTSD-MDD comorbidity group and TC group. Furthermore, correlation analysis was conducted between the BEN changes in patients with PTSD-MDD and clinical scales.

Results

Compared to the TCs, PTSD-MDD patients had a reduced BEN in the right middle frontal orbital gyrus (R_MFOG), left putamen, and right inferior frontal gyrus, opercular part (R_IFOG). Furthermore, a higher BEN in the R_MFOG was related to higher CAPS and HAMD-24 scores in the patients with PTSD-MDD.

Conclusion

The results showed that the R_MFOG is a potential marker for showing the symptom severity of PTSD-MDD comorbidity. Consequently, PTSD-MDD may have reduced BEN in frontal and basal ganglia regions which are related to emotional dysregulation and cognitive deficits.

Improved multivariate multiscale sample entropy and its application in multi-channel data

Entropy, as a nonlinear feature in information science, has drawn much attention for time series analysis. Entropy features have been used to measure the complexity behavior of time series. However, traditional entropy methods mainly focus on one-dimensional time series originating from single-channel transducers and are incapable of handling the multidimensional time series from multi-channel transducers. Previously, the multivariate multiscale sample entropy (MMSE) algorithm was introduced for multi-channel data analysis. Although MMSE generalizes multiscale sample entropy and provides a new method for multidimensional data analysis, it lacks necessary theoretical support and has shortcomings, such as missing cross-channel correlation information and having biased estimation results. This paper proposes an improved multivariate multiscale sample entropy (IMMSE) algorithm to overcome these shortcomings. This paper highlights the existing shortcomings in MMSE under the generalized algorithm. The rationality of IMMSE is theoretically proven using probability theory. Simulations and real-world data analysis have shown that IMMSE is capable of effectively extracting cross-channel correlation information and demonstrating robustness in practical applications. Moreover, it provides theoretical support for generalizing single-channel entropy methods to multi-channel situations.

Movement kinematic and postural control differences when performing a visuomotor skill in real and virtual environments

Immersive technologies, like virtual and mixed reality, pose a novel challenge for our sensorimotor systems as they deliver simulated sensory inputs that may not match those of the natural environment. These include reduced fields of view, missing or inaccurate haptic information, and distortions of 3D space; differences that may impact the control of motor actions. For instance, reach-to-grasp movements without end-point haptic feedback are characterised by slower and more exaggerated movements. A general uncertainty about sensory input may also induce a more conscious form of movement control. We tested whether a more complex skill like golf putting was also characterized by more consciously controlled movement. In a repeated-measures design, kinematics of the putter swing and postural control were compared between (i) real-world putting, (ii) VR putting, and (iii) VR putting with haptic feedback from a real ball (i.e., mixed reality). Differences in putter swing were observed both between the real world and VR, and between VR conditions with and without haptic information. Further, clear differences in postural control emerged between real and virtual putting, with both VR conditions characterised by larger postural movements, which were more regular and less complex, suggesting a more conscious form of balance control. Conversely, participants actually reported less conscious awareness of their movements in VR. These findings highlight how fundamental movement differences may exist between virtual and natural environments, which may pose challenges for transfer of learning within applications to motor rehabilitation and sport.

Evaluation of autonomic nervous system activity in intradialytic hypotension using entropy and skin sympathetic nerve activity

Introduction

The function of the autonomic nervous system (ANS) is crucial in the development of intradialytic hypotension (IDH). This study introduced the entropy of heart rate variability (HRV) and skin sympathetic nerve activity (SKNA) to provide a complementary nonlinear and dynamic perspective for evaluating ANS function concerning IDH.

Methods

93 patients undergoing hemodialysis (HD) were enrolled, and the baseline data, electrocardiogram (ECG), and SKNA were collected. The patients were separated into the IDH and nonIDH groups based on the thresholds, which were characterized as reductions in systolic blood pressure (SBP) of at least 20 mm Hg or mean arterial pressure (MAP) of at least 10 mm Hg. We developed a logistic regression model for IDH after analyzing the changes in the time domain, frequency domain, the entropy of HRV, and SKNA indices during HD.

