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The interest in photoplethysmography (PPG) for sleep monitoring is increasing because PPG may allow assessing heart rate variability (HRV), which is particularly important in breathing disorders. Thus, we aimed to evaluate how PPG wearable systems measure HRV during sleep at high altitudes, where hypobaric hypoxia induces respiratory disturbances. We considered PPG and electrocardiographic recordings in 21 volunteers sleeping at 4554 m a.s.l. (as a model of sleep breathing disorder), and five alpine guides sleeping at sea level, 6000 m and 6800 m a.s.l. Power spectra, multiscale entropy, and self-similarity were calculated for PPG tachograms and electrocardiography R–R intervals (RRI). Results demonstrated that wearable PPG devices provide HRV measures even at extremely high altitudes. However, the comparison between PPG tachograms and RRI showed discrepancies in the faster spectral components and at the shorter scales of self-similarity and entropy. Furthermore, the changes in sleep HRV from sea level to extremely high altitudes quantified by RRI and PPG tachograms in the five alpine guides tended to be different at the faster frequencies and shorter scales. Discrepancies may be explained by modulations of pulse wave velocity and should be considered to interpret correctly autonomic alterations during sleep from HRV analysis. [ABSTRACT FROM AUTHOR]

Climate and sediment supply are critical for geomorphic systems. It is known that complex relations between such forcings and sediment mobilization may lead to dampened or erased environmental signals in sediment records. But it is unclear under which circumstances environmental signals are transmitted and measurable downstream. We used a numerical approach consisting of a sediment cascade model and a stochastic weather generator to quantify climate forcing effects under a range of sediment supply regimes in a debris‐flow catchment in the Swiss Alps (Illgraben). We show that sediment yields estimated from short records are highly uncertain both in terms of mean and interannual variability. Furthermore, sediment yields tend to be underestimated in supply‐limited systems, where also long‐term memory effects, driven by the history of sediment storage, are evident. Consequently, determining geomorphic system response from short records may be grossly inaccurate and should be extended with sediment supply detection and uncertainty analysis. Plain Language Summary: Whether or not environmental signals, such as climate change or extreme events, are measurable in the sediment output of a basin is a timely question. Climate change affects processes related to sediment production and transport. However, because relations between these are complex, it is questionable if environmental signals are transmitted and measurable in the downstream sediment discharge. Here, we used a numerical approach for a geomorphic system; the Illgraben debris‐flow catchment in the Swiss Alps. We coupled a sediment cascade model and a stochastic weather generator to study the detectability of change in sediment yield under a range of conditions, such as different sampling durations and mean erosion rates. We show that sediment yields estimated from short records are highly uncertain and that the history of sediment supply and storage matters. These effects should be included in assessments of change in geomorphic systems. Key Points: Long‐term sediment flux simulations (10k years) at hourly resolution are studied under stochastic climatic forcingSediment yield estimates from short records (<30 years) are highly uncertain and likely underestimatedSediment yield is affected by sediment supply and sediment storage but does not reflect the exact timing of sediment‐input events [ABSTRACT FROM AUTHOR]

Climate change has been a significant subject in recent years all around the world. Statistical analysis of climatic parameters such as rainfall can investigate the actual status of the atmosphere. As a result, this study aimed to look at the pattern of mean annual rainfall in India from 1901 to 2016, considering 34 meteorological subdivisions. The Mann–Kendall (MK) test, Modified Mann–Kendall (MMK) test, Bootstrapped MK (BMK) test, and Innovative Trend Analysis (ITA) were used to find trends in yearly rainfall time-series results. Rainfall forecasting was evaluated using detrended fluctuation analysis (DFA). Because the research comprised 34 meteorological subdivisions, it may be challenging to convey the general climatic conditions of India in a nutshell. The MK, MMK, and BMK tests showed a significant (p < 0.01 to p < 0.1) negative trend in 9, 8, and 9 sub-divisions, respectively. According to the ITA, a negative trend was found in 17 sub-divisions, with 9 sub-divisions showing a significance level of 0.01 to 0.1. The ITA outperformed the other three trend test techniques. The results of DFA showed that 20 sub-divisions would decrease in future rainfall, suggesting that there was a link between past and future rainfall trends. Results show that highly negative or decreasing rainfall trends have been found in broad regions of India, which could be related to climate change, according to the results. ITA and DFA techniques to discover patterns in 34 sub-divisions across India have yet to be implemented. In developing management plans for sustainable water resource management in the face of climate change, this research is a valuable resource for climate scientists, water resource scientists, and government officials. [ABSTRACT FROM AUTHOR]

This paper intends to assess and test long-term memory in the Euronext stock indexes returns in the search for fractal dynamics that refute the random walk hypothesis. The Hurst exponents estimated through Rescaled-Range and Detrended Fluctuation Analysis evidence long memory in the form of persistence for all markets, with the exception of CAC 40 by the DFA. However, the Rescaled-Range Tests neither reject the absence of long dependency nor reject the existence of short dependency. On the contrary, the Fractional Differencing Test supports the presence of persistence in the PSI 20, ISE 20 and OBX indexes. This suggests that these markets are more prone to predictability, but also trends that may be unexpectedly disrupted by discontinuities, exhibiting dynamics incompatible with random walk behavior and providing evidence against the weak form of efficiency and validity of the asset pricing models. [ABSTRACT FROM AUTHOR]

Cardiovascular disease is among the leading causes of mortality in chronic obstructive pulmonary disease (COPD). Nonlinear heart rate variability (NHRV) measures are markers and predictors of cardiovascular disease, particularly arrhythmias. Our aim was to investigate NHRV in patients with COPD and changes after pulmonary rehabilitation. 20-minute ECGs were used to compare NHRV (a) in 45 healthy individuals and 31 patients with COPD and (b) in 16 patients who completed rehabilitation versus 13 age- and sex-matched control patients. We studied detrended fluctuation analysis (DFA1, DFA2), fractal dimension (low, high, average FD) and sample entropy. Compared to healthy individuals, patients with COPD had lower DFA1 (p=.038). During rehabilitation high FD decreased (p=.018) and DFA2 increased (p=.043). Cluster analysis displayed an increase of DFA1 in the rehabilitation cluster with DFA1 values below 1 (p=.032). NHRV reflects altered autonomic regulation in patients with COPD. Reduced DFA1 in patients with COPD implies a stronger pro-arrhythmic substrate and altered parasympathetic modulation. [ABSTRACT FROM AUTHOR]

