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Radioactive radon gas poses significant threats to human health. Understanding the complexities of radon distribution and the dynamic relationship with atmospheric parameters will help in mitigating its impact. In this study, Multifractal Detrended Fluctuation Analysis (MF-DFA) and chaos analysis were used to examine the fractal structure in radon gas at La Cueva del Viento, Tenerife, between January 2021 and December 2022. The results showed that radon has multifractal properties in 2021, 2022, and 2021-2022, with values of the spectrum with of about 0.43, 0.49 and 0.44 respectively. The multifractality in radon gas was found to be driven by both long-range correlations and fat-tail distribution. Radon gas concentration at La Cueva del Viento was found to be chaotic in nature, hence, long-term prediction is impossible. Meteorological parameters such as relative humidity, air temperature and pressure were found to contribute to the variation in radon gas concentration within the cave. Relative humidity was observed to have the strongest cross-correlation with radon gas in 2021, 2022, and 2021-2022. The results from this study will help in dosimetric control for both workers and visitors to the cave., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Neurodegenerative diseases (NDDs), which are caused by the degeneration of neurons and their functions, affect a significant part of the world's population. Although gait disorders are one of the critical and common markers to determine the presence of NDDs, diagnosing which NDD the patients have among a group of NDDs using gait data is still a significant challenge to be addressed. In this study, we addressed the multi-class classification of NDDs and aim to diagnose Parkinson's disease (PD), Amyotrophic lateral sclerosis disease (AD), and Huntington's disease (HD) from a group containing NDDs and healthy control subjects. We also examined the impact of disease-specific identified features derived from VGRF signals. Detrended Fluctuation Analysis (DFA), Dynamic Time Warping (DTW) and Autocorrelation (AC) were used for feature extraction on Vertical Ground Reaction Force (VGRF) signals. To compare the performance of the features, we employed Support Vector Machines, K-Nearest Neighbors, and Neural Networks as classifiers. In three-class problem addressing the classification of AD, PD and HD 93.3% accuracy rate was achieved, while in the four classes case, in which NDDs and HC groups were considered together, 93.5% accuracy rate was yielded. Considering the disease-specific impact of features, it is revealed that while DFA based features diagnose patients with AD with the highest accuracy, DTW has been shown to be more successful in diagnosing PD. AC based features provided the highest accuracy in diagnosing HD. Although gait disorder is common for NDDs, each disease may have its own distinctive gait rhythms; therefore, it is important to identify disease-specific patterns and parameters for the diagnosis of each disease. To increase the diagnostic accuracy, it is necessary to use a combination of features, which were effective for each disease diagnosis. Determining a limited number of disease-specific features would provide NDD diagnostic systems suitable to be deployed in edge-computing environments., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: In a secondary analysis of data taken from a publicly available database, we examined cognitive performance, postural sway, and relations between them for 4 groups: younger and older individuals with versus without a recent history of falls. Our objective was to compare linear versus nonlinear measures of postural activity as post hoc predictors of cognitive performance and falling., Methods: We evaluated standing body sway in 147 participants (18-85-years old) over 60 seconds, separately with eyes-open and with eyes-closed. We evaluated cognitive performance using portions of the Trail Making Test. We evaluated postural activity in terms of standard deviation, velocity, and amplitude of the Center of Pressure (CoP). Separately, we used detrended fluctuation analysis (DFA) to examine the complexity of CoP displacements. Using analysis of variance, we conducted separate analyses of cognitive performance and postural activity comparing Younger and Older Adults and Non-fallers and Fallers, taking into account Vision (eyes-closed vs open) and the direction of postural movements (Anteroposterior vs Mediolateral) while also controlling for participants' characteristics. We used moderation analyses to evaluate whether relationships between Trail Making Test scores and the linear and nonlinear outcomes were moderated by Age group or Fall status., Results: For postural activity, only DFA differed between Non-fallers and Fallers. Older adults exhibited increased complexity associated with better processing speed function, while fallers show an opposite association, relying on processing speed to increase postural rigidity instead of facilitating adaptive control of balance., Conclusions: We conclude that DFA can provide information regarding postural activity and cognitive performance that cannot be obtained from more traditional, linear measures of postural activity and that DFA may be a valuable tool for assessing fall risk., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Neuro-electrophysiological recordings contain prominent aperiodic activity - meaning irregular activity, with no characteristic frequency - which has variously been referred to as 1/f (or 1/f-like activity), fractal, or 'scale-free' activity. Previous work has established that aperiodic features of neural activity is dynamic and variable, relating (between subjects) to healthy aging and to clinical diagnoses, and also (within subjects) tracking conscious states and behavioral performance. There are, however, a wide variety of conceptual frameworks and associated methods for the analyses and interpretation of aperiodic activity - for example, time domain measures such as the autocorrelation, fractal measures, and/or various complexity and entropy measures, as well as measures of the aperiodic exponent in the frequency domain. There is a lack of clear understanding of how these different measures relate to each other and to what extent they reflect the same or different properties of the data, which makes it difficult to synthesize results across approaches and complicates our overall understanding of the properties, biological significance, and demographic, clinical, and behavioral correlates of aperiodic neural activity. To address this problem, in this project we systematically survey the different approaches for measuring aperiodic neural activity, starting with an automated literature analysis to curate a collection of the most common methods. We then evaluate and compare these methods, using statistically representative time series simulations. In doing so, we establish consistent relationships between the measures, showing that much of what they capture reflects shared variance - though with some notable idiosyncrasies. Broadly, frequency domain methods are more specific to aperiodic features of the data, whereas time domain measures are more impacted by oscillatory activity. We extend this analysis by applying the measures to a series of empirical EEG and iEEG datasets, replicating the simulation results. We conclude by summarizing the relationships between the multiple methods, emphasizing opportunities for reexamining previous findings and for future work., Competing Interests: Conflicts of Interest The authors declare no competing interests.

The short-term scaling exponent of detrended fluctuation analysis (DFAα1) applied to interbeat intervals may provide a method to identify ventilatory thresholds and indicate systemic perturbation during prolonged exercise. The purposes of this study were to (i) identify the gas exchange threshold (GET) and respiratory compensation point (RCP) using DFAα1 values of 0.75 and 0.5 from incremental exercise, (ii) compare DFAα1 thresholds with DFAα1 measures during constant-speed running near the maximal lactate steady state (MLSS), and (iii) assess the repeatability of DFAα1 between MLSS trials. Twelve runners performed an incremental running test and constant-speed running 5% below, at, and 5% above the MLSS, plus a repeat trial at MLSS. During 30-min running trials near MLSS, DFAα1 responses were variable (i.e., 0.27-1.24) and affected by intensity (p = 0.031) and duration (p = 0.003). No difference in DFAα1 was detected between MLSS trials (p = 0.597). In the early phase (~ 8 min), DFAα1 measures at MLSS (0.71 [0.13]) remained higher than the DFAα1 identified at RCP from the incremental test (0.57 [0.13]; p = 0.024). In addition, following ~ 18 min of constant speed running at MLSS, DFAα1 measures (0.64 [0.14]) remained higher than 0.5 (p = 0.011)-the value thought to demarcate the boundaries between heavy and severe exercise intensities. Accordingly, using fixed DFAα1 values associated with the RCP from incremental exercise to guide constant-speed exercise training may produce a greater than expected exercise intensity, however; the dependency of DFAα1 on intensity and duration suggest its potential utility to quantify systemic perturbations imposed by continuous exercise., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Conventional measures of heart rate variability (HRV) have shown only modest associations with sudden cardiac death (SCD). Detrended fluctuation analysis (DFA), with novel methodological developments to evaluate the short-term scaling exponent, is a potentially superior method compared to conventional HRV tools., Objectives: In this study, the authors studied the analysis of the association between DFA and SCD., Methods: The investigators studied the predictive value of ultra-short-term heart rate fluctuations (1-minute electrocardiogram samples) with DFA at rest and during different stages of physical exertion for incident SCD among 2,794 participants undergoing clinical exercise testing in the prospective FINCAVAS (Finnish Cardiovascular Study). The novel key DFA measure, the short-scale scaling exponent computed with second-order detrending (DFA2 α 1 ), was the main exposure variable. SCDs were defined by American Heart Association/European Society of Cardiology criteria using death certificates with written accounts of the events., Results: During a median follow-up of 8.3 years (Q1-Q3: 6.4-10.5), 83 SCDs occurred. DFA2 α 1 measured at rest (but not in exercise) associated highly significantly with the risk of SCD, with 1-SD lower values associating with a 2.4-fold (Q1-Q3: 2.0-3.0) risk (P < 0.001). The results persisted when adjusting for other major risk factors for SCD, including age, cardiovascular morbidities, cardiorespiratory fitness, heart rate reduction, and left ventricular ejection fraction. Associations between conventional HRV parameters (measured at any stage of exercise or at rest) and SCD were substantially weaker and statistically nonsignificant after adjusting for other risk factors., Conclusions: Ultra-short-term DFA2 α 1 , when measured at rest, is a powerful and independent predictor of SCD. The association between DFA2 α 1 and SCD is modified by physical exertion., Competing Interests: Funding Support and Author Disclosures FINCAVAS has been financially supported by the Competitive Research Funding of Tampere University Hospital (grants 9M048 and 9N035); the Finnish Cultural Foundation; the Finnish Foundation for Cardiovascular Research (to T.L); the Emil Aaltonen Foundation; the Tampere Tuberculosis Foundation, EU Horizon 2020 (grants 755320 for TAXINOMISIS and 848146 for To Aition); and the Academy of Finland (grant 322098). Additional funding has been also granted by Business Finland, R2B Funding 2022-23 (to E.R). The authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Integrating running gait coordination assessment into athlete monitoring systems could provide unique insight into training tolerance and fatigue-related gait alterations. This study investigated the impact of an overload training intervention and recovery on running gait coordination assessed by field-based self-testing. Fifteen trained distance runners were recruited to perform 1-week of light training (baseline), 2 weeks of heavy training (high intensity, duration, and frequency) designed to overload participants, and a 10-day light taper to allow recovery and adaptation. Field-based running assessments using ankle accelerometry and online short recovery and stress scale (SRSS) surveys were completed daily. Running performance was assessed after each training phase using a maximal effort multi-stage running test-to-exhaustion (RTE). Gait coordination was assessed using detrended fluctuation analysis (DFA) of a stride interval time series. Two participants withdrew during baseline training due to changed personal circumstances. Four participants withdrew during heavy training due to injury. The remaining nine participants completed heavy training and were included in the final analysis. Heavy training reduced DFA values (standardised mean difference (SMD) = -1.44 ± 0.90; p = 0.004), recovery (SMD = -1.83 ± 0.82; p less than 0.001), performance (SMD = -0.36 ± 0.32; p = 0.03), and increased stress (SMD = 1.78 ± 0.94; p = 0.001) compared to baseline. DFA values ( p = 0.73), recovery ( p = 0.77), and stress ( p = 0.73) returned to baseline levels after tapering while performance trended towards improvement from baseline (SMD = 0.28 ± 0.37; p = 0.13). Reduced DFA values were associated with reduced performance (r 2 = 0.55) and recovery (r 2 = 0.55) and increased stress (r 2 = 0.62). Field-based testing of running gait coordination is a promising method of monitoring training tolerance in running athletes during overload training.

