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We analyze and model the stochastic behavior of paleoclimate time series and assess the implications for the coupling of climate variables during the Pleistocene glacial cycles. We examine 800 kiloyears of carbon dioxide, methane, nitrous oxide, and temperature proxy data from the European Project for Ice Coring in Antarctica (EPICA) Dome-C ice core, which are characterized by 100 ky glacial cycles overlain by fluctuations across a wide range of timescales. We quantify this behavior through multifractal time-weighted detrended fluctuation analysis, which distinguishes near-red-noise and white-noise behavior below and above the 100 ky glacial cycle, respectively, in all records. This allows us to model each time series as a one-dimensional periodic nonautonomous stochastic dynamical system, and assess the stability of physical processes and the fidelity of model-simulated time series. We extend this approach to a four-variable model with intervariable coupling terms, which we interpret in terms of possible interrelationships among the four time series. Within the framework of our coupling coefficients, we find that carbon dioxide and temperature act to stabilize each other and methane and nitrous oxide, whereas the latter two destabilize each other and carbon dioxide and temperature. We also compute the response function for each pair of variables to assess the model performance by comparison to the data and confirm the model predictions regarding stability amongst variables. Taken together, our results are consistent with glacial pacing dominated by carbon dioxide and temperature that is modulated by terrestrial biosphere feedbacks associated with methane and nitrous oxide emissions.

The most recent fall of the Silicon Valley (SVB) and Credit Suisse (CS) banks increased the fear of a worldwide banking crisis. We analyse the impacts of their fall on five financial indices. We apply detrended fluctuation analysis, static and with sliding windows. We find a higher impact of the SVB fall on the efficiency dynamic of the studied indices, which revealed fluctuating efficiency and a loss of efficiency during the period of the falls. The fall of both banks contributed to some persistence in stock indices returns. The Nasdaq and STOXX Europe 600 Banks are the most and the least efficient indices, respectively. Despite the apparent evidence of inefficiency, it might not necessarily mean a capacity for abnormal profits.

This paper explores the impact of different crises on the informational efficiency of financial assets. The study covers stock markets indices (ASX200, DAX30, EuroStoxx50, S&P500 and Nikkei), commodities (gold and oil) and volatility (VIX). The study analyzes, using a rolling window method, the long memory profile and the multifractality of the time series by means of the DFA and generalized Hurst exponents. This dynamic analysis is important as it uncovers the time-varying behavior of returns characteristics, affecting the investment decisions and trading strategies at different moments of time. The paper extends the current literature on informational efficiency, providing evidence of the distinct impact on the long memory and on the multifractality of the time series, depending on the nature of the crisis and the market. The results could be of interest for investors as well as for academics, regarding the hedging limits of the models during calm or turbulent times.

Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems' persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work.

Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems' persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work.

Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems' persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work.

We investigate the behaviour of retail diesel prices in Brazil using fractional integration, with weekly data from January 2010 to April 2020. In this period, we have 3 episodes of relevant economic implications in the country under analysis: i) impeachment of the Brazilian President; ii) the lorry-drivers’ strike; iii) the rise of the global Covid-19 epidemic. We use a sliding windows approach to analyze price dynamics over time. The results suggest that, at the beginning of the sample, prices were non-stationary and non-mean reverting but over the time results changed in the different regions. Results are relevant for market agents and policy-makers, as it can be inferred whether exogenous shocks are temporary, despite taking some time to dissipate completely.

We investigate the behaviour of retail diesel prices in Brazil using fractional integration, with weekly data from January 2010 to April 2020. In this period, we have 3 episodes of relevant economic implications in the country under analysis: i) impeachment of the Brazilian President; ii) the lorry-drivers’ strike; iii) the rise of the global Covid-19 epidemic. We use a sliding windows approach to analyze price dynamics over time. The results suggest that, at the beginning of the sample, prices were non-stationary and non-mean reverting but over the time results changed in the different regions. Results are relevant for market agents and policy-makers, as it can be inferred whether exogenous shocks are temporary, despite taking some time to dissipate completely.

Infantile spasms (IS) is a potentially catastrophic epileptic encephalopathy that typically presents in children six to twelve months of age and is characterized by clusters of seizures consisting of abrupt muscle spasms. IS is associated with a host of comorbidities, high mortality rates, debilitating neurocognitive stagnation and psychomotor delay, and often progresses to other highly refractory forms of epilepsy. Diagnosis and clinical treatment decisions in IS are difficult due to the wide range of underlying etiologies and concomitant epilepsies. Patients with IS also exhibit a broad spectrum of electroencephalographic (EEG) morphologies, including a disorganized, high-amplitude pattern called hypsarrhythmia. Additionally, although the presence of hypsarrhythmia is often used as a diagnostic criterion for IS, there is low inter-rater reliability for identification of the pattern, and it is not a strong predictor of outcome. This lack of diagnostic accuracy and inefficient treatment response evaluation can delay successful treatment, which is associated with worse long-term outcomes. Computational EEG biomarkers of IS that are independent of the presence of hypsarrhythmia could supplement standard visual inspection of the EEG and enable objective identification of the disease and assessment of treatment response. We quantified basic characteristics of the pre-treatment EEG signal such as the amplitude, power spectrum, and Shannon entropy in cohorts of IS patients both with and without hypsarrhythmia. We identified significant differences between IS patients and age-matched control subjects, and these differences were robust to the presence of hypsarrhythmia. We also investigated the strength of long-range temporal correlations in the EEG with detrended fluctuation analysis (DFA) and developed statistical methods to infer confidence intervals for this metric. DFA differentiated IS patients from control subjects and reflected treatment response in post-treatment d

Following the new advances in encryption and network computing, Bitcoin emerged as a private sector system facilitating peer-to-peer exchange via distributed ledgers based on blockchains, driving a transformational change towards a global economy outside the core financial system. The main purpose of this paper is to examine the multifractal properties of the Bitcoin price using high frequency data. The methods used are the wavelet transform modulus maxima and the multifractal detrended fluctuation analysis. The results indicate that Bitcoin exhibits a large degree of multifractality in all examined time intervals, and the main source of multifractality is attributed to the high kurtosis and the fat distributional tails of the series returns. (C) 2019 Elsevier B.V. All rights reserved.

Following the new advances in encryption and network computing, Bitcoin emerged as a private sector system facilitating peer-to-peer exchange via distributed ledgers based on blockchains, driving a transformational change towards a global economy outside the core financial system. The main purpose of this paper is to examine the multifractal properties of the Bitcoin price using high frequency data. The methods used are the wavelet transform modulus maxima and the multifractal detrended fluctuation analysis. The results indicate that Bitcoin exhibits a large degree of multifractality in all examined time intervals, and the main source of multifractality is attributed to the high kurtosis and the fat distributional tails of the series returns. (C) 2019 Elsevier B.V. All rights reserved.