Results

After 4-h HD, the detected results for heart rate, the ratio of low frequency and high frequency (LF/HF), and average SKNA (aSKNA) all increased in both groups. Nine out of the ten HRV indices and aSKNA in the nonIDH group were higher than those in the IDH group at most moments. aSKNA was positively correlated with heart rate (p = 0.0001) and LF/HF (p = 0.0005) in the nonIDH group, while the correlation disappeared in the IDH group, which indicated a worse ANS response in IDH patients. The logistic regression model exhibited the results of initial SBP [odds ratio (OR) 1.076; p = 0.001], and the difference between the last and first segments (DLF) of heart rate [OR 1.101; p =0.012] and LF/HF [OR 0.209; p =0.034], as well as the extreme value of the difference between other segments and the first segments (EOF) of aSKNA [OR 2.908; p =0.017], which were independent indicators for IDH.

Discussion

The new nonlinear and dynamic assessment perspectives provided by the entropy of HRV and SKNA help to distinguish differences in ANS patterns between IDH patients and nonIDH patients and have the potential to be used in clinical monitoring for HD patients.

Modulation of center-of-pressure signal in children on the autism spectrum: A case-control study

BACKGROUND

Proper postural and motor control plays a fundamental role in the child's ontogenetic development. So far, the postural control in children on the autism spectrum has mainly been assessed with standard posturographic measurements of center of pressure (COP) displacements.

RESEARCH QUESTION

What are the differences in postural control between autistic and typically developing children?

METHODS

The study group comprised 16 autistic children aged 6-10 years, identified by a psychiatrist. The control group consisted of 16 typically developing children aged 6-10 years with no posture deformities, no pervasive developmental disorder and no history of postural control or movement deficits. The data were collected during quiet standing with eyes open using a force plate. To gain a better insight into the postural control processes, the rambling-trembling and sample entropy analyses were used in COP data processing.

RESULTS

Compared to typically developing children, those with autism spectrum had significantly higher values of COP and rambling trajectory parameters in the antero-posterior direction during quiet standing. The variables of the trembling trajectory did not differ significantly between the groups. The autistic children had significantly lower values of sample entropy in the antero-posterior direction compared to typically developing children.

SIGNIFICANCE

More advanced measures of COP displacements including the rambling-trembling method and sample entropy revealed differences in postural control between autistic and typically developing children. These methods may therefore contribute to functional assessment of postural control deficits in children on the autism spectrum.

Reliability of unconventional centre of pressure measures of quiet standing balance in people with chronic stroke

BACKGROUND

People with stroke often have asymmetric motor impairment. Investigating asymmetries in, and dynamic properties of, centre of pressure movement during quiet standing can inform how balance is controlled.

RESEARCH QUESTION

What are the test-retest reliabilities of unconventional measures of quiet standing balance control in people with chronic stroke?

METHODS

Twenty people with chronic stroke (>6 months post-stroke), who were able to stand for at least 30 s without support, were recruited. Participants completed two 30-second quiet standing trials in a standardized position. Unconventional measures of quiet standing balance control included: symmetry of variability in centre of pressure displacement and velocity, between-limb synchronization, and sample entropy. Root mean square of centre of pressure displacement and velocity in the antero-posterior and medio-lateral directions were also calculated. Intraclass correlation coefficients (ICCs) were used to determine test-retest reliability, and Bland-Altman plots were created to examine proportional biases.

RESULTS

ICC_3,2 were between 0.79 and 0.95 for all variables, indicating 'good' to 'excellent' reliability (>0.75). However, ICC_3,1 for symmetry indices and between-limb synchronization were < 0.75. Bland-Altman plots revealed possible proportional biases for root mean square of medio-lateral centre of pressure displacement and velocity and between-limb synchronization, with larger between-trial differences for participants with worse values.

SIGNIFICANCE

These findings suggest that centre of pressure measures extracted from a single 30-second quiet standing trial may have sufficient reliability for some research studies in chronic stroke. However, for clinical applications, the average of at least two trials may be required.

Exploring Complexity of Facial Dynamics in Autism Spectrum Disorder

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.