Understanding the complex dynamics of heart rate variability (HRV) during pregnancy is crucial for monitoring both maternal well-being and fetal health. In this study, we use the Multifractal Detrended Fluctuations Analysis approach to investigate HRV patterns in pregnant individuals during sleep based on RR interval maxima (MM fluctuations). In addition, we study the type of multifractality within MM fluctuations, that is, if it arises from a broad probability density function or from varying long-range correlations. Furthermore, to provide a comprehensive view of HRV changes during sleep in pregnancy, classical temporal and spectral HRV indices were calculated at quarterly intervals during sleep. Our study population consists of 21 recordings from nonpregnant women, 18 from the first trimester (early-pregnancy) and 18 from the second trimester (middle-pregnancy) of pregnancy. Results. There are statistically significant differences ( p -value < 0.05) in mean heart rate, rms heart rate, mean MM fluctuations, and standard deviation of MM fluctuations, particularly in the third and fourth quarter of sleep between pregnant and non-pregnant states. In addition, the early-pregnancy group shows significant differences ( p -value < 0.05) in spectral indices during the first and fourth quarter of sleep compared to the non-pregnancy group. Furthermore, the results of our research show striking similarities in the average multifractal structure of MM fluctuations between pregnant and non-pregnant states during normal sleep. These results highlight the influence of different long-range correlations within the MM fluctuations, which could be primarily associated with the emergence of sleep cycles on multifractality during sleep. Finally, we performed a separability analysis between groups using temporal and spectral HRV indices as features per sleep quarter. Employing only three features after Principal Component Analysis (PCA) to the original feature set, achieving complete separability among all groups appears feasible. Using multifractal analysis, our study provides a comprehensive understanding of the complex HRV patterns during pregnancy, which holds promise for maternal and fetal health monitoring. The separability analysis also provides valuable insights into the potential for group differentiation using simple measures such as mean heart rate, rms heart rate, and mean MM fluctuations or in the transformed feature space based on PCA., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Mendez, Bianchi, Recker, Strizek, Murguía, Reali and Jimenez-Cruz.)

Accelerometers, devices that measure body movements, have become valuable tools for studying the fragmentation of rest-activity patterns, a core circadian rhythm dimension, using metrics such as inter-daily stability (IS), intradaily variability (IV), transition probability (TP), and self-similarity parameter (named α ). However, their use remains mainly empirical. Therefore, we investigated the mathematical properties and interpretability of rest-activity fragmentation metrics by providing mathematical proofs for the ranges of IS and IV, proposing maximum likelihood and Bayesian estimators for TP, introducing the activity balance index (ABI) metric, a transformation of α , and describing distributions of these metrics in real-life setting. Analysis of accelerometer data from 2,859 individuals (age=60-83 years, 21.1% women) from the Whitehall II cohort (UK) shows modest correlations between the metrics, except for ABI and α . Sociodemographic (age, sex, education, employment status) and clinical (body mass index (BMI), and number of morbidities) factors were associated with these metrics, with differences observed according to metrics. For example, a difference of 5 units in BMI was associated with all metrics (differences ranging between -0.261 (95% CI -0.302, -0.220) to 0.228 (0.18, 0.268) for standardised TP rest to activity during the awake period and TP activity to rest during the awake period, respectively). These results reinforce the value of these rest-activity fragmentation metrics in epidemiological and clinical studies to examine their role for health. This paper expands on a set of methods that have previously demonstrated empirical value, improves the theoretical foundation for these methods, and evaluates their empirical use in a large dataset., (© 2024. The Author(s).)

Introduction: Sleep-wake cycle disruption caused by shift work may lead to cardiovascular stress, which is observed as an alteration in the behavior of heart rate variability (HRV). In particular, HRV exhibits complex patterns over different time scales that help to understand the regulatory mechanisms of the autonomic nervous system, and changes in the fractality of HRV may be associated with pathological conditions, including cardiovascular disease, diabetes, or even psychological stress. The main purpose of this study is to evaluate the multifractal-multiscale structure of HRV during sleep in healthy shift and non-shift workers to identify conditions of cardiovascular stress that may be associated with shift work., Methods: The whole-sleep HRV signal was analyzed from female participants: eleven healthy shift workers and seven non-shift workers. The HRV signal was decomposed into intrinsic mode functions (IMFs) using the empirical mode decomposition method, and then the IMFs were analyzed using the multiscale-multifractal detrended fluctuation analysis (MMF-DFA) method. The MMF-DFA was applied to estimate the self-similarity coefficients, α( q , τ), considering moment orders ( q ) between -5 and +5 and scales (τ) between 8 and 2,048 s. Additionally, to describe the multifractality at each τ in a simple way, a multifractal index, MFI (τ), was computed., Results: Compared to non-shift workers, shift workers presented an increase in the scaling exponent, α( q , τ), at short scales (τ < 64 s) with q < 0 in the high-frequency component (IMF1, 0.15-0.4 Hz) and low-frequency components (IMF2-IMF3, 0.04-0.15 Hz), and with q > 0 in the very low frequencies (IMF4, < 0.04 Hz). In addition, at large scales (τ> 1,024 s), a decrease in α( q , τ) was observed in IMF3, suggesting an alteration in the multifractal dynamic. MFI (τ) showed an increase at small scales and a decrease at large scales in IMFs of shift workers., Conclusion: This study helps to recognize the multifractality of HRV during sleep, beyond simply looking at indices based on means and variances. This analysis helps to identify that shift workers show alterations in fractal properties, mainly on short scales. These findings suggest a disturbance in the autonomic nervous system induced by the cardiovascular stress of shift work., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Delgado-Aranda, Dorantes-Méndez, Bianchi, Kortelainen, Coelli, Jimenez-Cruz and Méndez.)

Riemannian geometry-based classification (RGBC) gained popularity in the field of brain-computer interfaces (BCIs) lately, due to its ability to deal with non-stationarities arising in electroencephalography (EEG) data. Domain adaptation, however, is most often performed on sample covariance matrices (SCMs) obtained from EEG data, and thus might not fully account for components affecting covariance estimation itself, such as regional trends. Detrended cross-correlation analysis (DCCA) can be utilized to estimate the covariance structure of such signals, yet it is computationally expensive in its original form. A recently proposed online implementation of DCCA, however, allows for its fast computation and thus makes it possible to employ DCCA in real-time applications. In this study we propose to replace the SCM with the DCCA matrix as input to RGBC and assess its effect on offline and online BCI performance. First we evaluated the proposed decoding pipeline offline on previously recorded EEG data from 18 individuals performing left and right hand motor imagery (MI), and benchmarked it against vanilla RGBC and popular MI-detection approaches. Subsequently, we recruited eight participants (with previous BCI experience) who operated an MI-based BCI (MI-BCI) online using the DCCA-enhanced Riemannian decoder. Finally, we tested the proposed method on a public, multi-class MI-BCI dataset. During offline evaluations the DCCA-based decoder consistently and significantly outperformed the other approaches. Online evaluation confirmed that the DCCA matrix could be computed in real-time even for 22-channel EEG, as well as subjects could control the MI-BCI with high command delivery (normalized Cohen's κ: 0.7409 ± 0.1515) and sample-wise MI detection (normalized Cohen's κ: 0.5200 ± 0.1610). Post-hoc analysis indicated characteristic connectivity patterns under both MI conditions, with stronger connectivity in the hemisphere contralateral to the MI task. Additionally, fractal scaling exponent of neural activity was found increased in the contralateral compared to the ipsilateral motor cortices (C4 and C3 for left and right MI, respectively) in both classes. Combining DCCA with Riemannian geometry-based decoding yields a robust and effective decoder, that not only improves upon the SCM-based approach but can also provide relevant information on the neurophysiological processes behind MI., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Racz, Kumar, Kaposzta, Alawieh, Liu, Liu, Czoch, Mukli and Millán.)

