Search

To record all electrical activity of the human brain, an electroencephalogram (EEG) test using electrodes attached to the scalp is conducted. Analysis of EEG signals plays an important role in the diagnosis and treatment of brain diseases in the biomedical field. One of the brain diseases found in early ages include autism. Autistic behaviours are hard to distinguish, varying from mild impairments, to intensive interruption in daily life. The non-linear EEG signals arising from various lobes of the brain have been studied with the help of a robust technique called Detrended Fluctuation Analysis (DFA). Here, we study the EEG signals of Typically Developing (TD) and children with Autism Spectrum Disorder (ASD) using DFA. The Hurst exponents, which are the outputs of DFA, are used to find out the strength of self-similarity in the signals. Our analysis works towards analysing if DFA can be a helpful analysis for the early detection of ASD.

Purpose: To understand the dynamics of the agricultural commodities market and predict a possible economic crisis, in addition to helping agricultural producers balance their product portfolio, diversifying their goods and reducing risks. Theoretical framework: Prices of agricultural commodities have changed significantly since 2002; although had been an increase in demand, where weather problems negatively affected supply, resulting in price increases. With the global financial crisis of 2008, there was a reduction in international credit and an increase in the US dollar against the Brazilian Real. Design/Methodology/Approach: Detrended Cross-Correlation Analysis and Detrended Fluctuation Analysis methods were used to understand the behavior of the cross correlations of the price of five Brazilian agribusiness commodities (cotton, sugar, coffee, corn and soybeans) for the previous periods, during and after the subprime crisis. Findings: Both methods showed a significant change in the behavior of the series in the period of crisis, when compared to their temporal neighborhoods. Research, Practical & Social Implications: It was found that the crisis changed the structure of the correlation of the returns on the commodities analyzed. This change implies alterations to a possible product portfolio in order to minimize risks. Originality/Value: The long-term nonlinear correlation behavior was calculated and analyzed on the temporal series for the return on the main agricultural commodities in the period of the subprime crisis and its temporal neighborhoods were calculated and analyzed, allowing several changes to be found in the product correlation structure, due to the crisis process. Keywords: Subprime Financial Crisis; Agricultural Commodities; Detrended Fluctuation Analysis; Detrended Cross-Correlation Analysis.

The detrended fluctuation analysis, a technique for detecting long-range correlations in fractal time series, is used to characterize thermoacoustic oscillations of impending combustion instabiliti...

Military personnel in combat roles often perform gait tasks with additional load, which can affect the contributions of joint mechanical work (positive and negative). Furthermore, different locomotion patterns can also affect joint specific work contributions. While mean behavior of joint work is important to understanding gait, changes in joint kinetic modulation, or the regulation/control of stride-to-stride joint work variability is necessary to elucidate locomotor system function. Suboptimal modulation exhibited as a stochastic time-series (large fluctuation followed by an opposite smaller fluctuation) could potentially affect locomotion efficiency and portend injury risk. It remains unclear how the locomotor system responds to a combination of load perturbations and varying locomotion patterns.What are the interactive effects of load magnitude and locomotion pattern on joint positive/negative work and joint work modulation in healthy, active, recruit-aged women?Eleven healthy, active, recruit-aged (18-33 years) women ran and forced-marched (walking at a velocity an individual would typically jog) in bodyweight (BW), an additional 25 % of BW (+25 %BW) and an additional 45 % of BW (+45 %BW) conditions at a velocity above their gait transition velocity. Joint work was calculated as the time integral of joint power. Joint work modulation was assessed with detrended fluctuation analysis (DFA) on consecutive joint work time-series.Joint work contributions shifted proximally for forced-marching demonstrated by lesser (p .001) positive/negative ankle work but greater (p = .001) positive hip work contributions compared to running. Running exhibited optimal positive ankle work modulation compared to forced-marching (p = .040). Knee and ankle negative joint work modulation was adversely impacted compared to the hip during forced-marching (p .001).Employing forced-marching gait while under loads of 25 and 45 % of BW reduces the ability of the plantar-flexors and knee extensors to optimally contribute to energy absorption and propulsion in recruit-aged women, potentially reducing metabolic efficiency and increasing injury risk.