Background: Time-domain and non-linear methods can be used to quantify beat-to-beat repolarization variability but whether measures of repolarization variability can predict ventricular arrhythmogenesis in mice have never been explored. Methods: Left ventricular monophasic action potentials (MAPs) were recorded during constant right ventricular 8 ​Hz pacing in Langendorff-perfused mouse hearts, in the presence or absence of the gap junction and sodium channel inhibitor heptanol (0.1, 0.5, 1 or 2 ​mM). Results: Under control conditions, mean action potential duration (APD) was 39.4 ​± ​8.1 ​ms. Standard deviation (SD) of APDs was 0.3 ​± ​0.2 ​ms, coefficient of variation was 0.9 ​± ​0.8% and the root mean square (RMS) of successive differences in APDs was 0.15 ​± ​0.14 ​ms. Poincaré plots of APDn+1 against APDn revealed ellipsoid morphologies with a SD along the line-of-identity (SD2) to SD perpendicular to the line-of-identity (SD1) ratio of 4.6 ​± ​2.1. Approximate and sample entropy were 0.61 ​± ​0.12 and 0.76 ​± ​0.26, respectively. Detrended fluctuation analysis revealed short- and long-term fluctuation slopes of 1.49 ​± ​0.27 and 0.81 ​± ​0.36, respectively. Heptanol at 2 ​mM induced ventricular tachycardia in five out of six hearts. None of the above parameters were altered by heptanol during which reproducible electrical activity was observed (KW-ANOVA, P ​> ​0.05). Contrastingly, SD2/SD1 decreased to 2.03 ​± ​0.41, approximate and sample entropy increased to 0.82 ​± ​0.12 and 1.45 ​± ​0.34, and short-term fluctuation slope decreased to 0.82 ​± ​0.19 during the 20-s period preceding spontaneous ventricular tachy-arrhythmias (n ​= ​6, KW-ANOVA, P ​

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 α1 was extracted from the RR intervals. Our findings suggest that DFA α1 can differentiate between the viewing [DFA α1 range from 0.96 (SD = 0.25) to 1.08 (SD = 0.22)] and the task segments [DFA α1 range from 1.17 (SD = 0.21) to 1.26 (SD = 0.25)], p < 0.0006 for all comparisons. However, DFA α1 was not able to distinguish between the two image regimes. During the 24-hour follow up, participants had an average DFA α1 = 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.

IntroductionAlthough a few studies suggest that young overweight to obese children and adolescents (YO) may have impaired postural control compared to young normal-weight (YN) peers, little information exists about how these two groups differ in the quality of the underlying balance strategies employed. Hence, the aim of the present study was a first comprehensive examination of the structural complexity of postural sways in these two cohorts during quiet bilateral standing.MethodsNineteen YO secondary school students (13.0 ± 1.4 years; male = 10, female = 9) were carefully matched to YN controls (13.0 ± 1.5 years) for age, sex, height, and school. Mediolateral (ML) and anteriorposterior (AP) acceleration signals were recorded with an inertial measurement unit (IMU) positioned at the trunk while standing barefoot in two conditions: firm and foam support surface. The magnitude of postural fluctuations was obtained using the root mean square (RMS). The temporal structure of the signals was analyzed via sample entropy (SEn), largest Lyapunov exponent (LyE), and detrended fluctuation analysis (α-DFA) algorithm. Reliability was assessed using a test–retest design.ResultsIn both groups, foam standing caused higher postural fluctuations (higher RMS values) and reduced structural complexity (lower SEn values, higher LyE values, higher α-DFA values). In comparison to YN, YO exhibited a higher RMSAP. Especially in ML direction, the acceleration signals of the YO had higher repeatability (smaller SEn values), greater long-range correlations (higher α-DFA values), and lower local stability (higher LyE values). However, these observations were largely independent of the task difficulty. Except for α-DFAAP, the IMU approach proved reliable to characterize posture control.DiscussionOur outcomes confirm postural control deficits in YO compared to their YN peers and indicate impaired regulatory mechanisms reflected as rigidity. Such less complex patterns usually reflect diverse pathologies, are detrimental to compensate for internal or external perturbations, and are attributed to lower adaptability and task performance. Without targeted balance stimuli, YO likely end in a lifelong vicious circle of mutually dependent poor balance regulation and low physical activity.

Multiple sensory signals from visual, somatosensory and vestibular systems are used for human postural control. To maintain postural stability, the central nervous system keeps the center of mass (CoM) within the base of support. The influence of the stomatognathic motor system on postural control has been established under static conditions, but it has not yet been investigated during dynamic steady-state balance. The purpose of the study was to investigate the effects of controlled stomatognathic motor activity on the control and stability of the CoM during dynamic steady-state balance. A total of 48 physically active and healthy adults were assigned to three groups with different stomatognathic motor conditions: jaw clenching, tongue pressing and habitual stomatognathic behavior. Dynamic steady-state balance was assessed using an oscillating platform and the kinematic data were collected with a 3D motion capturing system. The path length (PL) of the 3D CoM trajectory was used for quantifying CoM sway. Temporal dynamics of the CoM movement was assessed with a detrended fluctuation analysis (DFA). An uncontrolled manifold (UCM) analysis was applied to assess the stability and control of the CoM with a subject-specific anthropometric 3D model. The statistical analysis revealed that the groups did not differ significantly in PL, DFA scaling exponents or UCM parameters. The results indicated that deliberate jaw clenching or tongue pressing did not seem to affect the sway, control or stability of the CoM on an oscillating platform significantly. Because of the task-specificity of balance, further research investigating the effects of stomatognathic motor activities on dynamic steady-state balance with different movement tasks are needed. Additionally, further analysis by use of muscle synergies or co-contractions may reveal effects on the level of muscles, which were not visible on the level of kinematics. This study can contribute to the understanding of postural control mechanisms, particularly in relation to stomatognathic motor activities and under dynamic conditions. [ABSTRACT FROM AUTHOR]