Introduction: Mental disorders are a significant concern in contemporary society, with a pressing need to identify biological markers. Long-range temporal correlations (LRTC) of brain rhythms have been widespread in clinical cohort studies, especially in major depressive disorder (MDD). However, research on LRTC in obsessive-compulsive disorder (OCD) is severely limited. Given the high co-occurrence of OCD and MDD, we conducted a comparative LRTC investigation. We assumed that the LRTC patterns will allow us to compare measures of brain cortical balance of excitation and inhibition in OCD and MDD, which will be useful in the area of differential diagnosis., Methods: In this study, we used the 64-channel resting state EEG of 29 MDD participants, 26 OCD participants, and a control group of 37 volunteers. Detrended fluctuation analyzes was used to assess LRTC., Results: Our results indicate that all scaling exponents of the three subject groups exhibited persistent LRTC of EEG oscillations. There was a tendency for LRTC to be higher in disorders than in controls, but statistically significant differences were found between the OCD and control groups in the entire frontal and left parietal occipital areas, and between the MDD and OCD groups in the middle and right frontal areas., Discussion: We believe that these results indicate abnormalities in the inhibitory and excitatory neurotransmitter systems, predominantly affecting areas related to executive functions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Proshina, Martynova, Portnova, Khayrullina and Sysoeva.)

Continuous adaptations of the movement system to changing environments or task demands rely on superposed fractal processes exhibiting power laws, that is, multifractality. The estimators of the multifractal spectrum potentially reflect the adaptive use of perception, cognition, and action. To observe time-specific behavior in multifractal dynamics, a multiscale multifractal analysis based on DFA (MFMS-DFA) has been recently proposed and applied to cardiovascular dynamics. Here we aimed at evaluating whether MFMS-DFA allows identifying multiscale structures in the dynamics of human movements. Thirty-six (12 females) participants pedaled freely, after a metronomic initiation of the cadence at 60 rpm, against a light workload for 10 min: in reference to cycling (C), cycling while playing "Tetris" on a computer, alone (CT) or collaboratively (CTC) with another pedaling participant. Pedal revolution periods (PRP) series were examined with MFMS-DFA and compared to linearized surrogates, which attested to a presence of multifractality at almost all scales. A marked alteration in multifractality when playing Tetris was evidenced at two scales, τ ≈ 16 and τ ≈ 64 s, yet less marked at τ ≈ 16 s when playing collaboratively. Playing Tetris in collaboration attenuated these alterations, especially in the best Tetris players. This observation suggests the high sensitivity to cognitive demand of MFMS-DFA estimators, extending to the assessment of skill/demand interplay from individual behavior. So, by identifying scale-dependent multifractal structures in movement dynamics, MFMS-DFA has obvious potential for examining brain-movement coordinative structures, likely with sufficient sensitivity to find echo in diagnosing disorders and monitoring the progress of diseases that affect cognition and movement control.

Reciprocating compressors and centrifugal pumps are rotating machines used in industry, where fault detection is crucial for avoiding unnecessary and costly downtime. A novel method for fault classification in reciprocating compressors and multi-stage centrifugal pumps is proposed. In the feature extraction stage, raw vibration signals are processed using multi-fractal detrended fluctuation analysis (MFDFA) to extract features indicative of different types of faults. Such MFDFA features enable the training of machine learning models for classifying faults. Several classical machine learning models and a deep learning model corresponding to the convolutional neural network (CNN) are compared with respect to their classification accuracy. The cross-validation results show that all models are highly accurate for classifying the 13 types of faults in the centrifugal pump, the 17 valve faults, and the 13 multi-faults in the reciprocating compressor. The random forest subspace discriminant (RFSD) and the CNN model achieved the best results using MFDFA features calculated with quadratic approximations. The proposed method is a promising approach for fault classification in reciprocating compressors and multi-stage centrifugal pumps.

Running synchronised to external cueing is often implemented in both clinical and training settings, and isochronous cueing has been shown to improve running economy. However, such cueing disregards the natural stride-to-stride fluctuations present in human locomotion which is thought to reflect higher levels of adaptability. The present study aimed to investigate how alterations in the temporal structure of cueing affect stride-to-stride variability during running. We hypothesised that running using cueing with a fractal-like structure would preserve the natural stride-to-stride variability of young adults. Thirteen runners performed four 8-min trials: one uncued (UNC) trial and three cued trials presenting an isochronous (ISO), a fractal (FRC) and a random (RND) structure. Repeated measures ANOVAs were used to identify changes in the dependent variables. We have found no main effect on the cardiorespiratory parameters, whereas a significant main effect was observed in the temporal structure of stride-to-stride variability. During FRC, the participants were able to retain the fractal patterns of their natural locomotor variability observed during the UNC condition, while during the ISO and RND they exhibited more random of fluctuations (i.e., lower values of fractal scaling). Our results demonstrate that cueing based on the natural stride-to-stride fluctuations opens new avenues for training and rehabilitation.

Several natural phenomena can be described by studying their statistical scaling patterns, hence leading to simple geometrical interpretation. In this regard, fractal geometry is a powerful tool to describe the irregular or fragmented shape of natural features, using spatial or time-domain statistical scaling laws (power-law behavior) to characterize real-world physical systems. This chapter presents some works on the usefulness of fractal features, mainly the fractal dimension and the related Hurst exponent, in the characterization and identification of pathologies and radiological features in neuroimaging, mainly, magnetic resonance imaging., (© 2024. The Author(s), under exclusive license to Springer Nature Switzerland AG.)

Over the past 40 years, from its classical application in the characterization of geometrical objects, fractal analysis has been progressively applied to study time series in several different disciplines. In neuroscience, starting from identifying the fractal properties of neuronal and brain architecture, attention has shifted to evaluating brain signals in the time domain. Classical linear methods applied to analyzing neurophysiological signals can lead to classifying irregular components as noise, with a potential loss of information. Thus, characterizing fractal properties, namely, self-similarity, scale invariance, and fractal dimension (FD), can provide relevant information on these signals in physiological and pathological conditions. Several methods have been proposed to estimate the fractal properties of these neurophysiological signals. However, the effects of signal characteristics (e.g., its stationarity) and other signal parameters, such as sampling frequency, amplitude, and noise level, have partially been tested. In this chapter, we first outline the main properties of fractals in the domain of space (fractal geometry) and time (fractal time series). Then, after providing an overview of the available methods to estimate the FD, we test them on synthetic time series (STS) with different sampling frequencies, signal amplitudes, and noise levels. Finally, we describe and discuss the performances of each method and the effect of signal parameters on the accuracy of FD estimation., (© 2024. The Author(s), under exclusive license to Springer Nature Switzerland AG.)

Aerobic and anaerobic thresholds of the three-zone exercise model are often used to evaluate the exercise intensity and optimize the training load. Conventionally, these thresholds are derived from the respiratory gas exchange or blood lactate concentration measurements. Here, we introduce and validate a computational method based on the RR interval (RRI) dynamics of the heart rate (HR) measurement, which enables a simple, yet reasonably accurate estimation of both metabolic thresholds. The method utilizes a newly developed dynamical detrended fluctuation analysis (DDFA) to assess the real-time changes in the dynamical correlations of the RR intervals during exercise. The training intensity is shown to be in direct correspondence with the time- and scale-dependent changes in the DDFA scaling exponent. These changes are further used in the definition of an individual measure to estimate the aerobic and anaerobic threshold. The results for 15 volunteers who participated in a cyclo-ergometer test are compared to the benchmark lactate thresholds, as well as to the ventilatory threshods and alternative HR-based estimates based on the maximal HR and the conventional detrended fluctuation analysis (DFA). Our method provides the best overall agreement with the lactate thresholds and provides a promising, cost-effective alternative to conventional protocols, which could be easily integrated in wearable devices. However, detailed statistical analysis reveals the particular strengths and weaknessess of each method with respect to the agreement and consistency with the thresholds-thus underlining the need for further studies with more data., Competing Interests: KL is the founder and the CEO of company Kauppi Sports Coaching Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Kanniainen, Pukkila, Kuisma, Molkkari, Lajunen and Räsänen.)