Altered brain entropy and functional connectivity patterns in generalized anxiety disorder patients

BACKGROUND

Generalized anxiety disorder (GAD) is a highly prevalent disease characterized by chronic, pervasive, and intrusive worry. Previous resting-state functional MRI (fMRI) studies on GAD have mainly focused on conventional static linear features. Entropy analysis of resting-state functional magnetic resonance imaging (rs-fMRI) has recently been adopted to characterize brain temporal dynamics in some neuropsychological or psychiatric diseases. However, the nonlinear dynamic complexity of brain signals has been rarely explored in GAD.

METHODS

We measured the approximate entropy (ApEn) and sample entropy (SampEn) of the resting-state fMRI data from 38 GAD patients and 37 matched healthy controls (HCs). The brain regions with significantly different ApEn and SampEn values between the two groups were extracted. Using these brain regions as seed points, we also investigated whether there are differences in whole brain resting-state function connectivity (RSFC) pattern between GADs and HCs. Correlation analysis was subsequently conducted to investigate the association between brain entropy, RSFC and the severity of anxiety symptoms. A linear support vector machine (SVM) was used to assess the discriminative power of BEN and RSFC features among GAD patients and HCs.

RESULTS

Compared to the HCs, patients with GAD showed increased levels of ApEn in the right angular cortex (AG) and increased levels of SampEn in the right middle occipital gyrus (MOG) as well as the right inferior occipital gyrus (IOG). Contrarily, compared to the HCs, patients with GAD showed decreased RSFC between the right AG and the right inferior parietal gyrus (IPG). The SVM-based classification model achieved 85.33 % accuracy (sensitivity: 89.19 %; specificity: 81.58 %; and area under the receiver operating characteristic curve: 0.9018). The ApEn of the right AG and the SVM-based decision value was positively correlated with the Hamilton Anxiety Scale (HAMA).

LIMITATIONS

This study used cross-sectional data and sample size was small.

CONCLUSION

Patients with GAD showed increased level of nonlinear dynamical complexity of ApEn in the right AG and decreased linear features of RSFC in the right IPG. Combining the linear and nonlinear features of brain signals may be used to effectively diagnose psychiatric disorders.

ECG-Based Semi-Supervised Anomaly Detection for Early Detection and Monitoring of Epileptic Seizures

Epilepsy is one of the most common brain diseases, characterized by frequent recurrent seizures or "ictal" states. A patient experiences uncontrollable muscular contractions, inducing loss of mobility and balance, which may result in injury or even death during these ictal states. Extensive investigation is vital to establish a systematic approach for predicting and informing patients about oncoming seizures ahead of time. Most methodologies developed are focused on the detection of abnormalities using mostly electroencephalogram (EEG) recordings. In this regard, research has indicated that certain pre-ictal alterations in the Autonomic Nervous System (ANS) can be detected in patient electrocardiogram (ECG) signals. The latter could potentially provide the basis for a robust seizure prediction approach. The recently proposed ECG-based seizure warning systems utilize machine learning models to classify a patient's condition. Such approaches require the incorporation of large, diverse, and thoroughly annotated ECG datasets, limiting their application potential. In this work, we investigate anomaly detection models in a patient-specific context with low supervision requirements. Specifically, we consider One-Class SVM (OCSVM), Minimum Covariance Determinant (MCD) Estimator, and Local Outlier Factor (LOF) models to quantify the novelty or abnormality of pre-ictal short-term (2-3 min) Heart Rate Variability (HRV) features of patients, trained on a reference interval considered to contain stable heart rate as the only form of supervision. Our models are evaluated against labels that were either hand-picked or automatically generated (weak labels) by a two-phase clustering procedure for samples of the "Post-Ictal Heart Rate Oscillations in Partial Epilepsy" (PIHROPE) dataset recorded by the Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, achieving detection in 9 out of 10 cases, with average AUCs of over 93% across all models and warning times ranging from 6 to 30 min prior to seizure. The proposed anomaly detection and monitoring approach can potentially pave the way for early detection and warning of seizure incidents based on body sensor inputs.