Assessing power-law cross-correlations between a pair – or among a set – of processes is of great significance in diverse fields of analyses ranging from neuroscience to financial markets. In most cases such analyses are computationally expensive and thus carried out offline once the entire signal is obtained. However, many applications – such as mental state monitoring or financial forecasting – call for fast algorithms capable of estimating scale-free coupling in real time. Detrended cross-correlation analysis (DCCA), a generalization of the detrended fluctuation analysis (DFA) to the bivariate domain, has been introduced as a method designed to quantify power-law cross-correlations between a pair of non-stationary signals. Later, in analogy with the Pearson cross-correlation coefficient, DCCA was adapted to the detrended cross-correlation coefficient (DCCC), however as of now no online algorithms were provided for either of these analysis techniques. Here we introduce a new formula for obtaining the scaling functions in real time for DCCA. Moreover, the formula can be generalized via matrix notation to obtain the scaling relationship between not only a pair of signals, but also all possible pairs among a set of signals at the same time. This includes parallel estimation of the DFA scaling function of each individual process as well, thus allowing also for real-time acquisition of DCCC. The proposed algorithm matches its offline variants in precision, while being substantially more efficient in terms of execution time. We demonstrate that the method can be utilized for mental state monitoring on multi-channel electroencephalographic recordings obtained in eyes-closed and eyes-open resting conditions. [ABSTRACT FROM AUTHOR]

Background: Adaptive treadmills allow real-time changes in walking speed by responding to changes in step length, propulsion, or position on the treadmill. The stride-to-stride variability, or persistence, of stride time during overground, fixed-speed, and adaptive treadmill walking has been studied, but persistence of propulsion during adaptive treadmill walking remains unknown. Because increased propulsion is often a goal of post-stroke rehabilitation, knowledge of the stride-to-stride variability may aid rehabilitation protocol design.Research Question: How do spatiotemporal and propulsive gait variables vary from stride to stride during adaptive treadmill walking, and how do they compare to fixed-speed treadmill walking?Methods: Eighteen young healthy subjects walked on an instrumented split-belt treadmill in the adaptive and fixed-speed modes for 10 minutes at their comfortable speed. Kinetic data was collected from the treadmill. Detrended fluctuation analysis was applied to the time series data. Shapiro-Wilk tests assessed normality and one-way repeated measures ANOVAs compared between adaptive, fixed-speed, and randomly shuffled conditions at a Bonferroni-corrected significance level of 0.0055.Results: Stride time, stride length, step length, and braking impulse were persistent (α > 0.5) in the adaptive and fixed-speed conditions. Adaptive and fixed-speed were different from each other. Stride speed was persistent in the adaptive condition and anti-persistent (α < 0.5) in the fixed-speed condition. Peak propulsive force, peak braking force, and propulsive impulse were persistent in the adaptive condition but not the fixed-speed condition (α ≈ 0.5). Net impulse was non-persistent in the adaptive and fixed-speed conditions. All variables were non-persistent in the shuffled condition.Significance: During adaptive treadmill walking, increases in propulsive force and impulse persist for multiple strides. Persistence was stronger on the adaptive treadmill, where increased propulsion translates into increased walking speed. For post-stroke gait rehabilitation where increasing propulsion and speed are goals, the stronger persistence of adaptive treadmill walking may be beneficial. [ABSTRACT FROM AUTHOR]

Early detection of preterm labor is important to avoid neonatal death and mortality. Uterine electromyography (UEMG) or electrohysterography is a non-invasive method of extracting electrical activity signal from the abdominal part during pregnancy, which helps in early detection. This signal can be used to classify term and preterm labors. Herein, the performances of four classifiers have been evaluated using seven nonlinear features extracted from UEMG signals. They were then compared with four features analyzed from different literature. The results show that with the Elman neural network classifier, the bi-spectrum feature, which has phase information, outperforms other features with 99.8875% accuracy, 100% sensitivity, and 99.77% specificity. [ABSTRACT FROM AUTHOR]

Background: Due to the imposed constant belt speed, motorized treadmills are known to affect linear and nonlinear gait variability outcomes. This is particularly true of patients with Parkinson's Disease where the treadmill can act as an external pacemaker. Self-paced treadmills update the belt speed in response to the subject's walking speed and might, therefore, be a useful tool for measurement of gait variability in this patient population. This study aimed to compare gait variability during walking at self-paced and constant treadmill speeds with overground walking in individuals with PD and individuals with unimpaired gait.Methods: Thirteen patients with Parkinson's Disease and thirteen healthy controls walked under three conditions: overground, on a treadmill at a constant speed, and using three self-paced treadmill modes. Gait variability was assessed with coefficient of variation (CV), sample entropy (SampEn), and detrended fluctuation analysis (DFA) of stride time and length. Systematic and random error between the conditions was quantified.Results: For individuals with PD, error in variability measurement was less during self-paced modes compared with constant treadmill speed for stride time but not for stride length. However, there was substantial error for stride time and length variability for all treadmill conditions. For healthy controls the error in measurement associated with treadmill walking was substantially less.Significance: The large systematic and random errors between overground and treadmill walking prohibit meaningful gait variability observations in patients with Parkinson's Disease using self-paced or constant-speed treadmills. [ABSTRACT FROM AUTHOR]

This study examines the persistence and long-term correlation of monthly and seasonal precipitation time series of the Kırşehir province (Turkey) for the period of 1960-2019, with the widely used Hurst exponent (H) and Detrended Fluctuation Analysis (DFA) methods. Both methods can be used to detect the long-term memory and correlation to be assessed as a reference of predictability. In order to support the study results, Augmented Dickey Fuller (ADF) and Mann-Kendall (MK) tests were applied to the time series under consideration. In some of the precipitation series, the evidence of persistence and long-term correlation was identified. According to the H exponent values, 10 out of 12 months, winter, and autumn seasons (with both simple R/S and corrected R/S methods), and spring and summer seasons (respectively with simple R/S and corrected R/S methods) exhibit long term correlations. On the other hand, according to the DFA scaling exponent values, 4 out of 12 months, winter and autumn seasons reveal long term correlations. When the H exponent and DFA scaling exponent values are compared only four monthly and two seasonal precipitation series are found to be consistent with each other. [ABSTRACT FROM AUTHOR]

Long-term memory (LTM) in the climate system has been well recognized and applied in different research fields, but the origins of this property are still not clear. In this work, the authors contribute to this issue by studying model simulations under different scenarios. The global mean temperatures from pre-industrial control runs (piControl), historical (all forcings) simulations, natural forcing only simulations (HistoricalNat), greenhouse gas forcing only simulations (HistoricalGHG), etc., are analyzed using the detrended fluctuation analysis. The authors find that the LTM already exists in the piControl simulations, indicating the important roles of internal natural variability in producing the LTM. By comparing the results among different scenarios, the LTM from the piControl runs is further found to be strengthened by adding natural forcings such as the volcanic forcing and the solar forcing. Accordingly, the observed LTM in the climate system is suggested to be mainly controlled by both the ‘internal’ natural variability and the ‘external’ natural forcings. The anthropogenic forcings, however, may weaken the LTM. In the projections from RCP2.6 to RCP8.5, a weakening trend of the LTM strength is found. In view of the close relations between the climate memory and the climate predictability, a reduced predictability may be expected in a warming climate.