The short-term scaling exponent of detrended fluctuation analysis (DFA-a1) of heart rate variability may be a helpful tool to assess autonomic balance as a prelude to daily, individualized training. For this concept to be useful, between-session reliability should be acceptable. The aim of this study was to explore the reliability of DFA-a1 during a low-intensity exercise session in both a non-fatigued and a fatigued condition in healthy males and females. Ten participants completed two sessions with each containing an exhaustive treadmill ramp protocol. Before and after the fatiguing ramp, a standardized submaximal low-intensity exercise bout was performed during which DFA-a1, heart rate, and oxygen consumption (VO 2 ) were measured. We compared between-session reliability of all metrics prior to the ramps (i.e., non-fatigued status) and after the first ramp (i.e., fatigued status). Intraclass correlation coefficients (ICC) with 95% confidence intervals (CI), the standard error of measurement, and the smallest worthwhile change (SWC) were determined. The ICC and SWC pre fatiguing ramp were 0.85 (95% CI 0.39-0.96) and 5.5% for DFA-a1, 0.85 (0.38-0.96) and 2.2% for heart rate, and 0.84 (0.31-0.96) and 3.1% for VO 2 . Post fatiguing ramp, the ICC and SWC were 0.55 (0.00-0.89) and 7.9% for DFA-a1, 0.91 (0.62-0.98) and 1.6% for heart rate, and 0.80 (0.17-0.95) and 3.0% for VO 2 . DFA-a1 shows generally acceptable to good between-session reliability with a SWC of 0.06 and 0.07 (5.5-7.9%) during non-fatigued and fatigued conditions. This suggests that this metric may be useful to inform on training readiness., (© 2023. The Author(s).)

Background: Fatigue is associated with increased injury risk along with changes in balance control and task performance. Musculoskeletal injury rates in runners are high and often result from an inability to adapt to the demands of exercise and a breakdown in the interaction among different biological systems. This study aimed to investigate whether changes in balance dynamics during a single-leg squat task following a high-intensity run could distinguish groups of recreational runners who did and did not sustain a running-related injury within 6 months. Methods: Thirty-one healthy recreational runners completed 60 s of single-leg squat before and after a high-intensity run. Six months after the assessment, this cohort was separated into two groups of 13 matched individuals with one group reporting injury within this period and the other not. Task performance was assessed by the number of repetitions, cycle time, amplitude, and speed. To evaluate balance dynamics, the regularity and temporal correlation structure of the center of mass (CoM) displacements in the transverse plane was analyzed. The interaction between groups (injury, non-injured) and time (pre, post) was assessed through a two-way ANOVA. Additionally, a one-way ANOVA investigated the percent change difference of each group across time. Results: The injured group presented more regular (reduced entropy; 15.6%) and diffusive (increased short-term persistence correlation; 5.6%) CoM displacements after a high-intensity run. No changes were observed in the non-injured group. The within-subject percent change was more sensitive in demonstrating the effects of fatigue and distinguishing the groups, compared to group absolute values. No differences were observed in task performance. Discussion: Runners who were injured in the future demonstrate changes in balance dynamics compared to runners who remain injury-free after fatigue. The single-leg squat test adopted appears to be a potential screening protocol that provides valuable information about balance dynamics for identifying a diminished ability to respond to training and exercise., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Aquino, Liddy, Napoli, Fonseca, van Emmerik and Busa.)

Purpose: The short-term scaling exponent alpha1 of detrended fluctuation analysis (DFA-a1) of heart rate variability (HRV) has shown potential to delineate the first ventilatory threshold (VT1). The aims of this study were to investigate the accuracy of this method for VT1 determination in runners using a consumer grade chest belt and to explore the effects of acute fatigue., Methods: We compared oxygen uptake (V̇O 2 ) and heart rate (HR) at gas exchange VT1 to V̇O 2 and HR at a DFA-a1 value of 0.75. Gas exchange and HRV data were obtained from 14 individuals during a treadmill run involving two incremental ramps. Agreement was assessed using Bland-Altman analysis and linear regression., Results: Bland-Altman analysis between gas exchange and HRV V̇O 2 and HR at VT1 during the first ramp showed a mean (95% limits of agreement) bias of -0.5 (-6.8 to 5.8) ml∙kg -1 ∙min -1 , and -0.9 (-12.2 to 10.5) beats∙min -1 , with R 2 of 0.83 and 0.56, respectively. During the second ramp, the differences were -7.3 (-18.1 to 3.5) ml∙kg -1 ∙min -1 and -12.3 (-30.4 to 5.9) beats∙min -1 , with R 2 of 0.62 and 0.43, respectively., Conclusion: A chest-belt derived DFA-a1 of 0.75 is closely related to gas exchange VT1, with the variability in accuracy at an individual level being similar to gas exchange methods. This suggests this to be a useful method for exercise intensity demarcation. The altered relationship during the second ramp indicates that DFA-a1 is only able to accurately demarcate exercise intensity thresholds in a non-fatigued state, but also opens opportunities for fatigue-based training prescription.

The kinetics of an ion channel are classically understood as a random process. However, studies have shown that in complex ion channels, formed by multiple subunits, this process can be deterministic, presenting long-term memory. Staphylococcus aureus α-hemolysin (α-HL) is a toxin that acts as the major factor in Staphylococcus aureus virulence. α-HL is a water-soluble protein capable of forming ion channels into lipid bilayers, by insertion of an amphipathic  β-barrel. Here, the α-HL was used as an experimental model to study memory in ion channel kinetics. We applied the approximate entropy (ApEn) approach to analyze randomness and the Detrended Fluctuation Analysis (DFA) to investigate the existence of long memory in α-HL channel kinetics. Single-channel currents were measured through experiments with α-HL channels incorporated in planar lipid bilayers. All experiments were carried out under the following conditions: 1 M NaCl solution, pH 4.5; transmembrane potential of + 40 mV and temperature 25 ± 1 °C. Single-channel currents were recorded in real-time in the memory of a microcomputer coupled to an A/D converter and a patch-clamp amplifier. The conductance value of the α-HL channels was 0.82 ± 0.0025 nS (n = 128). The DFA analysis showed that the kinetics of α-HL channels presents long-term memory ([Formula: see text] = 0.63 ± 0.04). The ApEn outcomes showed low complexity to dwell times when open (ApEn o  = 0.5514 ± 0.28) and closed (ApEn c  = 0.1145 ± 0.08), corroborating the results of the DFA method., (© 2023. European Biophysical Societies' Association.)

Temporal fluctuations in the stride parameters during human walking exhibit long-range correlations, but these long-range correlations in the stride parameters decrease due to aging or neuromuscular diseases. These observations suggest that any quantified index of the long-range correlation can be regarded as an indicator of gait functionality. Considering the effect of task-relevant sensory feedback on augmenting human motor performance, we devised shoes with active insoles that could deliver noisy vibration to the soles of feet and assessed their efficacy in enhancing the long-range correlations in the stride parameters for healthy young adults. The vibration could be wirelessly controlled using a smartphone. The actuators, control unit, and battery in the devised shoes were light and embedded in the shoes. By virtue of this compactness, the shoes could be easily used for daily walking outside a laboratory. We performed walking experiments with 20 healthy adults and evaluated the effects of sub- and supra-threshold vibration on long-range correlations in stride interval and length. We performed detrended fluctuation analysis to quantify the long-range correlation of temporal changes in stride interval and length. We found that supra-threshold vibration, applied to the soles with the amplitude of 130 % of the sensory threshold, significantly increased the long-range correlations in stride interval and length by 10.3 % (p = 0.009) and 10.1 % (p = 0.021), respectively. On the other hand, sub-threshold vibration with the amplitude of 90 % of the sensory threshold had no significant effect. These results demonstrate that additional somatosensory feedback through barely detectable vibrations, which are supplied by compact shoes with active insoles, can enhance the indices of "healthy" complexity of locomotor function., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Jooeun Ahn reports financial support was provided by Korea Ministry of Health and Welfare. Jooeun Ahn reports financial support was provided by Korea Ministry of Trade Industry and Energy. Jooeun Ahn reports financial support was provided by 10.13039/501100003725National Research Foundation of Korea. Jooeun Ahn has patent #US 11647806 B2 issued to SNU R&DB Foundation and Samsung Electronics. Jooeun Ahn has patent #EP3797626 issued to SNU R&DB Foundation and Samsung Electronics. Prabhat Pathak has patent #US 11647806 B2 issued to SNU R&DB Foundation and Samsung Electronics. Prabhat Pathak has patent #EP3797626 issued to SNU R&DB Foundation and Samsung Electronics. The active insole shoes used in this study were developed by Samsung Electronics, and these shoes might be commercialized in the future. However, Samsung Electronics had no role in the study design, in the collection, analysis and interpretation of data; in the writing of the manuscript; and in the decision to submit the manuscript for publication. These facts do not alter our adherence to Heliyon policies on sharing data and materials., (© 2023 The Authors. Published by Elsevier Ltd.)

Fractal fluctuations are a core concept for inquiries into human behavior and cognition from a dynamic systems perspective. Here, we present a generalized variance method for multivariate detrended fluctuation analysis (mvDFA). The advantage of this extension is that it can be applied to multivariate time series and considers intercorrelation between these time series when estimating fractal properties. First, we briefly describe how fractal fluctuations have advanced a dynamic system understanding of cognition. Then, we describe mvDFA in detail and highlight some of the advantages of the approach for simulated data. Furthermore, we show how mvDFA can be used to investigate empirical multivariate data using electroencephalographic recordings during a time-estimation task. We discuss this methodological development within the framework of interaction-dominant dynamics. Moreover, we outline how the availability of multivariate analyses can inform theoretical developments in the area of dynamic systems in human behavior., (© 2023 The Authors. Topics in Cognitive Science published by Wiley Periodicals LLC on behalf of Cognitive Science Society.)