Characterization of autonomic states by complex sympathetic and parasympathetic dynamics

Assessment of heartbeat dynamics provides a promising framework for non-invasive monitoring of cardiovascular and autonomic states. Nevertheless, the non-specificity of such measurements among clinical populations and healthy conditions associated with different autonomic states severely limits their applicability and exploitation in naturalistic conditions. This limitation arises especially when pathological or postural change-related sympathetic hyperactivity is compared to autonomic changes across age and experimental conditions. In this frame, we investigate the intrinsic irregularity and complexity of cardiac sympathetic and vagal activity series in different populations, which are associated with different cardiac autonomic dynamics. Sample entropy, fuzzy entropy, and distribution entropy are calculated on the recently proposed sympathetic and parasympathetic activity indices (SAI and PAI) series, which are derived from publicly available heartbeat series of congestive heart failure patients, elderly and young subjects watching a movie in the supine position, and healthy subjects undergoing slow postural changes. Results show statistically significant differences between pathological/old subjects and young subjects in the resting state and during slow tilt, with interesting trends in SAI- and PAI-related entropy values. Moreover, while CHF patients and healthy subjects in upright position show the higher cardiac sympathetic activity, elderly and young subjects in resting state showed higher vagal activity. We conclude that quantification of intrinsic cardiac complexity from sympathetic and vagal dynamics may provide new physiology insights and improve on the non-specificity of heartbeat-derived biomarkers.

Heart Rate Variability Analysis May Identify Individuals With Williams-Beuren Syndrome at Risk of Sudden Death

BACKGROUND

Williams-Beuren syndrome (WBS) (Online Mendelian Inheritance in Man #194050) is a rare genetic multisystem disorder resulting from a chromosomal microdeletion at 7q11.23. The condition is characterized by distinct facies, intellectual disability, and supravalvar aortic stenosis. Those with WBS have an increased risk of sudden death, but mechanisms underlying this phenotype are incompletely understood.

OBJECTIVES

The aim of this study was to quantify and compare autonomic activity as reflected by heart rate variability (HRV) measures in a cohort of individuals with WBS (n = 18) and age- and sex-matched control subjects (n = 18).

METHODS

We performed HRV analysis on 24-hour electrocardiography recordings using nonlinear, time and frequency domain analyses on a cohort of subjects with WBS and age- and sex-matched control subjects enrolled in a prospective cross-sectional study designed to characterize WBS disease natural history.

RESULTS

WBS subjects demonstrated diminished HRV (reflected by the SD of the NN intervals [P = 0.0001], SD of the average NN interval for 5-minute intervals over 24 hours [P < 0.0001], average of the 5-minute SDs of NN intervals for 24 hours [P = 0.0002], root mean square of successive differences of NN intervals [P = 0.0004], short axis of the Poincaré plot (SD1) [P < 0.0001], and long axis of the Poincaré plot [P < 0.0001]) and indirect markers of parasympathetic activity (reflected by the percent of NN intervals different from previous by 50% or more of local average [P < 0.0007], root mean square of successive differences of NN intervals [P = 0.0004], natural log high-frequency power [P = 0.0038], and SD1 [P < 0.0001]). Additional parameters were also significantly different, including natural log very low-frequency power (decreased; P = 0.0002), natural log low-frequency power (decreased; P = 0.0024), and SD1 divided by the long axis of the Poincaré plot (decreased; P < 0.0001).

CONCLUSIONS

Individuals with WBS demonstrate significant HRV abnormalities consistent with diminished autonomic reserve. Future studies will be needed to determine the relationship between autonomic dysregulation observed and sudden death risk seen in these patients. (Impact of Elastin Mediated Vascular Stiffness on End Organs; NCT02840448).

Application of probability theory to neonatal cardiac evaluation

Based on probability theory, a methodology that allows diagnosing neonatal cardiac dynamics was previously developed; however, diagnostic applications of this method are required to validate it to the neonatal cardiac dynamics was conducted, allowing to differentiate normal from pathological dynamics. The hourly maximum and minimum heart rate values from 39 continuous and ambulatory electrocardiographic records with a minimum length of 21 hours were taken, from newborns between 0 and 10 days of life, 9 clinically within normality limits and 30 with cardiac pathologies. The probability of occurrence of heart rates in ranges of 5 beats/minute was calculated. The distributions of probability were analysed, and finally the diagnosis was determined by the physical-mathematical methodology. Then, a statistical validation of sensitivity, specificity, and diagnostic agreement was performed. Normal registries showed probability distributions with absent or minimal presence of heart rates of the ranges between 125 and 135 beats/minute, while the abnormal ones had values within these ranges, as well as absence or minimal presence of heart rates from 75 beats/minute to 85 beats/minute. The sensitivity and specificity were 100%, and the Kappa coefficient had a value of 1. Hereby, it is concluded that through an application of a physical-mathematical methodology of neonatal cardiac diagnosis, it is possible to differentiate normality from disease.