Infantile spasms (IS) is a potentially catastrophic epileptic encephalopathy that typically presents in children six to twelve months of age and is characterized by clusters of seizures consisting of abrupt muscle spasms. IS is associated with a host of comorbidities, high mortality rates, debilitating neurocognitive stagnation and psychomotor delay, and often progresses to other highly refractory forms of epilepsy. Diagnosis and clinical treatment decisions in IS are difficult due to the wide range of underlying etiologies and concomitant epilepsies. Patients with IS also exhibit a broad spectrum of electroencephalographic (EEG) morphologies, including a disorganized, high-amplitude pattern called hypsarrhythmia. Additionally, although the presence of hypsarrhythmia is often used as a diagnostic criterion for IS, there is low inter-rater reliability for identification of the pattern, and it is not a strong predictor of outcome. This lack of diagnostic accuracy and inefficient treatment response evaluation can delay successful treatment, which is associated with worse long-term outcomes. Computational EEG biomarkers of IS that are independent of the presence of hypsarrhythmia could supplement standard visual inspection of the EEG and enable objective identification of the disease and assessment of treatment response. We quantified basic characteristics of the pre-treatment EEG signal such as the amplitude, power spectrum, and Shannon entropy in cohorts of IS patients both with and without hypsarrhythmia. We identified significant differences between IS patients and age-matched control subjects, and these differences were robust to the presence of hypsarrhythmia. We also investigated the strength of long-range temporal correlations in the EEG with detrended fluctuation analysis (DFA) and developed statistical methods to infer confidence intervals for this metric. DFA differentiated IS patients from control subjects and reflected treatment response in post-treatment d

Human movement is inherently complex, requiring the control and coordination of many neurophysiological and biomechanical degrees-of-freedom, and the extent to which individuals exhibit variation in their movement patterns is captured by the construct of motor variability (MV). MV is being used increasingly to describe movement quality and function among clinical populations and elderly individuals. However, current evidence presents conflicting views on whether increased MV offers benefits or is a hindrance to performance. To better understand the utility of MV for performance prediction, we focused on current research needs in the military domain. Dismounted soldiers, in particular, are expected to perform at a high level in complex environments and under demanding physical conditions. Hence, it is critical to understand what strategies allow soldiers to better adapt to fatigue and diverse environmental factors, and to develop predictive tools for estimating changes in soldier performance. Different aspects of performance such as motor learning, experience, and adaptability to fatigue were investigated when soldiers performed various gait tasks, and gait variability (GV) was quantified using four different types of measures (spatiotemporal, joint kinematics, detrended fluctuation analysis, and Lyapunov exponents). During a novel obstacle course task, we found that frontal plane coordination variability of the hip-knee and knee-ankle joint couples exhibited strong association with rate of learning the novel task, explaining 62% of the variance, and higher joint kinematic variability during the swing phase of baseline gait was associated with faster learning rate. In a load carriage task, GV measures were more sensitive than average gait measures in discriminating between experience and load condition: experienced cadets exhibited reduced GV (in spatiotemporal measures and joint kinematics) and lower long-term local dynamic stability at the ankle, compared to the no

Accelerometer data can be analyzed using a variety of methods which are effective in the clinical setting. Time-series analysis is used to analyze spatiotemporal variables in various populations. More recently, investigators have focused on gait complexity and the structure of spatiotemporal variations during walking and running. This study evaluated the use of time-series analyses to determine gait parameters during running. Subjects were college-age female soccer players. Accelerometer data were collected using GPS-embedded trunk-mounted accelerometers. Customized Matlab® programs were developed that included Gaussian continuous wavelet transform (CWT) to determine spatiotemporal characteristics, detrended fluctuation analysis (DFA) to examine gait complexity and autocorrelation analyses (ACF) to assess gait regularity. Reliability was examined using repeated running efforts and intraclass correlation. Proof of concept was determined by examining differences in each variable between various running speeds. Applicability was established by examining gait before and after fatiguing activity. The results showed most variables had excellent reliability. Test-retest R2 values for these variables ranged from 0.8 to 1.0. Low reliability was seen in bilateral comparisons of gait symmetry. Increases in running speed resulted in expected changes in spatiotemporal and acceleration variables. Fatiguing exercise had minimal effects on spatiotemporal variables but resulted in noticeable declines in complexity. This investigation shows that GPS-embedded trunk-mounted accelerometers can be effectively used to assess running gait. CWT and DFA yield reliable measures of spatiotemporal characteristics of gait and gait complexity. The effects of running speed and fatigue on these variables provides proof of concepts and applicability for this analytical approach.

Aging is associated with changes in balance control and elderly take longer to adapt to changing sensory conditions, which may increase falls risk. Low amplitude calf muscle vibration stimulates local sensory afferents/receptors and affects sense of upright when applied in stance. It has been used to assess the extent the nervous system relies on calf muscle somatosensory information and to rapidly change/perturb part of the somatosensory information causing balance unsteadiness by addition and removal of the vibratory stimulus. This study assessed the effect of addition and removal of calf vibration on balance control (in the absence of vision) in elderly individuals (> 65 years, n = 99) who did (n = 41) or did not prospectively report falls (n = 58), and in a group of young individuals (18-25 years, n = 23). Participants stood barefoot and blindfolded on a force plate for 135 s. Vibrators (60 Hz, 1 mm) attached bilaterally over the triceps surae muscles were activated twice for 15 s; after 15 and 75 s (45 s for recovery). Balance measures were applied in a windowed (15 s epoch) manner to compare center-of-pressure (CoP) motion before, during and after removal of calf vibration between groups. In each epoch, CoP motion was quantified using linear measures, and non-linear measures to assess temporal structure of CoP motion [using recurrence quantification analysis (RQA) and detrended fluctuation analysis]. Mean CoP displacement during and after vibration did not differ between groups, which suggests that calf proprioception and/or weighting assigned by the nervous system to calf proprioception was similar for the young and both groups of older individuals. Overall, compared to the elderly, CoP motion of young was more predictable and persistent. Balance measures were not different between fallers and non-fallers before and during vibration. However, non-linear aspects of CoP motion of fallers and non-fallers differed after removal of vibration, when dynamic re

Aging is associated with changes in balance control and elderly take longer to adapt to changing sensory conditions, which may increase falls risk. Low amplitude calf muscle vibration stimulates local sensory afferents/receptors and affects sense of upright when applied in stance. It has been used to assess the extent the nervous system relies on calf muscle somatosensory information and to rapidly change/perturb part of the somatosensory information causing balance unsteadiness by addition and removal of the vibratory stimulus. This study assessed the effect of addition and removal of calf vibration on balance control (in the absence of vision) in elderly individuals (>65 years, n = 99) who did (n = 41) or did not prospectively report falls (n = 58), and in a group of young individuals (18–25 years, n = 23). Participants stood barefoot and blindfolded on a force plate for 135 s. Vibrators (60 Hz, 1 mm) attached bilaterally over the triceps surae muscles were activated twice for 15 s; after 15 and 75 s (45 s for recovery). Balance measures were applied in a windowed (15 s epoch) manner to compare center-of-pressure (CoP) motion before, during and after removal of calf vibration between groups. In each epoch, CoP motion was quantified using linear measures, and non-linear measures to assess temporal structure of CoP motion [using recurrence quantification analysis (RQA) and detrended fluctuation analysis]. Mean CoP displacement during and after vibration did not differ between groups, which suggests that calf proprioception and/or weighting assigned by the nervous system to calf proprioception was similar for the young and both groups of older individuals. Overall, compared to the elderly, CoP motion of young was more predictable and persistent. Balance measures were not different between fallers and non-fallers before and during vibration. However, non-linear aspects of CoP motion of fallers and non-fallers differed after removal of vibra