Background: Pulmonary vein isolation (PVI) is a cornerstone therapy for paroxysmal atrial fibrillation (PAF). The variations in nonlinear heart rate variability (HRV) between patients with and without recurrences remain unclear. We aimed to characterize the nonlinear HRV before and after PVI in patients with and without recurrence., Methods: Twenty-five drug-refractory PAF patients (56.0 ± 9.1 years old, 20 males) who received PVI were enrolled. Holter electrocardiography were performed before, 1-3, and 6-12 months after PVI. After 8.2 ± 2.5 months of follow-ups after PVI, patients were divided into two groups: the recurrence (n = 8) and non-recurrence (n = 17) groups. Linear and nonlinear HRV variables were analyzed, including the Poincaré Plot analysis and the Detrended Fluctuation Analysis (DFA)., Results: The non-recurrence group, but not the recurrence group, had decreased high-frequency component (HF), the root mean square of successive RR interval differences (RMSSD), and the Poincaré Plot index SD1 1-3 months after PVI and increased DFA slope2 6-12 months after PVI. The non-recurrence group's LF/HF ratio and DFA slope1 decreased significantly 1-3 and 6-12 months after PVI, respectively, whereas there was no significant change in the recurrence group after PVI., Conclusions: Significantly reduced vagal tone 1-3 months after PVI, increased long-term fractal complexity 6-12 months after PVI, and decreased sympathetic tone as well as short-term fractal complexity 1-3 and 6-12 months after PVI led to a better AF-free survival after PVI. These findings suggest that neuromodulation and heart rate dynamics play crucial roles in AF recurrence following PVI., (© 2023 The Authors. Annals of Noninvasive Electrocardiology published by Wiley Periodicals LLC.)

Objective. Brain-injured patients may enter a state of minimal or inconsistent awareness termed minimally conscious state (MCS). Such patient may (MCS+) or may not (MCS-) exhibit high-level behavioral responses, and the two groups retain two inherently different rehabilitative paths and expected outcomes. We hypothesized that brain complexity may be treated as a proxy of high-level cognition and thus could be used as a neural correlate of consciousness. Approach. In this prospective observational study, 68 MCS patients (MCS-: 30; women: 31) were included (median [IQR] age 69 [20]; time post-onset 83 [28]). At admission to intensive rehabilitation, 30 min resting-state closed-eyes recordings were performed together with consciousness diagnosis following international guidelines. The width of the multifractal singularity spectrum (MSS) was computed for each channel time series and entered nested cross-validated interpretable machine learning models targeting the differential diagnosis of MCS±. Main results. Frontal MSS widths ( p < 0.05), as well as the ones deriving from the left centro-temporal network (C3: p = 0.018, T3: p = 0.017; T5: p = 0.003) were found to be significantly higher in the MCS+ cohort. The best performing solution was found to be the K-nearest neighbor model with an aggregated test accuracy of 75.5% (median [IQR] AuROC for 100 executions 0.88 [0.02]). Coherently, the electrodes with highest Shapley values were found to be Fz and Cz, with four out the first five ranked features belonging to the fronto-central network. Significance. MCS+ is a frequent condition associated with a notably better prognosis than the MCS-. High fractality in the left centro-temporal network results coherent with neurological networks involved in the language function, proper of MCS+ patients. Using EEG-based interpretable algorithm to complement differential diagnosis of consciousness may improve rehabilitation pathways and communications with caregivers., (© 2023 IOP Publishing Ltd.)

Accurate sleep stage detection is crucial for diagnosing sleep disorders and tailoring treatment plans. Polysomnography (PSG) is considered the gold standard for sleep assessment since it captures a diverse set of physiological signals. While various studies have employed complex neural networks for sleep staging using PSG, our research emphasises the efficacy of a simpler and more efficient architecture. We aimed to integrate a diverse set of feature extraction measures with straightforward machine learning, potentially offering a more efficient avenue for sleep staging. We also aimed to conduct a comprehensive comparative analysis of feature extraction measures, including the power spectral density, Higuchi fractal dimension, singular value decomposition entropy, permutation entropy, and detrended fluctuation analysis, coupled with several machine-learning models, including XGBoost, Extra Trees, Random Forest, and LightGBM. Furthermore, data augmentation methods like the Synthetic Minority Oversampling Technique were also employed to rectify the inherent class imbalance in sleep data. The subsequent results highlighted that the XGBoost classifier, when used with a combination of all feature extraction measures as an ensemble, achieved the highest performance, with accuracies of 87%, 90%, 93%, 96%, and 97% and average F1-scores of 84.6%, 89%, 90.33%, 93.5%, and 93.5% for distinguishing between five-stage, four-stage, three-stage, and two distinct two-stage sleep configurations, respectively. This combined feature extraction technique represents a novel addition to the body of research since it achieves higher performance than many recently developed deep neural networks by utilising simpler machine-learning models.

Introduction: Detrended Fluctuation Analysis (DFA) has been used to investigate self-similarity in center of pressure (CoP) time series. For fractional gaussian noise (fGn) signals, the analysis returns a scaling exponent, DFA-α, whose value characterizes the temporal correlations as persistent, random, or anti-persistent. In the study of postural control, DFA has revealed two time scaling regions, one at the short-term and one at the long-term scaling regions in the diffusion plots, suggesting different types of postural dynamics. Much attention has been given to the selection of minimum and maximum scales, but the choice of spacing (step size) between the window sizes at which the fluctuation function is evaluated may also affect the estimates of scaling exponents. The aim of this study is twofold. First, to determine whether DFA can reveal postural adjustments supporting performance of an upper limb task under variable demands. Second, to compare evenly-spaced DFA with two different step sizes, 0.5 and 1.0 in log 2 units, applied to CoP time series. Methods: We analyzed time series of anterior-posterior (AP) and medial-lateral (ML) CoP displacement from healthy participants performing a sequential upper limb task under variable demand. Results: DFA diffusion plots revealed two scaling regions in the AP and ML CoP time series. The short-term scaling region generally showed hyper-diffusive dynamics and long-term scaling revealed mildly persistent dynamics in the ML direction and random-like dynamics in the AP direction. There was a systematic tendency for higher estimates of DFA-α and lower estimates for crossover points for the 0.5-unit step size vs. 1.0-unit size. Discussion: Results provide evidence that DFA-α captures task-related differences between postural adjustments in the AP and ML directions. Results also showed that DFA-α estimates and crossover points are sensitive to step size. A step size of 0.5 led to less variable DFA-α for the long-term scaling region, higher estimation for the short-term scaling region, lower estimate for crossover points, and revealed anomalous estimates at the very short range that had implications for choice of minimum window size. We, therefore, recommend the use of 0.5 step size in evenly spaced DFAs for CoP time series similar to ours., Competing Interests: The author PS declared that they were an editorial board member of Frontiers at the time of submission. This had no impact on the peer review process and the final decision. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Nordbeck, Andrade, Silva and Kuznetsov.)

Introduction: Aging is a physiological process characterized by progressive changes in all organ systems. In the last few decades, the elderly population has been growing, so the scientific community is focusing on the investigation of the aging process, all in order to improve the quality of life in elderly. One of the biggest challenges in studying the impact of the aging on the human body represents the monitoring of the changes that inevitably occur in arterial blood vessels. Therefore, the medical community has invested a great deal of effort in studying and discovering new methods and tools that could be used to monitor the changes in arterial blood vessels caused by the aging process. The goal of our research was to develop a new diagnostic method using a photoplethysmographic sensor and to examine the impact of the aging process on the cardiovascular system in adults. Long-term recorded arterial blood flow waveforms were analyzed using detrended fluctuation analysis. Materials and Methods: The study included 117 respondents, aged 20-70 years. The waveform of the arterial blood flow was recorded for 5 min, with an optical sensor placed above the left common carotid artery, simultaneously with a single-channel ECG. For each cardiac cycle, the blood flow amplitude was determined, and a new time series was formed, which was analyzed non-linearly (DFA method). The values of the scalar coefficients α 1 and α 2 , particularly their ratio ( α 1 / α 2 ) were obtained, which were then monitored in relation to the age of the subjects. Result: The values of the scalar ratio ( α 1 / α 2 ) were significantly different between the subjects older and younger than 50 years. The value of the α 1 / α 2 decreased exponentially with the aging. In the population of middle-aged adults, this ratio had a value around 1, in young adults the value was exclusively higher than 1 and in older adults the value was exclusively lower than 1. Conclusion: The results of this study indicated that the aging led to a decrease in the α 1 / α 2 in the population of healthy subjects. With this non-invasive method, changes in the cardiovascular system due to aging can be detected and monitored., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Djurić, Žikić, Nestorović, Lepojević-Stefanović, Milošević and Žikić.)