Human Motor Noise Assessed by Electromagnetic Sensors and Its Relationship with the Degrees of Freedom Involved in Movement Control

Motor variability is a prominent feature of the human movement that, nowadays, can be easily measured through different sensors and analyzed using different types of variables, and it seems to be related to functional and adaptative motor behavior. It has been stated that motor variability is related to the system's flexibility needed to choose the right degrees of freedom (DoFs) to adapt to constant environmental changes. However, the potential relationship between motor variability and DoFs is unknown. The aim of this study was to analyze how motor variability, both the amount and structure, changes depending on the mechanical DoFs involved in the movement control. For this purpose, movement variability was assessed by a tracking sensor in five tasks with different DoFs, and the amount, using standard deviation, and the structure of variability, through fuzzy entropy and detrended fluctuation analysis, were also assessed. The results showed a higher amount of variability and a less predictable and more auto-correlated variability structure in the long-term when more mechanical DoFs are implied. The studies that analyze motor variability should consider the type of movement and the DoFs involved in the analyzed task since, as the findings have shown, both factors have a noticeable influence on the amount and the structure of motor variability.

One-Leg Stance Postural Sway Is Not Benefited by Bicycle Motocross Practice in Elite Riders

Balance has been positioned as an important performance skill in sport. Differences in postural control have been found between levels of expertise. However, this statement remains unanswered in some cyclic sports. This work aimed to describe the one-leg balance performance of a sample of elite BMX riders-racing and freestyle-compared to a control group formed by recreational athletes. The center of pressure (COP) of nineteen international BMX riders (freestyle, n = 7; racing, n = 12) and twenty physically active adults was analyzed in a 30-s one-leg stance test on both legs. COP dispersion and velocity variables were analyzed. Non-linear dynamics of postural sway were evaluated through Fuzzy Entropy and Detrended Fluctuation Analysis. BMX athletes did not show differences between legs in any of the variables. The control group did show differences between the dominant and non-dominant leg in the magnitude of variability of the COP in the mediolateral axis. Group comparison revealed non-significant differences. International BMX athletes did not show better balance parameters than the control group in a one-leg stance balance task. The adaptations derived from BMX practice do not have a significant impact in one-leg stance balance performance.

Considerations for Applying Entropy Methods to Temporally Correlated Stochastic Datasets

The goal of this paper is to highlight considerations and provide recommendations for analytical issues that arise when applying entropy methods, specifically Sample Entropy (SampEn), to temporally correlated stochastic datasets, which are representative of a broad range of biomechanical and physiological variables. To simulate a variety of processes encountered in biomechanical applications, autoregressive fractionally integrated moving averaged (ARFIMA) models were used to produce temporally correlated data spanning the fractional Gaussian noise/fractional Brownian motion model. We then applied ARFIMA modeling and SampEn to the datasets to quantify the temporal correlations and regularity of the simulated datasets. We demonstrate the use of ARFIMA modeling for estimating temporal correlation properties and classifying stochastic datasets as stationary or nonstationary. We then leverage ARFIMA modeling to improve the effectiveness of data cleaning procedures and mitigate the influence of outliers on SampEn estimates. We also emphasize the limitations of SampEn to distinguish among stochastic datasets and suggest the use of complementary measures to better characterize the dynamics of biomechanical variables. Finally, we demonstrate that parameter normalization is not an effective procedure for increasing the interoperability of SampEn estimates, at least not for entirely stochastic datasets.