Time-frequency analysis always plays an important role in machine health monitoring owing to its advantage in extracting the fault information contained in non-stationary signal. In this paper, we present a novel technique to detect and diagnose the rolling bearing faults based on high-order synchrosqueezing transform (FSSTH) and detrended fluctuation analysis (DFA). With this method, the high-order synchrosqueezing transform is first utilized to decompose the vibration signal into an ensemble of oscillatory components termed as intrinsic mode functions (IMFs). Meanwhile, an empirical equation, which is based on the DFA, is introduced to adaptively determine the number of IMFs from FSSTH. Then, a time-frequency representation originated from the decomposed modes or corresponding envelopes is exhibited in order to identify the fault characteristic frequencies related to rolling bearing. Experiments are carried out using both simulated signal and real ones from Case Western Reserve University. Results show that the proposed method is more effective for the detection of fault characteristic frequencies compared with the traditional synchrosqueezing transform (SST) based fault diagnosis algorithm, which renders this technique is promising for machine fault diagnosis.

Electrocardiogram (ECG) is a critical biological signal, which usually carries a great deal of essential information about patients. The high quality ECG signals are always required for a proper diagnosis of cardiac disorders. However, the raw ECG signals are highly noisy in nature. In the paper, we propose a hybrid denoising scheme to enhance ECG signals by combining high-order synchrosqueezing transform (FSSTH) with non-local means (NLM). With this method, a noisy ECG signal is first decomposed into an ensemble of intrinsic mode functions (IMFs) by FSSTH. Then, some noise is removed by eliminating a set of noisy IMFs that are determined by a scaling exponent obtained by the detrended fluctuation analysis (DFA); while the remaining IMFs are filtered by NLM. Finally, the denoised ECG signal is obtained by reconstructing the processed IMFs. Experiments are carried out using the simulated ECG signals and real ones from the MIT-BIH database, and the denoising performances are evaluated in terms of signal to noise ratio (SNR), root mean squared error (RMSE) and percent root mean square difference (PRD). Results show that the hybrid denoising scheme involving both FSSTH and NLM is able to suppress complex noise from ECG signals more effectively while preserving the details well.

Assessing power-law cross-correlations between a pair – or among a set – of processes is of great significance in diverse fields of analyses ranging from neuroscience to financial markets. In most cases such analyses are computationally expensive and thus carried out offline once the entire signal is obtained. However, many applications – such as mental state monitoring or financial forecasting – call for fast algorithms capable of estimating scale-free coupling in real time. Detrended cross-correlation analysis (DCCA), a generalization of the detrended fluctuation analysis (DFA) to the bivariate domain, has been introduced as a method designed to quantify power-law cross-correlations between a pair of non-stationary signals. Later, in analogy with the Pearson cross-correlation coefficient, DCCA was adapted to the detrended cross-correlation coefficient (DCCC), however as of now no online algorithms were provided for either of these analysis techniques. Here we introduce a new formula for obtaining the scaling functions in real time for DCCA. Moreover, the formula can be generalized via matrix notation to obtain the scaling relationship between not only a pair of signals, but also all possible pairs among a set of signals at the same time. This includes parallel estimation of the DFA scaling function of each individual process as well, thus allowing also for real-time acquisition of DCCC. The proposed algorithm matches its offline variants in precision, while being substantially more efficient in terms of execution time. We demonstrate that the method can be utilized for mental state monitoring on multi-channel electroencephalographic recordings obtained in eyes-closed and eyes-open resting conditions.

Vehicle driving cycles have complex characteristics, but there are few publicly reported methods for their quantitative characterization. This paper innovatively investigates their multifractal characteristics using the fractal theory to characterize their complex properties, laying the foundation for applications such as vehicle driving cycle feature identification, vehicle energy management strategies (EMS), and so on. To explore the scale-invariance of the vehicle driving cycles, the four vehicle driving cycles were analyzed using the Multifractal Detrended Fluctuation Analysis (MF-DFA) method, three of which are standard vehicle test cycles: the New European Driving Cycle (NEDC), the World-wide harmonized Light-duty Test Cycle (WLTC) and the China Light-duty Vehicle Test Cycle for Passenger Car (CLTC-P), and the other is the Urban Road Real Driving Cycle (URRDC), which was obtained by analyzing and processing vehicle driving data collected in actual urban driving conditions. The fluctuation functions, the generalized Hurst exponents, the mass exponent spectra, the multifractal singularity spectra, and the multifractal characteristic parameters were calculated to verify the multifractal characteristics, and to quantify the fluctuation singularities of different driving cycles as the time series. The results show that the fluctuations of all four driving cycles have long-range anticorrelations and exhibit significant multifractal characteristics. The results can provide a basis for the analysis of the complexity of the vehicle driving cycles.

In this study we investigate possible long-range trends in the cryptocurrency market. We employed the Hurst exponent in a sample covering the period from 1 January 2016 to 26 March 2021. We calculated the Hurst exponent in three non-overlapping consecutive windows and in the whole sample. Using these windows, we assessed the dynamic evolution in the structure and long-range trend behavior of the cryptocurrency market and evaluated possible changes in their behavior towards an efficient market. The innovation of this research is that we employ the Hurst exponent to identify the long-range properties, a tool that is seldomly used in analysis of this market. Furthermore, the use of both the R/S and the DFA analysis and the use of non-overlapping windows enhance our research’s novelty. Finally, we estimated the Hurst exponent for a wide sample of cryptocurrencies that covered more than 80% of the entire market for the last six years. The empirical results reveal that the returns follow a random walk making it difficult to accurately forecast them.

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.

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.)

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.)