Aging is associated with changes in balance control and elderly take longer to adapt to changing sensory conditions, which may increase falls risk. Low amplitude calf muscle vibration stimulates local sensory afferents/receptors and affects sense of upright when applied in stance. It has been used to assess the extent the nervous system relies on calf muscle somatosensory information and to rapidly change/perturb part of the somatosensory information causing balance unsteadiness by addition and removal of the vibratory stimulus. This study assessed the effect of addition and removal of calf vibration on balance control (in the absence of vision) in elderly individuals (> 65 years, n = 99) who did (n = 41) or did not prospectively report falls (n = 58), and in a group of young individuals (18-25 years, n = 23). Participants stood barefoot and blindfolded on a force plate for 135 s. Vibrators (60 Hz, 1 mm) attached bilaterally over the triceps surae muscles were activated twice for 15 s; after 15 and 75 s (45 s for recovery). Balance measures were applied in a windowed (15 s epoch) manner to compare center-of-pressure (CoP) motion before, during and after removal of calf vibration between groups. In each epoch, CoP motion was quantified using linear measures, and non-linear measures to assess temporal structure of CoP motion [using recurrence quantification analysis (RQA) and detrended fluctuation analysis]. Mean CoP displacement during and after vibration did not differ between groups, which suggests that calf proprioception and/or weighting assigned by the nervous system to calf proprioception was similar for the young and both groups of older individuals. Overall, compared to the elderly, CoP motion of young was more predictable and persistent. Balance measures were not different between fallers and non-fallers before and during vibration. However, non-linear aspects of CoP motion of fallers and non-fallers differed after removal of vibration, when dynamic re

Motor activity in healthy young humans displays intrinsic fluctuations that are scale-invariant over a wide range of time scales (from minutes to hours). Human postmortem and animal lesion studies showed that the intact function of the suprachiasmatic nucleus (SCN) is required to maintain such scale-invariant patterns. We therefore hypothesized that scale invariance is degraded in patients treated for suprasellar tumors that compress the SCN. To test the hypothesis, we investigated 68 patients with nonfunctioning pituitary macroadenoma and 22 patients with craniopharyngioma, as well as 72 age-matched healthy controls (age range 21.0–70.6 years). Spontaneous wrist locomotor activity was measured for 7 days with actigraphy, and detrended fluctuation analysis was applied to assess correlations over a range of time scales from minutes to 24 h. For all the subjects, complex scale-invariant correlations were only present for time scales smaller than 1.5 h, and became more random at time scales 1.5–10 h. Patients with suprasellar tumors showed a larger decrease in correlations at 1.5–10 h as compared to healthy controls. Within healthy subject, gender and age >33 year were associated with attenuated scale invariance. Conversely, activity patterns at time scales between 10 and 24 h were significantly more regular than all other time scales, and this was mostly associated with age. In conclusion, scale invariance is degraded in healthy subjects at the ages of >33 year as characterized by attenuation of correlations at time scales 1.5–10 h. In addition, scale invariance was more degraded in patients with suprasellar tumors as compared to healthy subjects.

Motor activity in healthy young humans displays intrinsic fluctuations that are scale-invariant over a wide range of time scales (from minutes to hours). Human postmortem and animal lesion studies showed that the intact function of the suprachiasmatic nucleus (SCN) is required to maintain such scale-invariant patterns. We therefore hypothesized that scale invariance is degraded in patients treated for suprasellar tumors that compress the SCN. To test the hypothesis, we investigated 68 patients with nonfunctioning pituitary macroadenoma and 22 patients with craniopharyngioma, as well as 72 age-matched healthy controls (age range 21.0–70.6 years). Spontaneous wrist locomotor activity was measured for 7 days with actigraphy, and detrended fluctuation analysis was applied to assess correlations over a range of time scales from minutes to 24 h. For all the subjects, complex scale-invariant correlations were only present for time scales smaller than 1.5 h, and became more random at time scales 1.5–10 h. Patients with suprasellar tumors showed a larger decrease in correlations at 1.5–10 h as compared to healthy controls. Within healthy subject, gender and age >33 year were associated with attenuated scale invariance. Conversely, activity patterns at time scales between 10 and 24 h were significantly more regular than all other time scales, and this was mostly associated with age. In conclusion, scale invariance is degraded in healthy subjects at the ages of >33 year as characterized by attenuation of correlations at time scales 1.5–10 h. In addition, scale invariance was more degraded in patients with suprasellar tumors as compared to healthy subjects.

© 2016, Springer Science+Business Media New York. This paper investigates the presence of long memory in corporate bond and stock indices of six European Union countries from July 1998 to February 2015. We compute the Hurst exponent by means of the DFA method and using a sliding window in order to measure long range dependence. We detect that Hurst exponents behave differently in the stock and bond markets, being smoother in the stock indices than in the bond indices. We verify that the level of informational efficiency is time-varying. Moreover we find an asymmetric impact of the 2008 financial crisis in the fixed income and the stock markets, affecting the former but not the latter. Similar results are obtained using the R/S method.

The current study incorporates concepts from dynamicalsystems theory (DST) and embodied cognition topropose a novel method of answering traditionalquestions in social psychology. Namely, we wereinterested in understanding postural sway complexityduring the important interpersonal task of disclosing ahidden stigmatized identity (e.g., mental health disorder,history of sexual abuse). Using detrended fluctuationanalysis and multifractal detrended fluctuation analysis,we captured postural activity while people shared theirpersonal secrets to an imagined other. Results suggestthat disclosure context, defined by both disclosureconfidant and antecedent goals, is indeed embodied inour complex postural activity.

Infantile spasms syndrome is an epileptic encephalopathy in which prompt diagnosis and treatment initiation are critical to therapeutic response. Diagnosis of the disease heavily depends on the identification of characteristic electroencephalographic (EEG) patterns, including hypsarrhythmia. However, visual assessment of the presence and characteristics of hypsarrhythmia is challenging because multiple variants of the pattern exist, leading to poor inter-rater reliability. We investigated whether a quantitative measurement of the control of neural synchrony in the EEGs of infantile spasms patients could be used to reliably distinguish the presence of hypsarrhythmia and indicate successful treatment outcomes. We used autocorrelation and Detrended Fluctuation Analysis (DFA) to measure the strength of long-range temporal correlations in 21 infantile spasms patients before and after treatment and 21 control subjects. The strength of long-range temporal correlations was significantly lower in patients with hypsarrhythmia than control patients, indicating decreased control of neural synchrony. There was no difference between patients without hypsarrhythmia and control patients. Further, the presence of hypsarrhythmia could be classified based on the DFA exponent and intercept with 92% accuracy using a support vector machine. Successful treatment was marked by a larger increase in the DFA exponent compared to those in which spasms persisted. These results suggest that the strength of long-range temporal correlations is a marker of pathological cortical activity that correlates with treatment response. Combined with current clinical measures, this quantitative tool has the potential to aid objective identification of hypsarrhythmia and assessment of treatment efficacy to inform clinical decision-making.