The psychotomimetic ketamine disrupts the transfer of late sensory information in the corticothalamic network

In prodromal and early schizophrenia, disorders of attention and perception are associated with structural and chemical brain abnormalities and with dysfunctional corticothalamic networks exhibiting disturbed brain rhythms. The underlying mechanisms are elusive. The non-competitive NMDA receptor antagonist ketamine simulates the symptoms of prodromal and early schizophrenia, including disturbances in ongoing and task & sensory-related broadband beta-/gamma-frequency (17-29 Hz/30-80 Hz) oscillations in corticothalamic networks. In normal healthy subjects and rodents, complex integration processes, like sensory perception, induce transient, large-scale synchronised beta/gamma oscillations in a time window of a few hundred ms (200-700 ms) after the presentation of the object of attention (e.g., sensory stimulation). Our goal was to use an electrophysiological multisite network approach to investigate, in lightly anesthetised rats, the effects of a single psychotomimetic dose (2.5 mg/kg, subcutaneous) of ketamine on sensory stimulus-induced oscillations. Ketamine transiently increased the power of baseline beta/gamma oscillations and decreased sensory-induced beta/gamma oscillations. In addition, it disrupted information transferability in both the somatosensory thalamus and the related cortex and decreased the sensory-induced thalamocortical connectivity in the broadband gamma range. The present findings support the hypothesis that NMDA receptor antagonism disrupts the transfer of perceptual information in the somatosensory cortico-thalamo-cortical system.

The Effects of Conscious Movement Processing on the Neuromuscular Control of Posture

Maintaining balance is thought to primarily occur sub-consciously. Occasionally, however, individuals will direct conscious attention towards balance, e.g., in response to a threat to balance. Such conscious movement processing (CMP) increases the reliance on attentional resources and may disrupt balance performance. However, the underlying changes in neuromuscular control remain poorly understood. We investigated the effects of CMP (manipulated using verbal instructions) on neural control of posture in twenty-five adults (11 females, mean age = 23.9, range = 18-33). Participants performed 90-s, bipedal stance balance trials in high- and low-CMP conditions, during both stable (solid surface) and unstable (foam) task conditions. Postural sway amplitude, frequency and complexity were used to assess postural control. Surface EMG was recorded bilaterally from lower leg muscles (Soleus, Tibialis Anterior, Gastrocnemius Medialis, Peroneus Longus) and intermuscular coherence (IMC) was assessed for 12 muscle pairs across four frequency bands. We observed significantly increased sway amplitude, and decreased sway frequency and complexity in the high- compared to the low-CMP conditions. All sway variables increased in the unstable compared to the stable conditions. We observed reduced beta band IMC between several muscle pairs during high- compared to low-CMP, but these findings did not remain significant after controlling for multiple comparisons. Finally, IMC significantly increased in the unstable conditions for most muscle combinations and frequency bands. In all, results tentatively suggest that CMP-induced changes in sway outcomes may be facilitated by reduced beta-band IMC, but these findings need to be replicated before they can be interpreted more conclusively.

Enhancing classification of preterm-term birth using continuous wavelet transform and entropy-based methods of electrohysterogram signals

Introduction

Despite vast research, premature birth's electrophysiological mechanisms are not fully understood. Prediction of preterm birth contributes to child survival by providing timely and skilled care to both mother and child. Electrohysterography is an affordable, noninvasive technique that has been highly sensitive in diagnosing preterm labor. This study aimed to choose the more appropriate combination of characteristics, such as electrode channel and bandwidth, as well as those linear, time-frequency, and nonlinear features of the electrohysterogram (EHG) for predicting preterm birth using classifiers.

Methods

We analyzed two open-access datasets of 30 minutes of EHG obtained in regular checkups of women around 31 weeks of pregnancy who experienced premature labor (P) and term labor (T). The current approach filtered the raw EHGs in three relevant frequency subbands (0.3-1 Hz, 1-2 Hz, and 2-3Hz). The EHG time series were then segmented to create 120-second windows, from which individual characteristics were calculated. The linear, time-frequency, and nonlinear indices of EHG of each combination (channel-filter) were fed to different classifiers using feature selection techniques.

Results

The best performance, i.e., 88.52% accuracy, 83.83% sensitivity, and 93.22% specificity, was obtained in the 2-3 Hz bands using Medium Frequency, Continuous Wavelet Transform (CWT), and entropy-based indices. Interestingly, CWT features were significantly different in all filter-channel combinations. The proposed study uses small samples of EHG signals to diagnose preterm birth accurately, showing their potential application in the clinical environment.

Discussion

Our results suggest that CWT and novel entropy-based features of EHG could be suitable descriptors for analyzing and understanding the complex nature of preterm labor mechanisms.