Background: People with depression typically exhibit diminished cognitive control. Control is subjectively costly, prompting speculation that control deficits reflect reduced cognitive effort. Evidence that people with depression exert less cognitive effort is mixed, however, and motivation may depend on state affect., Methods: We used a cognitive effort discounting task to measure propensity to expend cognitive effort and fractal structure in the temporal dynamics of interbeat intervals to assess on-task effort exertion for 49 healthy control subjects, 36 people with current depression, and 67 people with remitted depression., Results: People with depression discounted more steeply, indicating that they were less willing to exert cognitive effort than people with remitted depression and never-depressed control subjects. Also, steeper discounting predicted worse functioning in daily life. Surprisingly, a sad mood induction selectively boosted motivation among participants with depression, erasing differences between them and control subjects. During task performance, depressed participants with the lowest cognitive motivation showed blunted autonomic reactivity as a function of load., Conclusions: Discounting patterns supported the hypothesis that people with current depression would be less willing to exert cognitive effort, and steeper discounting predicted lower global functioning in daily life. Heart rate fractal scaling proved to be a highly sensitive index of cognitive load, and data implied that people with lower motivation for cognitive effort had a diminished physiological capacity to respond to rising cognitive demands. State affect appeared to influence motivation among people with current depression given that they were more willing to exert cognitive effort following a sad mood induction., (Copyright © 2022 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Introduction: Detrended fluctuation analysis (DFA) is a well-established method to evaluate scaling indices of time series, which categorize the dynamics of complex systems. In the literature, DFA has been used to study the fluctuations of reaction time Y(n) time series, where n is the trial number., Methods: Herein we propose treating each reaction time as a duration time that changes the representation from operational (trial number) time n to event (temporal) time t, or X(t). The DFA algorithm was then applied to the X(t) time series to evaluate scaling indices. The dataset analyzed is based on a Go-NoGo shooting task that was performed by 30 participants under low and high time-stress conditions in each of six repeated sessions over a 3-week period., Results: This new perspective leads to quantitatively better results in (1) differentiating scaling indices between low versus high time-stress conditions and (2) predicting task performance outcomes., Conclusion: We show that by changing from operational time to event time, the DFA allows discrimination of time-stress conditions and predicts performance outcomes., (© 2023 The Authors. Brain and Behavior published by Wiley Periodicals LLC.)

There are several ways to assess complexity, but no method has yet been developed for quantitatively calculating the 'loss of fractal complexity' under pathological or physiological states. In this paper, we aimed to quantitatively evaluate fractal complexity loss using a novel approach and new variables developed from Detrended Fluctuation Analysis (DFA) log-log graphics. Three study groups were established to evaluate the new approach: one for normal sinus rhythm (NSR), one for congestive heart failure (CHF), and white noise signal (WNS). ECG recordings of the NSR and CHF groups were obtained from PhysioNET Database and were used for analysis. For all groups Detrended Fluctuation Analysis scaling exponents (DFA α 1 , DFA α 2 ) were determined. Scaling exponents were used to recreate the DFA log-log graph and lines. Then, the relative total logarithmic fluctuations for each sample were identified and new parameters were computed. To do this, we used a standard log-log plane to standardize the DFA log-log curves and calculated the differences between the standardized and expected areas. We quantified the total difference in standardized areas using parameters called dS1, dS2, and TdS. Our results showed that; compared to the NSR group, DFA α 1 was lower in both CHF and WNS groups. However, DFA α 2 was only reduced in the WNSgroup and not in the CHFgroup. Newly derived parameters: dS1, dS2, and TdS were significantly lowerin the NSR group compared to the CHF and WNS groups. The new parameters derived from the DFA log-log graphs are highly distinguishing for congestive heart failure and white noise signal. In addition, it may be concluded that a potential feature of our approach can be beneficial in classifying the severity of cardiac abnormalities., (© 2023 IOP Publishing Ltd.)

Background: Heart-brain synchronization is the integration of mind, body, and spirit. It occurs when the electrical activity of the heart and brain is synchronized. In recent years, there has been mounting curiosity to investigate the effects of meditation on heart-brain synchronization with respect to mental and emotional health and well-being. The current investigation aims to explore the rhythmic synchronicity between the brain and the heart during heartfulness meditation (HM) practice., Materials and Methods: The study was performed on 45 healthy volunteers who were categorized into three equal groups: long-term meditators (LTMs), short-term meditators (STMs), and nonmeditators (NMs). The electroencephalogram (EEG) signals were recorded to measure the prefrontal activity, and electrocardiogram (ECG) signals were recorded to measure the cardiac activity. The data were recorded in four states: baseline, meditation, transmission, and posttransmission. The detrended fluctuation analysis (DFA) method was used for the analysis of EEG and ECG signals., Results: The result indicates that DFA values of EEG and ECG declined during meditation and transmission states as compared to pre- and postmeditation states. Significant results were obtained for the LTM group in all the states. A positive correlation was also observed between DFA of the heart and brain for the LTM group and no significant correlations were observed for the STM and NM groups., Conclusion: The shreds of evidence suggest that heart-brain synchronization facilitates mental and emotional stability. HM practice has the potential to regulate the fluctuation of the mind. Regular meditation practice may result in physiological synchrony between cardiac and neural behavior, which can be considered a quality index for meditation practice., Competing Interests: There are no conflicts of interest., (Copyright: © 2023 International Journal of Yoga.)

In this paper, we thoroughly analyze the detection of sleep apnea events in the context of Obstructive Sleep Apnea (OSA), which is considered a public health problem because of its high prevalence and serious health implications. We especially evaluate patients who do not always show desaturations during apneic episodes (non-desaturating patients). For this purpose, we use a database (HuGCDN2014-OXI) that includes desaturating and non-desaturating patients, and we use the widely used Physionet Apnea Dataset for a meaningful comparison with prior work. Our system combines features extracted from the Heart-Rate Variability (HRV) and SpO 2 , and it explores their potential to characterize desaturating and non-desaturating events. The HRV-based features include spectral, cepstral, and nonlinear information (Detrended Fluctuation Analysis (DFA) and Recurrence Quantification Analysis (RQA)). SpO 2 -based features include temporal (variance) and spectral information. The features feed a Linear Discriminant Analysis (LDA) classifier. The goal is to evaluate the effect of using these features either individually or in combination, especially in non-desaturating patients. The main results for the detection of apneic events are: (a) Physionet success rate of 96.19%, sensitivity of 95.74% and specificity of 95.25% (Area Under Curve (AUC): 0.99); (b) HuGCDN2014-OXI of 87.32%, 83.81% and 88.55% (AUC: 0.934), respectively. The best results for the global diagnosis of OSA patients (HuGCDN2014-OXI) are: success rate of 95.74%, sensitivity of 100%, and specificity of 89.47%. We conclude that combining both features is the most accurate option, especially when there are non-desaturating patterns among the recordings under study.

Background: Stroke patients often show postural instability. The patellar tendon reflex is a basic physical examination for stroke patients. This study aimed to explore the correlation between patellar tendon reflex grade and postural stability among stroke patients., Methods: A total of 37 elderly stroke patients, each with the same quadriceps muscle strength but different patellar tendon reflex levels, were tested on a force platform under eyes-open (EO) and eyes-closed (EC) conditions. Parametric analysis, detrended fluctuation analysis (DFA), and power spectral density (PSD) analysis were used in centre of pressure (COP) signal processing. The correlation between the results of measured data processing and the level of patellar tendon reflex was analysed., Results: All three parameters of COP (the length of the sway trajectory, the mean range of the sway trajectory in the mediolateral [ML] direction [ R x ], and the mean range of the sway trajectory in the anterior-posterior [AP] directions [ R y ]) were negatively correlated with the patient's patellar tendon reflex grade under the EC condition. The DFA results showed that a higher grade of patellar tendon reflex was associated with a smaller value of the crossover point in the AP direction. Only the PSD values of each frequency band in the AP direction were negatively correlated with patellar tendon reflex grade with EO and became negatively correlated in both AP and ML directions with EC. Overall, the results showed a strong correlation between patellar tendon reflex and postural stability in stroke patients when vision was blocked., Significance: The strong correlation with EC may provide insights into clinic evaluation and treatment for rehabilitation or fall risks of stroke patients., Competing Interests: Conflict of interest: Authors state no conflict of interest., (© 2023 the author(s), published by De Gruyter.)