Non-linear analysis of electrograms (EGM) has been proposed as a tool to detect critical conduction sites (e.g., rotors vortex, multiple wavefronts) in atrial fibrillation (AF). Likewise, studies have shown that multifractal analysis is useful to detect critical activity in EGM signals. However, the multifractal spectrum does not consider the temporal information. There is a new mathematical formalism to overcome this limitation: the time-singularity multifractal spectrum distribution (TS-MFSD), which involves the time variation of the spectrum. In this manuscript, we describe the methodology to compute the TS-MFSD from EGM signals. Moreover, we propose a methodology to extract features from time-singularity spectrum and from singularity energy spectrum (SES). We tested the features in an EGM database labeled by experts as: non-fragmented, discrete fragmented potentials, disorganized activity, and continuous activity. We tested the area under the receiver operating characteristic (ROC) curve. The proposed features achieve an area under the ROC curve of 95.17% when detecting signals with continuous activity. These results outperform those reported using multifractal analysis. To our knowledge, this is the first work that report the use of TS-MFSD in biomedical signals and our findings suggest that time-singularity has the potential to be used in the study of non-stationary behavior of EGM signals in AF., El análisis de la dinámica no lineal de señales de Electrogramas Intracardiacos (EGM) ha sido propuesto como una herramienta para detectar sitios críticos de conducción eléctrica (ejm: rotores o múltiples frentes de onda) en fibrilación auricular (AF). Estudios previos han mostrado que el análisis multifractal puede ser de utilidad para detectar actividad crítica en la señal EGM. A pesar de esto, el análisis multifractal no considera la información temporal de la señal. Existe un nuevo formalismo matemático para superar esta limitación, el cual es llamado Distribución Tiempo-Singularidad del Espectro Multifractal (TS-MFSD), que involucra la variación en el tiempo del espectro. Este artículo describe una nueva metodología para calcular características a partir del TS-MFSD en señales EGM. Nosotros evaluamos los métodos descritos en una base de datos de EGM etiquetada por expertos en cuatro clases: no fragmentada, potenciales fragmentados discretos, actividad desorganizada y actividad continua. Para evaluar el rendimiento se calculó el área bajo la curva ROC. El mejor resultado de las características propuestas alcanzó un área bajo la curva ROC de 95.17% en la detección de señales con actividad continua. Este resultado supera los reportados mediante la utilización del análisis multifractal. Hasta donde sabemos, este es el primer trabajo que reporta la utilización de la TS-MFSD en señales biomédicas, y nuestros resultados sugieren que el análisis Tiempo-Singularidad tiene el potencial para estudiar el comportamiento no estacionario de las señales EGM en AF.

Infantile spasms syndrome is an epileptic encephalopathy in which prompt diagnosis and treatment initiation are critical to therapeutic response. Diagnosis of the disease heavily depends on the identification of characteristic electroencephalographic (EEG) patterns, including hypsarrhythmia. However, visual assessment of the presence and characteristics of hypsarrhythmia is challenging because multiple variants of the pattern exist, leading to poor inter-rater reliability. We investigated whether a quantitative measurement of the control of neural synchrony in the EEGs of infantile spasms patients could be used to reliably distinguish the presence of hypsarrhythmia and indicate successful treatment outcomes. We used autocorrelation and Detrended Fluctuation Analysis (DFA) to measure the strength of long-range temporal correlations in 21 infantile spasms patients before and after treatment and 21 control subjects. The strength of long-range temporal correlations was significantly lower in patients with hypsarrhythmia than control patients, indicating decreased control of neural synchrony. There was no difference between patients without hypsarrhythmia and control patients. Further, the presence of hypsarrhythmia could be classified based on the DFA exponent and intercept with 92% accuracy using a support vector machine. Successful treatment was marked by a larger increase in the DFA exponent compared to those in which spasms persisted. These results suggest that the strength of long-range temporal correlations is a marker of pathological cortical activity that correlates with treatment response. Combined with current clinical measures, this quantitative tool has the potential to aid objective identification of hypsarrhythmia and assessment of treatment efficacy to inform clinical decision-making.

Non-linear analysis of electrograms (EGM) has been proposed as a tool to detect critical conduction sites (e.g., rotors vortex, multiple wavefronts) in atrial fibrillation (AF). Likewise, studies have shown that multifractal analysis is useful to detect critical activity in EGM signals. However, the multifractal spectrum does not consider the temporal information. There is a new mathematical formalism to overcome this limitation: the time-singularity multifractal spectrum distribution (TS-MFSD), which involves the time variation of the spectrum. In this manuscript, we describe the methodology to compute the TS-MFSD from EGM signals. Moreover, we propose a methodology to extract features from time-singularity spectrum and from singularity energy spectrum (SES). We tested the features in an EGM database labeled by experts as: non-fragmented, discrete fragmented potentials, disorganized activity, and continuous activity. We tested the area under the receiver operating characteristic (ROC) curve. The proposed features achieve an area under the ROC curve of 95.17% when detecting signals with continuous activity. These results outperform those reported using multifractal analysis. To our knowledge, this is the first work that report the use of TS-MFSD in biomedical signals and our findings suggest that time-singularity has the potential to be used in the study of non-stationary behavior of EGM signals in AF., El análisis de la dinámica no lineal de señales de Electrogramas Intracardiacos (EGM) ha sido propuesto como una herramienta para detectar sitios críticos de conducción eléctrica (ejm: rotores o múltiples frentes de onda) en fibrilación auricular (AF). Estudios previos han mostrado que el análisis multifractal puede ser de utilidad para detectar actividad crítica en la señal EGM. A pesar de esto, el análisis multifractal no considera la información temporal de la señal. Existe un nuevo formalismo matemático para superar esta limitación, el cual es llamado Distribución Tiempo-Singularidad del Espectro Multifractal (TS-MFSD), que involucra la variación en el tiempo del espectro. Este artículo describe una nueva metodología para calcular características a partir del TS-MFSD en señales EGM. Nosotros evaluamos los métodos descritos en una base de datos de EGM etiquetada por expertos en cuatro clases: no fragmentada, potenciales fragmentados discretos, actividad desorganizada y actividad continua. Para evaluar el rendimiento se calculó el área bajo la curva ROC. El mejor resultado de las características propuestas alcanzó un área bajo la curva ROC de 95.17% en la detección de señales con actividad continua. Este resultado supera los reportados mediante la utilización del análisis multifractal. Hasta donde sabemos, este es el primer trabajo que reporta la utilización de la TS-MFSD en señales biomédicas, y nuestros resultados sugieren que el análisis Tiempo-Singularidad tiene el potencial para estudiar el comportamiento no estacionario de las señales EGM en AF.