The dynamical complexities of the Nigerian stock market have been largely uninvestigated. In this study, we analysed the return price of financial stocks on Nigerian Stock Exchange for chaotic behaviour. Our analysis was conducted for the period 2000–2015, as well as for three distinct periods covering the pre-crisis, crisis and post-crisis period of 2008. Fractal anlaysis (detrended fluctuation analysis and rescale range), entropy (Kolmogorov and permutation), recurrence quantification analysis (determinism and longest diagonal line) and Lyapunov exponent (Rosenstein and Eckmann) methods were used in the investigation. Results showed that the return prices of six financial stocks exhibit behaviour associated with random noise and chaos. The stocks were found to be more efficient post-crisis than during the pre-crisis period. Return prices post-crisis were found to be more chaotic. [ABSTRACT FROM AUTHOR]

Recent advancements in detrended fluctuation analysis (DFA) allow evaluating multifractal coefficients scale-by-scale, a promising approach for assessing the complexity of biomedical signals. The multifractality degree is typically quantified by the singularity spectrum width (WSS), a method that is critically unstable in multiscale applications. Thus, we aim to propose a robust multiscale index of multifractality, compare it with WSS and illustrate its performance on real biosignals. The proposed index is the cumulative function of squared increments between consecutive DFA coefficients at each scale n: aCF(n). We compared it with WSS calculated scale-by-scale considering monofractal/monoscale, monofractal/multiscale, multifractal/monoscale and multifractal/multiscale random processes. The two indices provided qualitatively similar descriptions of multifractality, but aCF(n) differentiated better the multifractal components from artefacts due to crossovers or detrending overfitting. Applied on 24 h heart rate recordings of 14 participants, the singularity spectrum failed to always satisfy the concavity requirement for providing meaningful WSS, while aCF(n) demonstrated a statistically significant heart rate multifractality at night in the scale ranges 16-100 and 256-680 s. Furthermore, aCF(n) did not reject the hypothesis of monofractality at daytime, coherently with previous reports of lower nonlinearity and monoscale multifractality during the day. Thus, aCF(n) appears a robust index of multiscale multifractality that is useful for quantifying complexity alterations of physiological series. [ABSTRACT FROM AUTHOR]

• Connection between complexity and parameter settings of financial models is analyzed. • Nine popular small-scale financial agent-based models are studied. • Herding behavior and various in-built stabilizing factors increase model complexity. • Increasing number of agents has model-specific effects. We examine the complexity of financial returns generated by popular agent-based models through studying multifractal properties of such time series. Specifically, we are interested in the sensitivity of the models to their parameter settings and whether some patterns emerge in the connection between complexity and a specific type of parameter. We find that (i) herding behavior mostly boosts the model complexity as measured by multifractality, (ii) various in-built stabilizing factors increase model complexity, while (iii) the role of the intensity of choice, the number of agents, as well as the chartists' representation have rather model-specific effects. Finally, the core feature driving the model complexity seems to be the implementation of a switching mechanism governing agents' interactions. The heterogeneous set of nine analyzed models thus offers some universal concepts that hold across their range. Our results also indicate that complex dynamics are observed not only for the benchmark parameter settings but also for other combinations of parameter values for most models. This opens new avenues for future research and specifically motivates examining the models in more detail by focusing on other dynamic properties in addition to the herein presented multifractality. [ABSTRACT FROM AUTHOR]

There is only very limited data examining cardiovascular responses in real-world endurance training/competition. The present study examines the influence of a marathon race on non-linear dynamics of heart rate (HR) variability (HRV). Eleven male recreational runners performed a self-paced marathon road race on an almost flat profile. During the race, heart rate and beat-to-beat (RR) intervals were recorded continuously. Besides HRV time-domain measurements, fractal correlation properties using short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA-alpha1) were calculated. The mean finishing time was 3:10:22 ± 0:17:56 h:min:s with a blood lactate concentration of 4.04 ± 1.12 mmol/L at the end of the race. Comparing the beginning to the end segment of the marathon race (Begin vs. End) significant increases could be found for km split time (p < .001, d = .934) and for HR (p = .010, d = .804). Significant decreases could be found for mean RR (p = .013, d = .798) and DFA-alpha1 (p = .003, d = 1.132). DFA-alpha1 showed an appropriate dynamic range throughout the race consisting of both uncorrelated and anti-correlated values. Lactate was consistent with sustained high intensity exercise when measured at the end of the event. Despite the runners slowing after halfway, DFA-alpha1 continued to fall to values seen in the highest intensity domain during incremental exercise testing in agreement with lactate assessment. Therefore, the discrepancy between the reduced running pace with that of the decline of DFA-alpha1, demonstrate the benefit of using this dimensionless HRV index as a biomarker of internal load during exercise over the course of a marathon race. [ABSTRACT FROM AUTHOR]

Studies on entomopter's performance under the influence of gust have received impetus in the past decade. There exists a dire need to ascertain the threshold of the frontal gusty conditions which would destabilize these anthropogenic flyers. This would help to devise methods to mitigate the detrimental effects of gust. In light of this aspect, the present study aims at analyzing the onsets of instability in a flapping wing system subjected to temporal gust by employing recurrence period density entropy (RPDE) and detrended fluctuation analysis (DFA). Simulation of the flapping wing along inclined stroke is carried out for a Reynolds number of 150. This Reynolds number lies in the typical operating regime of fruit flies and entomopters like the Pico aerial vehicle. Numerical simulations are carried out to solve the laminar, unsteady, and incompressible Navier–Stokes equations. The dynamic meshing technique is employed to model flapping kinematics. Nine gusts with a combination of frequency and velocity ratios of 0.1, 0.5, and 1.0 are considered. Instantaneous horizontal and vertical forces are estimated. Time series of these forces are analyzed using RPDE and DFA paradigms. These analyses indicate that gust frequency of an order of magnitude higher than flapping frequency and gust amplitude of the order of magnitude as the wing's root mean square velocity induces a possible onset of instability. [ABSTRACT FROM AUTHOR]

Ion channels are transport proteins present in the lipid bilayers of biological membranes. They are involved in many physiological processes, such as the generation of nerve impulses, hormonal secretion, and heartbeat. Conformational changes in the ion channel-forming protein allow the opening or closing of pores to control the ionic flux through the cell membranes. The opening and closing of the ion channel have been classically treated as a random kinetic process, known as a Markov process. Here the time the channel remains in a given state is assumed to be independent of the condition it had in the previous state. More recently, however, several studies have shown that this process is not random but a deterministic one, where both the open and closed dwell-times and the ionic current flowing through the channel are history-dependent. This property is called long memory or long-range correlation. However, there is still much controversy regarding how this memory originates, which region of the channel is responsible for this property, and which models could best reproduce the memory effect. In this article, we provide a review of what is, where it is, its possible origin, and the mathematical methods used to analyze the long-term memory present in the kinetic process of ion channels. [ABSTRACT FROM AUTHOR]