Previous studies showed that scale-free structures and long-range temporal correlations are ubiquitous in physiological signals (e.g., electroencephalography). This is supposed to be associated with optimized information processing in human brain. The instantaneous alpha frequency (IAF) (i.e., the instantaneous frequency of alpha band of human EEG signals) may dictate the resolution at which information is sampled and/or processed by cortical neurons. To the best of our knowledge, no research has examined the scale-free dynamics and potential functional significance of IAF. Here, through three studies (Study 1: 25 participants; Study 2: 82 participants; Study 3: 26 participants), we investigated the possibility that time series of IAF exhibit scale-free property through maximum likelihood based detrended fluctuation analysis (ML-DFA). This technique could provide the scaling exponent (i.e., DFA exponent) on the basis of presence of scale-freeness being validated. Then the test-retest reliability (Study 1) and potential influencing factors (Study 2 and Study 3) of DFA exponent of IAF fluctuations were investigated. Firstly, the scale-free property was found to be inherent in IAF fluctuations with fairly high test-retest reliability over the parietal-occipital region. Moreover, the task manipulations could potentially modulate the DFA exponent of IAF fluctuations. Specifically, in Study 2, we found that the DFA exponent of IAF fluctuations in eye-closed resting-state condition was significantly larger than that in eye-open resting-state condition. In Study 3, we found that the DFA exponent of IAF fluctuations in eye-open resting-state condition was significantly larger than that in visual n-back tasks. The DFA exponent of IAF fluctuations in the 0-back task was significantly larger than in the 2-back and 3-back tasks. The results in studies 2 and 3 indicated that: (1) a smaller DFA exponent of IAF fluctuations should signify more efficient online visual information processing; (2) the scaling property of IAF fluctuations could reflect the physiological arousal level of participants., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Breast hypertrophy is a significant health problem with both physiological and psychological impacts on the patients' lives. Patients with macromastia adopt a corrective posture due to the effect of the breast on the center of gravity and possibly in a subconscious effort to conceal their breasts., Objective: This study aimed to evaluate whether the posture of patients with macromastia changed after the reduction of mammoplasty., Material and Methods: In this prospective study, patients with breast cup sizes C, D, and DD were scheduled for reduction mammoplasty in 3 Shiraz University Hospitals. Age, weight, height, and preoperative cup sizes of the breasts were recorded for every patient, and all patients underwent posture analysis with forceplate before and after reduction mammoplasty. Finally, the preoperative and postoperative data were compared., Results: Mean age at the time of reduction mammaplasty was 43.57±9.1; the mean pre-operation, such as weight, height, and mean the body mass index (BMI) was 76.57±10 kg, 158.28±6 cm and 30.57±4.1, respectively. The average Anterior-posterior (AP) direction velocity before and after the surgery was 0.85±0.12 cm/s and 0.79±0.098, respectively. These values were 0.83±0.09 and 0.81±0.10 for the mediolateral direction. The Detrended Fluctuation Analysis (DFA) value for the AP direction was 1.63±0.3 and 1.60±0.2 for pre-and post-surgery, respectively, which was not statistically different. The DFA value for maximum likelihood (ML) direction was 1.65±0.2 and 1.48±0.2 in pre-op and post-op, respectively, which was statistically significantly different., Conclusion: Reducing the weight of enlarged breasts can correct disturbed sagittal balance and postural sway., Competing Interests: None, (Copyright: © Journal of Biomedical Physics and Engineering.)

Studies highlight the usage of non-linear time series analysis of heart rate variability (HRV) using the short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA-alpha1) during exercise to determine aerobic and anaerobic thresholds. The present study aims to further verify this approach in women. Gas exchange and HRV data were collected from 26 female participants with different activity levels. Oxygen uptake (VO 2 ) and heart rate (HR) at first (VT1) and second ventilatory thresholds (VT2) were compared with DFA-alpha1-based thresholds 0.75 (HRVT1) and 0.50 (HRVT2). Results: VO 2 at VT1 and VT2 were 25.2 ml/kg/min (± 2.8) and 31.5 ml/kg/min (± 3.6) compared with 26.5 ml/kg/min (± 4.0) and 31.9 ml/kg/min (± 4.5) for HRVT1 and HRVT2, respectively (ICC 3,1  = 0.77, 0.84; r = 0.81, 0.86, p < 0.001). The mean HR at VT1 was 147 bpm (± 15.6) and 167 bpm (± 12.7) for VT2, compared with 152 bpm (± 15.5) and 166 bpm (± 13.2) for HRVT1 and HRVT2, respectively (ICC 3,1  = 0.87, 0.90; r = 0.87, 0.90, p < 0.001). Bland-Altman analysis for VT1 vs. HRVT1 showed a mean difference of - 1.3 ml/kg/min (± 2.4; LoA: 3.3, - 6.0 ml/kg/min) for VO 2 and of - 4.7 bpm (± 7.8; LoA: 10.6, - 20.0 bpm) for HR. VT2 vs. HRVT2 showed a mean difference of - 0.4 ml/kg/min (± 2.3; LoA: 4.1, - 4.9 ml/kg/min) for VO 2 and 0.5 bpm (± 5.7; LoA: 11.8, - 10.8 bpm) for HR. DFA-alpha1-based thresholds showed good agreement with traditionally used thresholds and could be used as an alternative approach for marking organismic transition zones for intensity distribution in women., (© 2022. The Author(s).)

Proprioception is a crucial property for movement stability and balance, but its current assessment, based on clinical testing, lacks precision and adequacy in real contexts. This study proposes assessing proprioception and its sensitivity to training effects through acceleration time series recorded during two slackline experiments. In the first experiment, slackliners of different expertise (highly and poorly trained) had to walk on a slackline for 30 s. In the second, twelve beginners had to balance up on the slackline for at least 11 s before and after a training process. Acceleration time series were recorded in body components (legs and centre of mass) and the slackline. The acceleration fluctuations were analysed through Detrended Fluctuation Analysis. The obtained Hurst (H)-exponents were compared between both groups (first experiment) and before and after training (second experiment) using Whitney and Wilcoxon tests, respectively. The values of H-exponents were lower in the highly trained group (Z = -2.15, p = 0.03) (first experiment), and in the post-training conditions (Z = -2.35, p = 0.02) (second experiment). These results suggest better motor and proprioceptive control with training status. Hence, the time-variability structure of acceleration in real contexts, like slackline tasks, is proposed as an objective measure of proprioception and its training effects., (Copyright © 2023 Montull, Borrallo, Almarcha and Balagué.)

Increased fall risk in older adults and clinical populations is linked with increased amount and altered temporal structure of step width variability. One approach to rehabilitation seeks to reduce fall risk in older adults by reducing the amount of step width variability and restoring the temporal structure characteristic of healthy young adults. The success of such a program depends on our ability to modulate step width variability effectively. To this end, we investigated how manipulation of the visual walking space in a virtual environment could modulate the amount and temporal structure of step width variability. Nine healthy adults performed self-paced treadmill walking in a virtual alley in a fixed-width Control condition (1.91 m) and two conditions in which the alley's width oscillated sinusoidally at 0.03 Hz: between 0.38 and 1.14 m and 0.38-2.67 m in Narrow and Wide conditions, respectively. The step width time series from each condition was evaluated using: (i) the standard deviation to identify changes in the amount of variability and (ii) the fractal scaling exponent estimated using detrended fluctuation analysis (DFA) to identify changes in the temporal structure of variability in terms of persistence in fluctuations. The Wide condition neither affected the standard deviation nor the fractal scaling exponent of step width time series. The Narrow condition did not affect the standard deviation of step width time series compared to the Control condition but significantly increased its fractal scaling exponent compared to the Control and Wide conditions, suggestive of more persistent fluctuations characteristic of a healthy gait. These results show that virtual reality based rehabilitative intervention can modulate step width variability to potentially reduce fall risk in older adults and clinical populations., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

Researchers may select from varied technological and practical options when evaluating balance. Methodological choices inform the quantitative outcomes observed and allow practitioners to diagnose balance abnormalities. Past investigations have differed widely on sampling duration, and these discrepancies hinder comparisons among studies and confidence in outcomes where trials were excessively short. This study aimed to identify necessary trial lengths for common and emerging center of pressure-based measures. We hypothesized that dependent variables would fluctuate over time but eventually reach a stable magnitude. Ninety-seven apparently healthy adults performed quiet standing for 180-seconds (s) with eyes (A) open and (B) closed on a force platform. Anterior-posterior and medial-lateral elements of the center of pressure were used to calculate velocity, time-to-boundary, and Hurst exponents using 15, 30, 90, 120, 150, and 180 s of data. Two-way repeated measures ANOVAs were used to differentiate postural measures over time and between vision conditions. Outcomes were considered stable when significant changes in the measure were no longer observed in the time factor. Dependent measures stabilized for velocity between 60 and 120 s, time-to-boundary between 120 and 150 s, and the Hurst exponent between 30 and 120 s. Velocity measures stabilized quicker with eyes open, whereas vision had no effect or the eyes closed condition was faster to stabilize in time-to-boundary and detrended fluctuation analysis measures. We conclude that 150 s of standing data is sufficient to capture a broad range of postural stability outcomes regardless of vision condition., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

People working as a team can achieve more than when working alone due to a team's ability to parallelize the completion of tasks. In collaborative search tasks, this necessitates the formation of effective division of labor strategies to minimize redundancies in search. For such strategies to be developed, team members need to perceive the task's relevant components and how they evolve over time, as well as an understanding of what others will do so that they can structure their own behavior to contribute to the team's goal. This study explored whether the capacity for team members to coordinate effectively can be related to how participants structure their search behaviors in an online multiplayer collaborative search task. Our results demonstrated that the structure of search behavior, quantified using detrended fluctuation analysis, was sensitive to contextual factors that limit a participant's ability to gather information. Further, increases in the persistence of movement fluctuations during search behavior were found as teams developed more effective coordinative strategies and were associated with better task performance., (© 2022 The Authors. Cognitive Science published by Wiley Periodicals LLC on behalf of Cognitive Science Society (CSS).)