It has been shown that statistical persistence in stride intervals characteristics exist during walking, running and cycling and were speed-dependent among healthy young adults. The purpose of this study was to determine if such statistical persistence in stride time interval, stride length and stride speed also exists during self-paced continuous stairmill climbing and if the strength is dependent on stepping rate. Stride time, stride length, and stride speed were collected from nine healthy participants during 3 min of stairmill climbing at 100, 110, and 120% of their preferred stepping rate (PSR) and 5 min of treadmill walking at preferred walking speed (PWS). The amount of variability (assessed by standard deviation and coefficient of variation) and dynamics (assessed by detrended fluctuation analysis and sample entropy) of the stride time, stride length, and stride speed time series were investigated. The amounts of variability were significantly higher during stairmill climbing for the stride time, stride length, and stride speed and did only change with increased stepping rate for stride speed. In addition to a more irregular pattern during stairmill climbing, the detrended fluctuation analysis (DFA) revealed that the stride length fluctuations were statistical anti-persistent for all subjects. On a group level both stride time and stride speed fluctuations were characterized by an uncorrelated pattern which was more irregular compared to that during treadmill walking. However, large inter-participant differences were observed for these two variables. In addition, the dynamics did not change with increase in stepping rate.

It has been shown that statistical persistence in stride intervals characteristics exist during walking, running and cycling and were speed-dependent among healthy young adults. The purpose of this study was to determine if such statistical persistence in stride time interval, stride length and stride speed also exists during self-paced continuous stairmill climbing and if the strength is dependent on stepping rate. Stride time, stride length, and stride speed were collected from nine healthy participants during 3 min of stairmill climbing at 100, 110, and 120% of their preferred stepping rate (PSR) and 5 min of treadmill walking at preferred walking speed (PWS). The amount of variability (assessed by standard deviation and coefficient of variation) and dynamics (assessed by detrended fluctuation analysis and sample entropy) of the stride time, stride length, and stride speed time series were investigated. The amounts of variability were significantly higher during stairmill climbing for the stride time, stride length, and stride speed and did only change with increased stepping rate for stride speed. In addition to a more irregular pattern during stairmill climbing, the detrended fluctuation analysis (DFA) revealed that the stride length fluctuations were statistical anti-persistent for all subjects. On a group level both stride time and stride speed fluctuations were characterized by an uncorrelated pattern which was more irregular compared to that during treadmill walking. However, large inter-participant differences were observed for these two variables. In addition, the dynamics did not change with increase in stepping rate.

A quantitative and objective assessment of background electroencephalograph (EEG) in sick neonates remains an everyday clinical challenge. We studied whether long range temporal correlations quantified by detrended fluctuation analysis (DFA) could be used in the neonatal EEG to distinguish different grades of abnormality in the background EEG activity. Long-term EEG records of 34 neonates were collected after perinatal asphyxia, and their background was scored in 1 h epochs (8 h in each neonate) as mild, moderate or severe. We applied DFA on 15 min long, non-overlapping EEG epochs (n = 1088) filtered from 3 to 8 Hz. Our for

A quantitative and objective assessment of background electroencephalograph (EEG) in sick neonates remains an everyday clinical challenge. We studied whether long range temporal correlations quantified by detrended fluctuation analysis (DFA) could be used in the neonatal EEG to distinguish different grades of abnormality in the background EEG activity. Long-term EEG records of 34 neonates were collected after perinatal asphyxia, and their background was scored in 1 h epochs (8 h in each neonate) as mild, moderate or severe. We applied DFA on 15 min long, non-overlapping EEG epochs (n = 1088) filtered from 3 to 8 Hz. Our formal feasibility study suggested that DFA exponent can be reliably assessed in only part of the EEG epochs, and in only relatively short time scales (10-60 s), while it becomes ambiguous if longer time scales are considered. This prompted further exploration whether paradigm used for quantifying multifractal DFA (MF-DFA) could be applied in a more efficient way, and whether metrics from MF-DFA paradigm could yield useful benchmark with existing clinical EEG gradings. Comparison of MF-DFA metrics showed a significant difference between three visually assessed background EEG grades. MF-DFA parameters were also significantly correlated to interburst intervals quantified with our previously developed automated detector. Finally, we piloted a monitoring application of MF-DFA metrics and showed their evolution during patient recovery from asphyxia. Our exploratory study showed that neonatal EEG can be quantified using multifractal metrics, which might offer a suitable parameter to quantify the grade of EEG background, or to monitor changes in brain state that take place during long-term brain monitoring.

Objective It is generally assumed that motion in motion images is responsible for increased postural sway as well as for visually induced motion sickness (VIMS). However, this has not yet been tested. To that end, we studied postural sway and VIMS induced by motion and still images. Method 15 Participants were exposed to motion- and still images in separate sessions. Motion images consisted of video clips taken from a first person shooter game. Still images consisted of stills taken every 10 s from these same clips. Before, during, and after exposure, VIMS was rated and postural sway was measured. Sway path length, standard deviation and short- and long-term scaling components of the centre of pressure were calculated as measures of postural sway. Results VIMS scores obtained during and after exposure to motion images were significantly higher compared to scores obtained before, and directly after exposure to still images. The sway path length, standard deviation in anteroposterior direction and short-term scaling components in mediolateral and anteroposterior direction increased significantly during exposure to motion and still images. Conclusion In this experiment motion- and still images caused different levels of VIMS, but comparable increases in postural sway. We assume VIMS was caused by a mismatch between visual and vestibular motion cues. The increase in sway during exposure to still images can be explained by visual effects present in still images. The lack of vection in the motion images may explain why sway was not larger when viewing these motion images as compared to viewing the still images. © 2015 Elsevier B.V. All rights reserved.

The purpose of this study is to measure the predictability of the S&P 500 US stock index and identify its possible determinants. Though predictability has been broadly studied, in very few cases it has been approached via dynamic measures, as done in this work. In order to establish predictability we used the eta measure of the field of neural networks, along with the p-value of the variance ratio test of financial econometrics, and detrended fluctuation analysis from the field of econophysics. We built an investment algorithm based on dynamic measures of predictability which results better than the strategy of buying and holding in the period studied. For the most promising measure, an empirical relationship between predictability and various economic and financial factors is established. This study points at the picture of a market that is not statically predictable, but whose predictability dynamically evolves over time in accordance with the adaptive market hypothesis., El objeto de este estudio es medir la predictibilidad del índice accionario estadounidense S&P 500 y establecer posibles determinantes de esta predictibilidad. La predictibilidad se ha estudiado ampliamente, pero en pocos casos mediante medidas dinámicas y per se, como se hace aquí. Para establecer la predictibilidad se utilizan la medida eta del campo de las redes neuronales, el valor p del test de la razón de varianzas del campo de la econometría financiera y el análisis de fluctuación sin tendencia del campo de la econofísica. Se construye un algoritmo de inversión basado en las medidas dinámicas de predictibilidad que es mejor que la estrategia de comprar y mantenerse en el periodo estudiado. Para la medida más promisoria, se establece una relación empírica entre la predictibilidad y varios factores económicos y financieros. El panorama que arroja este estudio es el de un mercado que no es estáticamente predecible, sino cuya predictibilidad evoluciona dinámicamente en el tiempo, lo cual es compatible con la hipótesis del mercado adaptativo., O objeto deste estudo é medir a previsibilidade do índice acio-nário americano S&P 500 e estabelecer possíveis determinantes dessa previsibilidade. A previsibilidade foi estudada amplamente, mas em poucos casos através de medidas dinâmicas e por si só, como se faz aqui. Para estabelecer a previsibilidade, utilizam-se a medida eta do campo das redes neuronais, o valor p do teste da razão de variâncias do campo da econometria financeira e a análise de flutuação sem tendência do campo da econofísica. Constrói-se um algoritmo de investimento baseado nas medidas dinâmicas de previsibilidade que é melhor que a estratégia de comprar e manter-se no período estudado. Para a medida mais promissora, estabelece-se uma relação empírica entre a previsibilidade e vários fatores econômicos e financeiros. O panorama que este estudo apresenta é o de um mercado que não é estaticamente previsível, mas cuja previsibilidade evoluciona dinamicamente no tempo, o qual é compatível com a hipótese do mercado adaptativo.