Saliva has emerged as an efficient screening sample for early stage detection of oral cancer (OC) owing to non-invasiveness coupled with high sensitivity and specificity. Although spectroscopic characterization of saliva in oral potentially malignant disorders OPMDs) and OC is extensively studied, its potential as imaging biomarker is sparsely explored. Further, the literature on crystalline pattern of saliva for other diseases or different physiological conditions is mostly qualitative. This paper proposed multifractal based methodology to quantitatively study alteration of the salivary fern pattern in different OPMDs and OC in relation to normal counterpart. The fern pattern of dried saliva is captured by stereo-zoom microscope in reflective mode and an image dataset is developed. We resort to two dimensional multi-fractal detrended fluctuation analysis (2d MFDFA) to elucidate the complexity and heterogeneity of these micro-structured patterns. Existence of multifractal nature embedded in salivary fern has been validated for the first time. Long range spatial correlation is found to be the origin of multifractality. Variation in multi-scale self-similarity of irregular pattern in different study groups is demonstrated by four features extracted from MFDFA. Statistical analysis shows discriminating nature of these features for combinations of pairwise interclass classification. This study sheds light on acceptability of microscopic images of arborized saliva in fast and cost effective screening of different oral lesions.

The paper presents a technique for calculating information-correlation characteristics based on the methods of average mutual information and two-dimensional detrended fluctuation analysis. The experimental samples are structures with an array of self-organized InAs/GaAs quantum dots grown without a cover layer by gas-phase epitaxy from metalorganic compounds. The microstructure of the sample surfaces is studied by scanning electron microscopy. The information-correlation characteristics of InAs/GaAs structures with quantum dots are obtained using two-dimensional detrended fluctuation analysis and average mutual information. The order of the array of quantum dots in the InAs/GaAs structure is estimated depending on the applied technological modes of production. It is found that increasing the growth temperature of the experimental samples from 480 to 520°C increases the ordering of the location of quantum dots on the surface.

Altered movement complexity, indicative of system dysfunction, has been demonstrated with increased running velocity and neuromuscular fatigue. The critical velocity (CV) denotes a metabolic and neuromuscular fatigue threshold. It remains unclear whether changes to complexity during running are coupled with the exercise intensity domain in which it is performed. The purpose of this study was to examine whether movement variability and complexity differ exclusively above the CV intensity during running. Ten endurance-trained participants ran at 95%, 100%, 105% and 115% CV for 20 min or to task failure, whichever occurred first. Movement at the hip, knee, and ankle were sampled throughout using 3D motion analysis. Complexity of kinematics in the first and last 30 s were quantified using sample entropy (SampEn) and detrended fluctuation analysis (DFA-α). Variability was determined using standard deviation (SD). SampEn decreased during all trials in knee flexion/extension and it increased in hip internal/external rotation, whilst DFA-α increased in knee internal/external rotation. SD of ankle plantar/dorsiflexion and inversion/eversion, knee internal/external rotation, and hip flexion/extension and abduction/adduction increased during trials. Hip flexion/extension SampEn values were lowest below CV. DFA-α was lower at higher velocities compared to velocities below CV in ankle plantar/dorsiflexion, hip flexion/extension, hip adduction/abduction, hip internal/external rotation. In hip flexion/extension SD was highest at 115% CV. Changes to kinematic complexity over time are consistent between heavy and severe intensity domains. The findings suggest running above CV results in increased movement complexity and variability, particularly at the hip, during treadmill running.