Nonlinear analyses of heart rate variability (HRV) can be used to quantify the unpredictability, fractal properties and complexity of heart rate. Fractality and its analysis provides valuable information about cardiovascular health. Multi-Scale Multi-Fractal Detrended Fluctuation Analysis (MSMFDFA) is a complexity-based algorithm that can be used to quantify the multi-fractal dynamics of the HRV time series through investigating characteristic exponents at different time scales. This method is applicable to short time series and it is robust to noise and nonstationarity. We have used MSMFDFA, which enables assessment of HRV in the frequency ranges encompassing the very-low frequency and ultra-low frequency bands, to jointly assess multi-scale and multi-fractal dynamics of HRV signals obtained from telemetric ECG recordings in wildtype mice at baseline and after autonomic nervous system (ANS) blockade, from electrograms recorded from isolated atrial preparations and from spontaneous action potential recordings in isolated sinoatrial node myocytes. Data demonstrate that the fractal profile of the intrinsic heart rate is significantly different from the baseline heart rate in vivo , and it is also altered after ANS blockade at specific scales and fractal order domains. For beating rate in isolated atrial preparations and intrinsic heart rate in vivo , the average fractal structure of the HRV increased and multi-fractality strength decreased. These data demonstrate that fractal properties of the HRV depend on both ANS activity and intrinsic sinoatrial node function and that assessing multi-fractality at different time scales is an effective approach for HRV assessment., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Moghtadaei, Dorey and Rose.)

This study aimed to apply different complexity-based methods to surface electromyography (EMG) in order to detect neuromuscular changes after realistic warm-up procedures that included stretching exercises. Sixteen volunteers conducted two experimental sessions. They were tested before, after a standardized warm-up, and after a stretching exercise (static or neuromuscular nerve gliding technique). Tests included measurements of the knee flexion torque and EMG of biceps femoris (BF) and semitendinosus (ST) muscles. EMG was analyzed using the root mean square (RMS), sample entropy (SampEn), percentage of recurrence and determinism following a recurrence quantification analysis (%Rec and %Det) and a scaling parameter from a detrended fluctuation analysis. Torque was significantly greater after warm-up as compared to baseline and after stretching. RMS was not affected by the experimental procedure. In contrast, SampEn was significantly greater after warm-up and stretching as compared to baseline values. %Rec was not modified but %Det for BF muscle was significantly greater after stretching as compared to baseline. The a scaling parameter was significantly lower after warm-up as compared to baseline for ST muscle. From the present results, complexity-based methods applied to the EMG give additional information than linear-based methods. They appeared sensitive to detect EMG complexity increases following warm-up.

Background: Postural impairment is one of the most debilitating symptoms in people with Parkinson's disease (PD), which show faster and more variable oscillation during quiet stance than neurologically healthy individuals. Despite the center of pressure parameters can characterize PD's body sway, they are limited to uncover underlying mechanisms of postural stability and instability., Research Question: Do a multiple domain analysis, including postural adaptability and rambling and trembling components, explain underlying postural stability and instability mechanisms in people with PD?, Method: Twenty-four individuals (12 people with PD and 12 neurologically healthy peers) performed three 60-s trials of upright quiet standing on a force platform. Traditional and non-linear parameters (Detrended Fluctuation Analysis- DFA and Multiscale Entropy- MSE) and rambling and trembling trajectories were calculated for anterior-posterior (AP) and medial-lateral (ML) directions., Results: PDG's postural control was worse compared to CG, displaying longer displacement, higher velocity, and RMS. Univariate analyses revealed largely longer displacement and RMS only for the AP direction and largely higher velocity for both AP and ML directions. Also, PD individuals showed lower AP complexity, higher AP and ML DFA, and increased AP and ML displacement, velocity, and RMS of rambling and trembling components compared to neurologically healthy individuals., Significance: Based upon these results, people with PD have a lower capacity to adapt posture and impaired both rambling and trembling components compared to neurologically healthy individuals. These findings provide new insights to explain the larger, faster, and more variable sway in people with PD., Competing Interests: Conflict of interest The authors declare that there is no conflict of interest., (Copyright © 2022 Elsevier B.V. All rights reserved.)

We investigated the effect of prolonged running on joint kinematics and its association with stride complexity between novice and elite runners. Ten elite marathoners and eleven healthy individuals took part in a 20 min submaximal prolonged running experiment at their preferred running speed (PRS). A three-dimensional motion capture system was utilized to capture and calculate the alpha exponent, stride-to-stride fluctuations (SSFs), and stride-to-stride variability (SSV) of spatiotemporal parameters and joint kinematics. In the results, the elite athletes ran at a considerably higher PRS than the novice runners, yet no significant differences were found in respiratory exchange ratio with increasing time intervals. For the spatiotemporal parameters, we observed a significant increase in the step width and length variability in novice runners with increasing time-interval (p < 0.05). However, we did not observe any differences in the alpha exponent of spatiotemporal parameters. Significant differences in SSF of joint kinematics were observed, particularly in the sagittal plane for ankle, knee, and hip at heel strike (p < 0.05). While in mid-stance, time-interval differences were observed in novices who ran with a lower knee flexion angle (p < 0.05). During toe-off, significantly higher SSV was observed, particularly in the hip and ankle for novices (p < 0.05). The correlation analysis of joint SSV revealed a distinct negative relationship with the alpha exponent of step-length and step-width for elite runners, while, for novices, a positive relation was observed only for the alpha exponent of step-width. In conclusion, our study shows that increased step-width variability seen in novices could be a compensatory mechanism to maintain performance and mitigate the loss of stability. On the other hand, elite runners showed a training-induced effective modulation of lower-limb kinematics to improve their running performance.

Background: Previous studies have associated slowed ventricular conduction with the arrhythmogenesis mediated by the gap junction and sodium channel inhibitor heptanol in mouse hearts. However, they did not study the propagation patterns that might contribute to the arrhythmic substrate. This study used a multi-electrode array mapping technique to further investigate different conduction abnormalities in Langendorff-perfused mouse hearts exposed to 0.1 or 2 mM heptanol. Methods: Recordings were made from the left ventricular epicardium using multi-electrode arrays in spontaneously beating hearts during right ventricular 8 Hz pacing or S1S2 pacing. Results: In spontaneously beating hearts, heptanol at 0.1 and 2 mM significantly reduced the heart rate from 314 ± 25 to 189 ± 24 and 157 ± 7 bpm, respectively (ANOVA, p < 0.05 and p < 0.001). During regular 8 Hz pacing, the mean LATs were increased by 0.1 and 2 mM heptanol from 7.1 ± 2.2 ms to 19.9 ± 5.0 ms (p < 0.05) and 18.4 ± 5.7 ms (p < 0.05). The standard deviation of the mean LATs was increased from 2.5 ± 0.8 ms to 10.3 ± 4.0 ms and 8.0 ± 2.5 ms (p < 0.05), and the median of phase differences was increased from 1.7 ± 1.1 ms to 13.9 ± 7.8 ms and 12.1 ± 5.0 ms by 0.1 and 2 mM heptanol (p < 0.05). P5 took a value of 0.2 ± 0.1 ms and was not significantly altered by heptanol at 0.1 or 2 mM (1.1 ± 0.9 ms and 0.9 ± 0.5 ms, p > 0.05). P50 was increased from 7.3 ± 2.7 ms to 24.0 ± 12.0 ms by 0.1 mM heptanol and then to 22.5 ± 7.5 ms by 2 mM heptanol (p < 0.05). P95 was increased from 1.7 ± 1.1 ms to 13.9 ± 7.8 ms by 0.1 mM heptanol and to 12.1 ± 5.0 ms by 2 mM heptanol (p < 0.05). These changes led to increases in the absolute inhomogeneity in conduction (P5−95) from 7.1 ± 2.6 ms to 31.4 ± 11.3 ms, 2 mM: 21.6 ± 7.2 ms, respectively (p < 0.05). The inhomogeneity index (P5−95/P50) was significantly reduced from 3.7 ± 1.2 to 3.1 ± 0.8 by 0.1 mM and then to 3.3 ± 0.9 by 2 mM heptanol (p < 0.05). Conclusion: Increased activation latencies, reduced CVs, and the increased inhomogeneity index of conduction were associated with both spontaneous and induced ventricular arrhythmias.

Heart Rate Variability (HRV) can be a useful metric to capture meaningful information about heart function. One of the non-linear indices used to analyze HRV, Detrended Fluctuation Analysis (DFA), finds short and long-term correlations in RR intervals to capture quantitative information about variability. This study focuses on the impact of visual and mental stimulation on HRV as expressed via DFA within healthy adults. Visual stimulation can activate the automatic nervous system to directly impact physiological behavior such as heart rate. In this investigation of HRV, 70 participants (21 males) viewed images on a screen followed by a math and recall task. Each viewing segment lasted 2 min and 18 s. The math and memory recall task segment lasted 4 min total. This process was repeated 9 times during which the participants' electrocardiogram was recorded. 37 participants (12 males) opted in for an additional 24-h Holter recording after the viewing and task segments of the study were complete. Participants were randomly assigned to either a pure (organized image presentation) or mixed (random image presentation) image regime for the viewing portion of the study to investigate the impact of the external environment on HRV. DFA α 1 was extracted from the RR intervals. Our findings suggest that DFA α 1 can differentiate between the viewing [DFA α 1 range from 0.96 (SD = 0.25) to 1.08 (SD = 0.22)] and the task segments [DFA α 1 range from 1.17 (SD = 0.21) to 1.26 (SD = 0.25)], p < 0.0006 for all comparisons. However, DFA α 1 was not able to distinguish between the two image regimes. During the 24-hour follow up, participants had an average DFA α 1 = 1.09 (SD = 0.14). In conclusion, our findings suggest a graded response in DFA during short term stimulation and a responsiveness in participants to adjust physiologically to their external environment expressed through the DFA exponent., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Gu, Zarubin, Mickley Steinmetz and Martsberger.)