The purpose of this study is to measure the predictability of the S&P 500 US stock index and identify its possible determinants. Though predictability has been broadly studied, in very few cases it has been approached via dynamic measures, as done in this work. In order to establish predictability we used the eta measure of the field of neural networks, along with the p-value of the variance ratio test of financial econometrics, and detrended fluctuation analysis from the field of econophysics. We built an investment algorithm based on dynamic measures of predictability which results better than the strategy of buying and holding in the period studied. For the most promising measure, an empirical relationship between predictability and various economic and financial factors is established. This study points at the picture of a market that is not statically predictable, but whose predictability dynamically evolves over time in accordance with the adaptive market hypothesis., El objeto de este estudio es medir la predictibilidad del índice accionario estadounidense S&P 500 y establecer posibles determinantes de esta predictibilidad. La predictibilidad se ha estudiado ampliamente, pero en pocos casos mediante medidas dinámicas y per se, como se hace aquí. Para establecer la predictibilidad se utilizan la medida eta del campo de las redes neuronales, el valor p del test de la razón de varianzas del campo de la econometría financiera y el análisis de fluctuación sin tendencia del campo de la econofísica. Se construye un algoritmo de inversión basado en las medidas dinámicas de predictibilidad que es mejor que la estrategia de comprar y mantenerse en el periodo estudiado. Para la medida más promisoria, se establece una relación empírica entre la predictibilidad y varios factores económicos y financieros. El panorama que arroja este estudio es el de un mercado que no es estáticamente predecible, sino cuya predictibilidad evoluciona dinámicamente en el tiempo, lo cual es compatible con la hipótesis del mercado adaptativo., O objeto deste estudo é medir a previsibilidade do índice acio-nário americano S&P 500 e estabelecer possíveis determinantes dessa previsibilidade. A previsibilidade foi estudada amplamente, mas em poucos casos através de medidas dinâmicas e por si só, como se faz aqui. Para estabelecer a previsibilidade, utilizam-se a medida eta do campo das redes neuronais, o valor p do teste da razão de variâncias do campo da econometria financeira e a análise de flutuação sem tendência do campo da econofísica. Constrói-se um algoritmo de investimento baseado nas medidas dinâmicas de previsibilidade que é melhor que a estratégia de comprar e manter-se no período estudado. Para a medida mais promissora, estabelece-se uma relação empírica entre a previsibilidade e vários fatores econômicos e financeiros. O panorama que este estudo apresenta é o de um mercado que não é estaticamente previsível, mas cuja previsibilidade evoluciona dinamicamente no tempo, o qual é compatível com a hipótese do mercado adaptativo.

Entropy estimation metrics have become a widely used method to identify subtle changes or hidden features in biomedical records. These methods have been more effective than conventional linear techniques in a number of signal classification applications, specially the healthy pathological segmentation dichotomy. Nevertheless, a thorough characterization of these measures, namely, how to match metric and signal features, is still lacking. This paper studies a specific characterization problem: the influence of missing samples in biomedical records. The assessment is conducted using four of the most popular entropy metrics: Approximate Entropy, Sample Entropy, Fuzzy Entropy, and Detrended Fluctuation Analysis. The rationale of this study is that missing samples are a signal disturbance that can arise in many cases: signal compression, non-uniform sampling, or data transmission stages. It is of great interest to determine if these real situations can impair the capability of segmenting signal classes using such metrics. The experiments employed several biosignals: electroencephalograms, gait records, and RR time series. Samples of these signals were systematically removed, and the entropy computed for each case. The results showed that these metrics are robust against missing samples: With a data loss percentage of 50% or even higher, the methods were still able to distinguish among signal classes.

Entropy estimation metrics have become a widely used method to identify subtle changes or hidden features in biomedical records. These methods have been more effective than conventional linear techniques in a number of signal classification applications, specially the healthy pathological segmentation dichotomy. Nevertheless, a thorough characterization of these measures, namely, how to match metric and signal features, is still lacking. This paper studies a specific characterization problem: the influence of missing samples in biomedical records. The assessment is conducted using four of the most popular entropy metrics: Approximate Entropy, Sample Entropy, Fuzzy Entropy, and Detrended Fluctuation Analysis. The rationale of this study is that missing samples are a signal disturbance that can arise in many cases: signal compression, non-uniform sampling, or data transmission stages. It is of great interest to determine if these real situations can impair the capability of segmenting signal classes using such metrics. The experiments employed several biosignals: electroencephalograms, gait records, and RR time series. Samples of these signals were systematically removed, and the entropy computed for each case. The results showed that these metrics are robust against missing samples: With a data loss percentage of 50% or even higher, the methods were still able to distinguish among signal classes.

Complexity is a result of interactions among individual components of a distributed system, each with their own dynamical time scale. Statistical techniques such as fluctuation analysis are used to quantify extent of long range correlations within time series data by estimating a scaling exponent commonly known as Hurst exponent. Data from magnetospheric dynamics, physiology and finance are known to show multi-exponent nature (two exponents in particular) in their fluctuation analysis with a crossover between the power laws. This correlation crossover can be seen due to the statistical approach taken in the analysis of a range of systems with differing dynamical time scales, particularly due to the nature of interactions with one another. We refer to this property as multi-scale nature in the time series data from complex systems. The main contributions of the thesis are as follows: Study of crossover in fluctuation analysis of data from magnetosphere, physiology and finance: We propose an innovative regression scheme, whose mathematical model well describes two exponent nature with an intermediate crossover regime seen in fluctuation analysis - the Hyperbolic regression. Slopes of the asymptotes of the hyperbola are the Hurst exponents, and, center of the resulting hyperbolic fit is an estimate of the correlation crossover time. It is key to note that in this regression, there are no assumptions made about the crossover time, unlike previous approach to crossover fitting. Different data sets corresponding to different physical processes demonstrate multi-scale nature. However, each data presents a unique challenge to be addressed, as a result of characterization of its scaling crossover. Application of hyperbolic regression on the crossover seen in fluctuation analysis of auroral electrojet index data from magnetosphere resulted in estimation of Hurst exponents before and after the crossover. Also, the correlation crossover time scale is now measured by improved mo

© 2014 The Economic and Social Review. All rights reserved. This paper investigates the effect of the 2008 financial crisis on informational efficiency by carrying out a long-memory analysis of European corporate bond markets. We compute the Hurst exponent for fifteen sectorial indices to scrutinise the time-varying behaviour of long-range memory, applying a shuffling technique to avoid short-term correlation. We find that the financial crisis has uneven effects on the informational efficiency of all corporate bond sectors, especially those related to financial services. However, their vulnerability is not homogeneous and some nonfinancial sectors suffer only a transitory effect.