The driving mechanism of Arctic amplification (AA) is so complex that no consistent and definitive conclusion has been formed yet. In particular, the internally and externally driven trends of AA have not been distinguished using observation-based methods. Given that the Arctic is more sensitive than other regions to anthropogenic greenhouse gas increases and other external forcings, we focus on separating anthropogenically driven trends from the Arctic surface air temperature (SAT) changes during 1979–2017 to quantify the contribution of anthropogenic effects on AA, with detection and attribution converted to probability distribution functions. Results indicate that the Arctic coast of the Siberian Great Plains, from the Barents Sea to the Kara Sea and eastward to the Bering Strait, has been warming most significantly, and is mainly dominated by anthropogenically driven trends. From 1979 to 2017, the minimum anthropogenically driven warming in most parts of the Arctic Ocean exceeds 2℃, especially the Kara Sea area, where the anthropogenically driven warming is significant, reaching 4℃. In addition, the minimum anthropogenic contributions exceed 60% in most parts of the Arctic Circle and are more than 80% of the warming trend in (75–90° N, 150–180° W). In 140° W–140° E Arctic region, the anthropogenically driven trend is the most remarkable, at 0.82℃ / decade, accounting for 84.5% of the measured warming trend. Meanwhile, the anthropogenically driven trend accelerates most rapidly in this area (0–140° W, 60–90° N). [ABSTRACT FROM AUTHOR]

Functional connectivity, quantified by phase synchrony, between brain regions is known to be aberrant in patients with autism spectrum disorder (ASD). Here, we evaluated the long-range temporal correlations of time-varying phase synchrony (TV-PS) of electrocortical oscillations in patients with ASD as well as typically developing people using detrended fluctuation analysis (DFA) after validating the scale-invariance of the TV-PS time series. By comparing the DFA exponents between the two groups, we found that those of the TV-PS time series of high-gamma oscillations were significantly attenuated in patients with ASD. Furthermore, the regions involved in aberrant TV-PS time series were mainly within the social ability and cognition-related cortical networks. These results support the notion that abnormal social functions observed in patients with ASD may be caused by the highly volatile phase synchrony states of electrocortical oscillations. [ABSTRACT FROM AUTHOR]

The lymphatic system of the brain meninges and head plays a crucial role in the clearance of amyloid-β protein (Aβ), a peptide thought to be pathogenic in Alzheimer's disease (AD), from the brain. The development of methods to modulate lymphatic clearance of Aβ from the brain coild be a revolutionary step in the therapy of AD. The opening of the blood–brain barrier (OBBB) by focused ultrasound is considered as a possible tool for stimulation of clearance of Aβ from the brain of humans and animals. Here, we propose an alternative method of noninvasive music-induced OBBB that is accompanied by the activation of clearance of fluorescent Aβ (Fαβ) from the mouse brain. Using confocal imaging, fluorescence microscopy, and magnetic resonance tomography, we clearly demonstrate that OBBB by music stimulates the movement of Fαβ and Omniscan in the cerebrospinal fluid and lymphatic clearance of Fαβ from the brain. We propose the extended detrended fluctuation analysis (EDFA) as a promising method for the identification of OBBB markers in the electroencephalographic (EEG) patterns. These pilot results suggest that music-induced OBBB and the EDFA analysis of EEG can be a noninvasive, low-cost, labeling-free, clinical perspective and completely new approach for the treatment and monitoring of AD. [ABSTRACT FROM AUTHOR]

Summary : Among 1299 older adults with 24-h Holter monitoring data at baseline, followed for approximately 15 years, 190 incident hip fractures occurred. Increased heart rate variability was independently associated with reduced risk of hip fracture among female participants. Purpose: Autonomic nervous system function modulates bone remodeling in rodent osteoporosis models. We tested whether cardiovascular autonomic function is associated with hip fracture risk in humans. Methods: Participants were 1299 subjects from the Cardiovascular Health Study (mean age 72.8 years). Eight heart rate variability (HRV) measures (time and frequency domains, detrended fluctuation analysis variables, and heart rate turbulence) were derived from 24-h Holter monitor scans in sinus rhythm. Median follow-up for incident hip fracture was 14.7 years [IQR 9.1, 20.2]. Cox proportional hazards models were used to calculate hazard ratios (95% confidence intervals, CI). Results: There were 144 hip fractures among 714 women (1.31 [1.06, 1.61] per 100-person years) and 46 among 585 men (0.62 [0.43, 0.90] per 100 person-years). From among HRV variables examined, a one standard deviation (SD) higher variation between normal heart beats over 24 h (the SD of NN intervals [SDNN]) was associated with a multivariable-adjusted lower hip fracture risk (HR = 0.80; 95% CI 0.65–0.99; p = 0.04) in women. The adjusted association between very low frequency power, and hip fracture was borderline statistically significant in women (HR = 0.82; 95% CI, 0.66–1.00; p = 0.06). When the 8 HRV variables were considered conjointly and adjusted for each other's association with hip fracture risk, a 1 SD higher SDNN value was significantly associated with reduced hip fracture risk in women (HR 0.74; 95% CI, 0.50–0.99; p = 0.05). No HRV variables were associated with hip fracture in men. Conclusions: In older women, increased heart rate variation is associated with hip fracture risk. [ABSTRACT FROM AUTHOR]

Functional connectivity, quantified by phase synchrony, between brain regions is known to be aberrant in patients with autism spectrum disorder (ASD). Here, we evaluated the long-range temporal correlations of time-varying phase synchrony (TV-PS) of electrocortical oscillations in patients with ASD as well as typically developing people using detrended fluctuation analysis (DFA) after validating the scale-invariance of the TV-PS time series. By comparing the DFA exponents between the two groups, we found that those of the TV-PS time series of high-gamma oscillations were significantly attenuated in patients with ASD. Furthermore, the regions involved in aberrant TV-PS time series were mainly within the social ability and cognition-related cortical networks. These results support the notion that abnormal social functions observed in patients with ASD may be caused by the highly volatile phase synchrony states of electrocortical oscillations.

Study region: Central eastern continental United States. Study focus: Groundwater level prediction is of great significance for the management of global water resources. Recently, machine learning, which can deal with highly nonlinear interactions among complex hydrological factors, has been widely applied to groundwater level prediction. However, previous studies mainly focused on improving the simulation performance in specific regions using different machine learning methods, while this study focused on the impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning. New hydrological insights for the region: A gated recurrent unit (GRU) neural network was built for groundwater level simulation in 78 catchments in the study region, and principal component analysis was used to cluster a variety of catchment hydrological variables and determine the input variables for the GRU model. Detrended fluctuation analysis was applied to analyze the autocorrelation of groundwater level in each catchment. This study further explored the influences of the hydrogeological properties of different catchments and the autocorrelation of groundwater levels on machining learning simulations. The results showed that the GRU model performed better in regions where hydrogeological properties could promote more effective responses of groundwater to external changes. Moreover, a negative correlation between the simulation performance of machine learning and the autocorrelation of the groundwater level was found.