Decoding the correlation between heart activation and walking path by information-based analysis

BACKGROND

One of the important areas of heart research is to analyze heart rate variability during (HRV) walking.

OBJECTIVE

In this research, we investigated the correction between heart activation and the variations of walking paths.

METHOD

We employed Shannon entropy to analyze how the information content of walking paths affects the information content of HRV. Eight healthy students walked on three designed walking paths with different information contents while we recorded their ECG signals. We computed and analyzed the Shannon entropy of the R-R interval time series (as an indicator of HRV) versus the Shannon entropy of different walking paths and accordingly evaluated their relation.

RESULTS

According to the obtained results, walking on the path that contains more information leads to less information in the R-R time series.

CONCLUSION

The analysis method employed in this research can be extended to analyze the relation between other physiological signals (such as brain or muscle reactions) and the walking path.

Cardiorespiratory signature of neonatal sepsis: development and validation of prediction models in 3 NICUs

BACKGROUND

Heart rate characteristics aid early detection of late-onset sepsis (LOS), but respiratory data contain additional signatures of illness due to infection. Predictive models using cardiorespiratory data may improve early sepsis detection. We hypothesized that heart rate (HR) and oxygenation (SpO₂) data contain signatures that improve sepsis risk prediction over HR or demographics alone.

METHODS

We analyzed cardiorespiratory data from very low birth weight (VLBW, <1500 g) infants admitted to three NICUs. We developed and externally validated four machine learning models to predict LOS using features calculated every 10 m: mean, standard deviation, skewness, kurtosis of HR and SpO₂, and cross-correlation. We compared feature importance, discrimination, calibration, and dynamic prediction across models and cohorts. We built models of demographics and HR or SpO₂ features alone for comparison with HR-SpO₂ models.

RESULTS

Performance, feature importance, and calibration were similar among modeling methods. All models had favorable external validation performance. The HR-SpO₂ model performed better than models using either HR or SpO₂ alone. Demographics improved the discrimination of all physiologic data models but dampened dynamic performance.

CONCLUSIONS

Cardiorespiratory signatures detect LOS in VLBW infants at 3 NICUs. Demographics risk-stratify, but predictive modeling with both HR and SpO₂ features provides the best dynamic risk prediction.

IMPACT

Heart rate characteristics aid early detection of late-onset sepsis, but respiratory data contain signatures of illness due to infection. Predictive models using both heart rate and respiratory data may improve early sepsis detection. A cardiorespiratory early warning score, analyzing heart rate from electrocardiogram or pulse oximetry with SpO₂, predicts late-onset sepsis within 24 h across multiple NICUs and detects sepsis better than heart rate characteristics or demographics alone. Demographics risk-stratify, but predictive modeling with both HR and SpO₂ features provides the best dynamic risk prediction. The results increase understanding of physiologic signatures of neonatal sepsis.

The complexity analysis of cerebral oxygen saturation during pneumoperitoneum and Trendelenburg position: a retrospective cohort study

BACKGROUND

The human brain is a highly complex and nonlinear system, nonlinear complexity measures such as approximate entropy (ApEn) and sample entropy (SampEn) can better reveal characteristics of brain dynamics. However, no studies report complexity of perioperative physiological signals to reveal how brain complexity associates with age, varies along with the development of surgery and postoperative neurological complications.

AIM

This study examined the complexity of intraoperative regional cerebral oxygen saturation (rSO₂), aiming to reveal brain dynamics during surgery.

METHODS

This retrospective cohort study enrolled patients who scheduled for robot-assisted urological surgery. Intraoperative rSO₂ was continuously monitored throughout the surgery. Postoperative delirium (POD) was diagnosed by the Confusion Assessment Method. ApEn and SampEn were used to characterize the complexity of rSO₂. Pearson correlation coefficients were used to measure the correlation between complexity of rSO₂ and age. The association between complexity of rSO₂ and POD was examined using T tests.

RESULTS

A total of 68 patients (mean [SD] age, 63.0 (12.0) years; 47 (69.1%) males) were include in this analysis. There was a significant reverse relationship between the complexity of rSO₂ and age (The correlation coefficients range between - 0.32 and - 0.28, all p < 0.05). Patients  ≥ 75 years showed significantly lower complexity of rSO₂ than the other two groups. Older age remained an independent factor influencing complexity of rSO₂ after adjusting for a number of covariates. Six patients (8.8%) developed POD, and POD patients had lower complexity of rSO₂ compared with non-POD patients.