Parasitism and other stressors are ubiquitous in nature but their effects on animal behaviour can be difficult to identify. We investigated the effects of nematode parasitism and other indicators of physiological impairment on the sequential complexity of foraging and locomotion behaviour among wild Japanese macaques (Macaca fuscata yakui). We observed all sexually mature individuals (n = 28) in one macaque study group between October 2007 and August 2008, and collected two faecal samples/month/individual (n = 362) for parasitological examination. We used detrended fluctuation analysis (DFA) to investigate long-range autocorrelation in separate, binary sequences of foraging (n = 459) and locomotion (n = 446) behaviour collected via focal sampling. All behavioural sequences exhibited long-range autocorrelation, and linear mixed-effects models suggest that increasing infection with the nodular worm Oesophagostomum aculeatum, clinically impaired health, reproductive activity, ageing and low dominance status were associated with reductions in the complexity of locomotion, and to a lesser extent foraging, behaviour. Furthermore, the sequential complexity of behaviour increased with environmental complexity. We argue that a reduction in complexity in animal behaviour characterizes individuals in impaired or ‘stressed’ states, and may have consequences if animals cannot cope with heterogeneity in their natural habitats.

Spatial patterns of species diversity have important influences on the functioning of ecosystems, and the effect of livestock grazing on spatial heterogeneity can differ depending on the scale of the analysis. This study examined the effects of grazing on the spatial patterns of species distributions and whether the effects of grazing on the spatial distributions of a species vary with the scale of the analysis. Data were collected at three locations in the subalpine grasslands of Ordesa-Monte Perdido National Park and Aísa Valley, Central Pyrenees, Spain, which differed in mean stocking rates. Aspect explained about one-third of the environmental variation in species distributions. In flat areas, spatial variation in species composition varied with grazing intensity at two scales. At a coarse scale (among vegetation patches), grazing promoted patchiness, and among-transect variation in species diversity and grazing intensity were positively correlated. At a fine scale (within vegetation patches), the disruption of the self-organizing processes of the species spatial distributions resulted in a reduction in the long-range spatial autocorrelations of some of the characteristic species and in the homogenization of species spatial distributions. The presence of encroaching Echinospartum horridum had a significant influence on the effect of grazing on south-facing grassland slopes.

臨床検査データの解釈にパターン認識の手法を用いることによって,コンピュー タによる自動判読や,パターンの中に隠された新たな病態情報を抽出することが期待される。 この目的のために筆者がこれまで行ってきた試みについて紹介する。内容は,数値データセッ トについてのパターン類似性を用いたマッチング検索,ニューラルネットによる学習を用いた 診断支援,波形データについてのゆらぎ解析による機能評価,画像データについての複雑性を 指標とした質的評価の定量化,である.

臨床検査データの解釈にパターン認識の手法を用いることによって,コンピュー タによる自動判読や,パターンの中に隠された新たな病態情報を抽出することが期待される。 この目的のために筆者がこれまで行ってきた試みについて紹介する。内容は,数値データセッ トについてのパターン類似性を用いたマッチング検索,ニューラルネットによる学習を用いた 診断支援,波形データについてのゆらぎ解析による機能評価,画像データについての複雑性を 指標とした質的評価の定量化,である.

We propose a new and very fast index (the frequency of sign changes of the mirrored differences or fscmd) with good correlation with the short range scaling exponent (  Ｒ) estimated among scales 4 to 16 of the DFA. fscmd computes the relative number of sign changes of the difference of the RR time series minus their corresponding reversed RR time series after a moving average detrending procedure is applied using a window of 30 samples. Linear regression results with simulated time series with Fractional Brownian Noise and with actual time series using the Fantasia (FT), Normal Sinus Rhythm RR time series (NSR) and Congestive Heart Failure RR interval (CHF) databases after artifact correction show good agreement between fscmd and  Ｒ. Finally, Mann-Whitney Rank Sum tests applied to  Ｒ and fscmd when comparing NSR and CHF databases show very significant differences (p<0.001) between groups for both indices., Postprint (published version)

Se propone un nuevo método para la cuantificación de la autosemejanza de corto alcance en series temporales. Los índices propuestos presentan alta correlación con los resultados del análisis de fluctuaciones tras eliminación de tendencias (DFA) y tienen una velocidad de cálculo mayor en más de dos órdenes de magnitud respecto al cálculo tradicional de exponentes de escala. En simulaciones con ruido fraccional gaussiano y en series RR, los exponentes de escala y los índices propuestos presentan una relación prácticamente lineal por lo que la metodología presentada puede emplearse como un método alternativo al DFA en series temporales con un gran número de muestras., Postprint (published version)

We combined genometric (DNA walks) and statistical (detrended fluctuation analysis) methods on 456 prokaryotic chromosomes from 309 different bacterial and archaeal species to look for specific patterns and long-range correlations along the genome and relate them to ecological lifestyles. The position of each nucleotide along the complete genome sequence was plotted on an orthogonal plane (DNA landscape), and fluctuation analysis applied to the DNA walk series showed a long-range correlation in contrast to the lack of correlation for artificially generated genomes. Different features in the DNA landscapes among genomes from different ecological and metabolic groups of prokaryotes appeared with the combined analysis. Transition from hyperthermophilic to psychrophilic environments could have been related to more complex structural adaptations in microbial genomes, whereas for other environmental factors such as pH and salinity this effect would have been smaller. Prokaryotes with domain-specific metabolisms, such as photoautotrophy in Bacteria and methanogenesis in Archaea, showed consistent differences in genome correlation structure. Overall, we show that, beyond the relative proportion of nucleotides, correlation properties derived from their sequential position within the genome hide relevant phylogenetic and ecological information. This can be studied by combining genometric and statistical physics methods, leading to a reduction of genome complexity to a few useful descriptors.