Abstract Detrended fluctuation analysis and detrended cross-correlation analysis are used in this study to identify and characterize correlated data. The objective of these two techniques is to separate different fluctuations from the contributions due to external trends by evaluating the autocorrelation and cross-correlation exponents, in order to determine if scale properties persist with the size of the series. Two new methodologies were extended from cross-correlation coefficients for local analysis, which we call the \textit{automatic search procedure

Rangeland ecosystems comprise more than a third of the global land surface, sustaining essential ecosystem services and livelihoods. In Spain, Southeast Spain includes some of the driest regions; accordingly, rangelands from Murcia and Almeria provinces were selected for this study. We used time series metrics and the Hurst Exponent from rescale range and detrended fluctuation analysis to cluster different rangeland dynamics to classify temporally and spatially diverse rangelands. The metrics were only calculated for three time periods that showed significant NDVI changes: March to April, April to July, and September to December. Detrended fluctuation analysis was not previously employed to cluster vegetation. This study used it to improve rangeland classification. K-means and unsupervised random forest were used to cluster the pixels using time series metrics and Hurst exponents. The best clustering results were obtained when unsupervised random forest was used with the Hurst exponent calculated with detrended fluctuation analysis. We used the Silhouette Index to evaluate the clustering results and a spatial comparison with topographical data. Our results show that adding the Hurst exponent, calculated with detrended fluctuation analysis, provided a better classification when clustering NDVI time series, while classifications without the Hurst exponent or with the Hurst exponent calculated with the rescale range method showed lower silhouette values. Overall, this shows the importance of using detrending when calculating the Hurst exponent on vegetation time series, and its usefulness in studying rangeland dynamics for management and research.

The aim of this study was to examine (1) the temporal structures of variation in rowers' (natural) ergometer strokes to make inferences about the underlying motor organization, and (2) the relation between these temporal structures and skill level. Four high-skilled and five lower-skilled rowers completed 550 strokes on a rowing ergometer. Detrended Fluctuation Analysis was used to quantify the temporal structure of the intervals between force peaks. Results showed that the temporal structure differed from random, and revealed prominent patterns of pink noise for each rower. Furthermore, the high-skilled rowers demonstrated more pink noise than the lower-skilled rowers. The presence of pink noise suggests that rowing performance emerges from the coordination among interacting component processes across multiple time scales. The difference in noise pattern between high-skilled and lower-skilled athletes indicates that the complexity of athletes' motor organization is a potential key characteristic of elite performance. [ABSTRACT FROM AUTHOR]

Complex systems typically demonstrate a mixture of regularity and flexibility in their behavior, which would make them adaptive. At the same time, adapting to perturbations is a core characteristic of resilience. The first aim of the current research was therefore to test the possible relation between complexity and resilient motor performance (i.e., performance while being perturbed). The second aim was to test whether complexity and resilient performance improve through differential learning. To address our aims, we designed two parallel experiments involving a motor task, in which participants moved a stick with their non-dominant hand along a slider. Participants could score points by moving a cursor as fast and accurately as possible between two boxes, positioned on the right- and left side of the screen in front of them. In a first session, we determined the complexity by analyzing the temporal structure of variation in the box-to-box movement intervals with a Detrended Fluctuation Analysis. Then, we introduced perturbations to the task: We altered the tracking speed of the cursor relative to the stick-movements briefly (i.e., 4 s) at intervals of 1 min (Experiment 1), or we induced a prolonged change of the tracking speed each minute (Experiment 2). Subsequently, participants had three sessions of either classical learning or differential learning. Participants in the classical learning condition were trained to perform the ideal movement pattern, whereas those in the differential learning condition had to perform additional and irrelevant movements. Finally, we conducted a posttest that was the same as the first session. In both experiments, results showed moderate positive correlations between complexity and points scored (i.e., box touches) in the perturbation-period of the first session. Across the two experiments, only differential learning led to a higher complexity index (i.e., more prominent patterns of pink noise) from baseline to post-test. Unexpectedly, the classical learning group improved more in their resilient performance than the differential learning group. Together, this research provides empirical support for the relation between complexity and resilience, and between complexity and differential learning in human motor performance, which should be examined further.

Complex systems typically demonstrate a mixture of regularity and flexibility in their behavior, which would make them adaptive. At the same time, adapting to perturbations is a core characteristic of resilience. The first aim of the current research was therefore to test the possible relation between complexity and resilient motor performance (i.e., performance while being perturbed). The second aim was to test whether complexity and resilient performance improve through differential learning. To address our aims, we designed two parallel experiments involving a motor task, in which participants moved a stick with their non-dominant hand along a slider. Participants could score points by moving a cursor as fast and accurately as possible between two boxes, positioned on the right- and left side of the screen in front of them. In a first session, we determined the complexity by analyzing the temporal structure of variation in the box-to-box movement intervals with a Detrended Fluctuation Analysis. Then, we introduced perturbations to the task: We altered the tracking speed of the cursor relative to the stick-movements briefly (i.e., 4 s) at intervals of 1 min (Experiment 1), or we induced a prolonged change of the tracking speed each minute (Experiment 2). Subsequently, participants had three sessions of either classical learning or differential learning. Participants in the classical learning condition were trained to perform the ideal movement pattern, whereas those in the differential learning condition had to perform additional and irrelevant movements. Finally, we conducted a posttest that was the same as the first session. In both experiments, results showed moderate positive correlations between complexity and points scored (i.e., box touches) in the perturbation-period of the first session. Across the two experiments, only differential learning led to a higher complexity index (i.e., more prominent patterns of pink noise) from baseline to post-test. Unexpectedly, the classical learning group improved more in their resilient performance than the differential learning group. Together, this research provides empirical support for the relation between complexity and resilience, and between complexity and differential learning in human motor performance, which should be examined further. [ABSTRACT FROM AUTHOR]