CONCLUSIONS

The complexity of rSO₂ may serve as a new candidate marker of aging and POD prediction.

Comparison of oxygen supplementation in very preterm infants: Variations of oxygen saturation features and their application to hypoxemic episode based risk stratification

Background

Oxygen supplementation is commonly used to maintain oxygen saturation (SpO₂) levels in preterm infants within target ranges to reduce intermittent hypoxemic (IH) events, which are associated with short- and long-term morbidities. There is not much information available about differences in oxygenation patterns in infants undergoing such supplementations nor their relation to observed IH events. This study aimed to describe oxygenation characteristics during two types of supplementation by studying SpO₂ signal features and assess their performance in hypoxemia risk screening during NICU monitoring.

Subjects and methods

SpO₂ data from 25 infants with gestational age <32 weeks and birthweight <2,000 g who underwent a cross over trial of low-flow nasal cannula (NC) and digitally-set servo-controlled oxygen environment (OE) supplementations was considered in this secondary analysis. Features pertaining to signal distribution, variability and complexity were estimated and analyzed for differences between the supplementations. Univariate and regularized multivariate logistic regression was applied to identify relevant features and develop screening models for infants likely to experience a critically high number of IH per day of observation. Their performance was assessed using area under receiver operating curves (AUROC), accuracy, sensitivity, specificity and F1 scores.

Results

While most SpO₂ measures remained comparable during both supplementations, signal irregularity and complexity were elevated while on OE, pointing to more volatility in oxygen saturation during this supplementation mode. In addition, SpO₂ variability measures exhibited early prognostic value in discriminating infants at higher risk of critically many IH events. Poincare plot variability at lag 1 had AUROC of 0.82, 0.86, 0.89 compared to 0.63, 0.75, 0.81 for the IH number, a clinical parameter at observation times of 30 min, 1 and 2 h, respectively. Multivariate models with two features exhibited validation AUROC > 0.80, F1 score > 0.60 and specificity >0.85 at observation times ≥ 1 h. Finally, we proposed a framework for risk stratification of infants using a cumulative risk score for continuous monitoring.

Conclusion

Analysis of oxygen saturation signal routinely collected in the NICU, may have extensive applications in inferring subtle changes to cardiorespiratory dynamics under various conditions as well as in informing clinical decisions about infant care.

Complexity analysis of head movements in autistic toddlers

BACKGROUND

Early differences in sensorimotor functioning have been documented in young autistic children and infants who are later diagnosed with autism. Previous research has demonstrated that autistic toddlers exhibit more frequent head movement when viewing dynamic audiovisual stimuli, compared to neurotypical toddlers. To further explore this behavioral characteristic, in this study, computer vision (CV) analysis was used to measure several aspects of head movement dynamics of autistic and neurotypical toddlers while they watched a set of brief movies with social and nonsocial content presented on a tablet.

METHODS

Data were collected from 457 toddlers, 17-36 months old, during their well-child visit to four pediatric primary care clinics. Forty-one toddlers were subsequently diagnosed with autism. An application (app) displayed several brief movies on a tablet, and the toddlers watched these movies while sitting on their caregiver's lap. The front-facing camera in the tablet recorded the toddlers' behavioral responses. CV was used to measure the participants' head movement rate, movement acceleration, and complexity using multiscale entropy.

RESULTS

Autistic toddlers exhibited significantly higher rate, acceleration, and complexity in their head movements while watching the movies compared to neurotypical toddlers, regardless of the type of movie content (social vs. nonsocial). The combined features of head movement acceleration and complexity reliably distinguished the autistic and neurotypical toddlers.

CONCLUSIONS

Autistic toddlers exhibit differences in their head movement dynamics when viewing audiovisual stimuli. Higher complexity of their head movements suggests that their movements were less predictable and less stable compared to neurotypical toddlers. CV offers a scalable means of detecting subtle differences in head movement dynamics, which may be helpful in identifying early behaviors associated with autism and providing insight into the nature of sensorimotor differences associated with autism.