OBJECTIVE: To investigate the dynamics of the synchronization between heart rate variability and sleep electroencephalogram power spectra and the effect of sleep apnea-hypopnea syndrome. METHODS: Heart rate and sleep electroencephalogram signals were recorded in controls and patients with sleep apnea-hypopnea syndrome that were matched for age, gender, sleep parameters, and blood pressure. Spectral analysis was applied to electrocardiogram and electroencephalogram sleep recordings to obtain power values every 20s. Synchronization likelihood was computed between time series of the normalized high frequency spectral component of RR-intervals and all electroencephalographic frequency bands. Detrended fluctuation analysis was applied to the synchronizations in order to qualify their dynamic behaviors. RESULTS: For all sleep bands, the fluctuations of the synchronization between sleep EEG and heart activity appear scale free and the scaling exponent is close to one as for 1/f noise. We could not detect any effect due to sleep apnea-hypopnea syndrome. CONCLUSIONS: The synchronizations between the high frequency component of heart rate variability and all sleep power bands exhibited robust fluctuations characterized by self-similar temporal behavior of 1/f noise type. No effects of sleep apnea-hypopnea syndrome were observed in these synchronizations. SIGNIFICANCE: Sleep apnea-hypopnea syndrome does not affect the interdependence between the high frequency component of heart rate variability and all sleep power bands as measured by synchronization likelihood., Journal Article, Research Support, Non-U.S. Gov't, SCOPUS: ar.j, info:eu-repo/semantics/published

OBJECTIVE: To investigate the dynamics of the synchronization between heart rate variability and sleep electroencephalogram power spectra and the effect of sleep apnea-hypopnea syndrome. METHODS: Heart rate and sleep electroencephalogram signals were recorded in controls and patients with sleep apnea-hypopnea syndrome that were matched for age, gender, sleep parameters, and blood pressure. Spectral analysis was applied to electrocardiogram and electroencephalogram sleep recordings to obtain power values every 20s. Synchronization likelihood was computed between time series of the normalized high frequency spectral component of RR-intervals and all electroencephalographic frequency bands. Detrended fluctuation analysis was applied to the synchronizations in order to qualify their dynamic behaviors. RESULTS: For all sleep bands, the fluctuations of the synchronization between sleep EEG and heart activity appear scale free and the scaling exponent is close to one as for 1/f noise. We could not detect any effect due to sleep apnea-hypopnea syndrome. CONCLUSIONS: The synchronizations between the high frequency component of heart rate variability and all sleep power bands exhibited robust fluctuations characterized by self-similar temporal behavior of 1/f noise type. No effects of sleep apnea-hypopnea syndrome were observed in these synchronizations. SIGNIFICANCE: Sleep apnea-hypopnea syndrome does not affect the interdependence between the high frequency component of heart rate variability and all sleep power bands as measured by synchronization likelihood., Journal Article, Research Support, Non-U.S. Gov't, SCOPUS: ar.j, info:eu-repo/semantics/published

Analýza variability srdečního rytmu HRV může být použita pro diagnózu srdečních onemocnění. Metody analýzy HRV jsou rozděleny na lineární a nelineární metody. Časová metoda je jedna z nejjednodušších metod a řadí se k lineárním metodám. Detrendovaná analýza fluktuace DFA je nelineární metoda vytvořená relativně nedávno. V této práci bylo provedeno srovnání těchto dvou metod na základě detekování změn v HRV způsobených ischemií., Heart rate variability analysis can be used for a diagnosis of the cardiac diseases. The HRV analysis methods are divided into linear and nonlinear methods. Time-domain method is one of the simplest method and belongs to linear methods. Detrended fluctuation analysis DFA is nonlinear method made relatively recently. In this paper, it has been done the comparison of these two methods based on the changes detection in HRV caused by an ischemia.

Bakalárska práca sa zaoberá identifikáciou artefaktov vyskytujúcich sa počas behu a plávania a vplyvu týchto artefaktov pri stanovení aeróbneho prahu z RR intervalov pomocou analýzy detrendovanej fluktuácie a jeho indexu 1 počas záťažového testu. Najprv je rozobraná teoretická časť, kde sú popísané všetky informácie ohľadom vymedzenia tréningových zón, HRV, analýzy detrendovanej fluktuácie a spôsob stanovenia aeróbneho prahu z DFA. Druhá časť práce je praktická a zaoberá sa konkrétnymi meraniami, ich spracovaniu, a následnou štatistickou analýzou výsledkov. Bolo zistené, že s artefaktmi vyskytujúcich počas behu bola pri určovaní aeróbneho prahu priemerná absolútna chyba len 1,58 úderov/min. a po aplikovaní korekčnej metódy už len 0,33 úderov/min. Počas analýzy s artefaktmi vyskytujúcich sa pri plávaní bola táto chyba až na úrovni 9,89 úderov/min a po aplikovaní korekčnej metódy už len 0,67 úderov/min., The bachelor's thesis deals with the identification of artifacts occurring during running and swimming and the influence of these artifacts in determining the aerobic threshold from RR intervals using the analysis of detrended fluctuation and its 1 index during a stress test. First, the theoretical part is discussed, where all information related to the definition of training zones, HRV, detrended fluctuation analysis and the method of determining the aerobic threshold from DFA are described. The second part of the work is practical and deals with specific measurements, their processing and subsequent statistical analysis of the results. It was found that for artifacts occurring during running, the mean absolute error in determining the aerobic threshold was only 1.58 beats/min. and after applying the correction method only 0.33 beats/min. When analyzed with artifacts occurring during swimming, this error was up to 9.89 beats/min, and after applying the correction method it was only 0.67 beats/min.

Bakalárska práca sa zaoberá identifikáciou artefaktov vyskytujúcich sa počas behu a plávania a vplyvu týchto artefaktov pri stanovení aeróbneho prahu z RR intervalov pomocou analýzy detrendovanej fluktuácie a jeho indexu 1 počas záťažového testu. Najprv je rozobraná teoretická časť, kde sú popísané všetky informácie ohľadom vymedzenia tréningových zón, HRV, analýzy detrendovanej fluktuácie a spôsob stanovenia aeróbneho prahu z DFA. Druhá časť práce je praktická a zaoberá sa konkrétnymi meraniami, ich spracovaniu, a následnou štatistickou analýzou výsledkov. Bolo zistené, že s artefaktmi vyskytujúcich počas behu bola pri určovaní aeróbneho prahu priemerná absolútna chyba len 1,58 úderov/min. a po aplikovaní korekčnej metódy už len 0,33 úderov/min. Počas analýzy s artefaktmi vyskytujúcich sa pri plávaní bola táto chyba až na úrovni 9,89 úderov/min a po aplikovaní korekčnej metódy už len 0,67 úderov/min., The bachelor's thesis deals with the identification of artifacts occurring during running and swimming and the influence of these artifacts in determining the aerobic threshold from RR intervals using the analysis of detrended fluctuation and its 1 index during a stress test. First, the theoretical part is discussed, where all information related to the definition of training zones, HRV, detrended fluctuation analysis and the method of determining the aerobic threshold from DFA are described. The second part of the work is practical and deals with specific measurements, their processing and subsequent statistical analysis of the results. It was found that for artifacts occurring during running, the mean absolute error in determining the aerobic threshold was only 1.58 beats/min. and after applying the correction method only 0.33 beats/min. When analyzed with artifacts occurring during swimming, this error was up to 9.89 beats/min, and after applying the correction method it was only 0.67 beats/min.