The respiratory system reacts instantaneously to intrinsic and extrinsic inputs. This adaptability results in significant fluctuations in breathing parameters, such as respiratory rate, tidal volume, and inspiratory flow profiles. Breathing variability is influenced by several conditions, including sleep, various pulmonary diseases, hypoxia, and anxiety disorders. Recent studies have suggested that weaning failure during mechanical ventilation may be predicted by low respiratory variability. This review describes methods for quantifying breathing variability, summarises the conditions and comorbidities that affect breathing variability, and discusses the potential implications of breathing variability for anaesthesia and intensive care. [ABSTRACT FROM AUTHOR]

The extraction of effective signals of microseismic event caused by water inrush in tunnels is an important prerequisite for accurate location of event. In this paper, based on the characteristics of the main frequency components of the tunnel water burst signal, the variational modal method (VMD) with good frequency decomposition ability is adopted to effectively extract the high-frequency components of the water inrush signal. The effective modes are identified by the method of detrend analysis, and the modal decomposition number K is determined. At the same time, the boundary between noise and effective signal is determined. The method proposed in this paper solves the problem that noise in some modal components is mixed with effective signals, and further improves the ability of extracting weak high-frequency signals. Through the analysis of case data, the validity of the research results in this paper is verified, and an effective signal extraction method of tunnel water inrush microseismic event based on VMD-DFA principle is formed. [ABSTRACT FROM AUTHOR]

The main objective of the study was to develop a low-cost, non-invasive diagnostic model for the early prediction of T2DM risk and validation of this model on patients. The model was designed based on the machine learning classification technique using non-linear Heart rate variability (HRV) features. The electrocardiogram of the healthy subjects (n = 35) and T2DM subjects (n = 100) were recorded in the supine position for 15 min, and HRV features were extracted. The significant non-linear HRV features were identified through statistical analysis. It was found that Poincare plot features (SD1 and SD2) can differentiate the T2DM subject data from healthy subject data. Several machine learning classifiers, such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis, Naïve Bayes, and Gaussian Process Classifier (GPC), have classified the data based on the cross-validation approach. A GP classifier was implemented using three kernels, namely radial basis, linear, and polynomial kernel, considering the ability to handle the non-linear data. The classifier performance was evaluated and compared using performance metrics such as accuracy(AC), sensitivity(SN), specificity(SP), precision(PR), F1 score, and area under the receiver operating characteristic curve(AUC). Initially, all non-linear HRV features were selected for classification, but the specificity of the model was the limitation. Thus, only two Poincare plot features were used to design the diagnostic model. Our diagnostic model shows the performance using GPC based linear kernel as AC of 92.59%, SN of 96.07%, SP of 81.81%, PR of 94.23%, F1 score of 0.95, and AUC of 0.89, which are more extensive compared to other classification models. Further, the diagnostic model was deployed on the hardware module. Its performance on unknown/test data was validated on 65 subjects (healthy n = 15 and T2DM n = 50). Considering the desirable performance of the diagnostic model, it can be used as an initial screening test tool for a healthcare practitioner to predict T2DM risk. [ABSTRACT FROM AUTHOR]

• DFA of MEG data may aid in the identification of the epileptogenic zone. • Aberrant LRTC correlates with resection area and interictal epileptiform discharges. • Relationship between aberrant LRTCs and resection area showed etiological dependence. To examine the usability of long-range temporal correlations (LRTCs) in non-invasive localization of the epileptogenic zone (EZ) in refractory parietal lobe epilepsy (RPLE) patients. We analyzed 10 RPLE patients who had presurgical MEG and underwent epilepsy surgery. We quantified LRTCs with detrended fluctuation analysis (DFA) at four frequency bands for 200 cortical regions estimated using individual source models. We correlated individually the DFA maps to the distance from the resection area and from cortical locations of interictal epileptiform discharges (IEDs). Additionally, three clinical experts inspected the DFA maps to visually assess the most likely EZ locations. The DFA maps correlated with the distance to resection area in patients with type II focal cortical dysplasia (FCD) (p < 0.05), but not in other etiologies. Similarly, the DFA maps correlated with the IED locations only in the FCD II patients. Visual analysis of the DFA maps showed high interobserver agreement and accuracy in FCD patients in assigning the affected hemisphere and lobe. Aberrant LRTCs correlate with the resection areas and IED locations. This methodological pilot study demonstrates the feasibility of approximating cortical LRTCs from MEG that may aid in the EZ localization and provide new non-invasive insight into the presurgical evaluation of epilepsy. [ABSTRACT FROM AUTHOR]

Control system performance assessment is significant, especially in practical applications. One of the most important indices for the performance assessment of control systems is minimum variance. Calculating the minimum variance index in multivariate systems requires prior knowledge of system parameters and models, and is therefore an obstacle in practical applications. In this article, an index is proposed for the performance assessment of multivariate control loops, evaluating the system performance with the minimum variance criterion and using only the system's routine operation data. This index can quantify the performance using neither any prior knowledge of system parameters nor the system's optimal operation data. The proposed index is based on the Hurst exponent, a parameter for measuring correlations in time series data. In this article, detrended fluctuation analysis and rescaled range analysis are used to estimate the Hurst exponents of system outputs. Using a combination of these Hurst exponents, an index is defined for the performance assessment of multivariate systems. The results of simulation examples illustrate that the proposed index can assess the performance efficiently. [ABSTRACT FROM AUTHOR]

The conventional extended empirical wavelet transform (EEWT) proposed recently is intended to decompose multivariate signals with clear peaks in power spectra without considering the cases where the signals contain high noise levels. Even when dealing with signals with distinct peaks, the EEWT method can still encounter challenges in properly decomposing the signals. However, plant-wide data from industrial control loops, including controllers' outputs, process variables, and manipulated variables, are commonly corrupted by high levels of noise, which can be introduced at various stages of data acquisition, transmission, and processing within the control system. To address these limitations and ensure the applicability of the EEWT to real-world industrial data with diverse and challenging characteristics, this paper presents an improved version called the improved extended empirical wavelet transform (IEEWT). The IEEWT incorporates noise-reduced power spectra and detrended fluctuation analysis techniques to enhance the decomposition. The proposed method demonstrates accurate multivariate data decomposition for both simulated and real data sets, surpassing the limitations associated with the conventional EEWT. • IEEWT tackles the challenge of high noise levels in signals, enabling accurate decomposition. • By incorporating noise-reduced power spectra, IEEWT enhances the reliability of peak detection. • The use of detrended fluctuation analysis in IEEWT improves the identification of relevant peaks. • IEEWT demonstrates accurate multivariate data decomposition for both simulated and real data sets. • The proposed IEEWT overcomes the limitations of conventional EEWT, ensuring reliable results. [ABSTRACT FROM AUTHOR]