The impairment of short-term heart rate regulation in patients with heart failure with preserved ejection fraction (HFpEF) can cause acute hemodynamic collapse. Detrended fluctuation analysis (DFA) is a useful tool for the diagnosis of heart diseases and the prediction of mortality. In DFA, the short-term scaling exponent α is decreased in heart failure. However, its change in HFpEF patients remains unclear.Twenty patients diagnosed with HFpEF [defined as brain natriuretic peptide (BNP)100 pg/mL, ejection fraction (EF) ≥50%, and without significant valvular disease], 20 diagnosed with non-HFpEF (BNP 100 pg/mL and EF 50%), and 20 control subjects generally matched for age and gender were enrolled. Holter electrocardiography was performed, and heart rate variability was calculated. In the DFA, the scaling exponents in 1000 beats were calculated for each 15-min segment and the average of all segments was used. We compared both the short-term (11 beats, α1) and long-term (≥11 beats, α2) scaling exponents among the three groups.In the HFpEF, non-HFpEF, and control groups, α1 was 0.73 ± 0.27, 0.66 ± 0.29, and 1.01 ± 0.20 (p 0.01), and α2 was 0.95 ± 0.08, 0.88 ± 0.11, and 0.96 ± 0.07 (p 0.01), respectively. The α1 exponent was significantly decreased in the HFpEF group (p 0.01 vs. control) and the non-HFpEF group (p 0.01 vs. control), while the α2 exponent was significantly decreased in the non-HFpEF group only (p 0.05 vs. HFpEF and control).Short-term heart rate regulation is impaired in patients with HFpEF, while patients with non-HFpEF have both short-term and long-term impairment.

Ambulating while carrying a mission specific load is one of the most frequently executed occupational tasks for the military, especially for individuals in combat roles. Prolonged ambulation is a naturally dynamic and complex process, characterized by highly multi-dimensional interactions within the gait mechanics of the lower extremity. Recent wearable sensors studies, like inertial measurement unit (IMU)-related gait studies have demonstrated that machine learning (MLN) algorithms and fractal analysis can successfully discriminate between classes, such as movement patterns, injury, age and sex. This study attempts to classify fractal gait patterns of women and men using IMU-based signal data obtained from accelerometer, gyroscope and magnetometer during a 2 km loaded (20 kg) march. Random Forest (RF) MLN algorithm was used to generate a model that can measure the accuracy and identify the importance of IMU-based signal-related fractal variables. A total of 18 variables were calculated using 2 fractal methods, detrended fluctuation analysis (DFA) and wavelet transform-based power spectral density (PSD), from 3 IMU-based signals in their 3 axes (medial-lateral, vertical and anterior-posterior). A total of 33 healthy adults (17 men [26.7±5.9 years] and 16 women [25.2±4.5 years]) volunteered for this study. A 9-axis IMU sensor was attached to each participant at each of the following locations: feet, shanks, thighs and lumbar spine. An independent training-testing approach, called one-vs-one (i.e., variables from one IMU-based signal were trained and tested using another IMU-based signal) was applied to determine the classification accuracy (i.e., similarities between IMUs) and variable importance (score ranges: 0.0-1.0) measures. These values were then used to select the variables that best independently describe the rank in classification margin. The results from each IMU sensor placement based on the fractal values showed ‘moderate’ accuracy (50-75%), with the exception of two cases: the left shank yielded ‘good’ accuracy (80.1%) compared with the right shank, and the right thigh generated ‘poor’ accuracy (48.9%) compared with the left foot. No IMU location showed excellent accuracy (>90%). The results indicate that each IMU placement location has their own fractal patterns that are not similar to another IMU location in terms of sex classification. The analysis of the variable importance in the classification margin showed that most of the PSD resulted variables were classified as ‘most important’ compared with the DFA resulted variables. IMU sensors, and the associated analyses, could be used during military load carriage to evaluate changes in gait resulting from injury, fatigue or overtraining.

Motivation The quantification of long-range correlation of electroencephalogram (EEG) signals is an important research direction for its relevance in helping understanding the brain activity. Epileptic seizures have been studied in the past years where different non-linear statistical approaches have been employed to understand the relationship between the EEG signal and the epileptic discharge. One of the most widely used method for to analyse long memory processes is the detrended fluctuation analysis (DFA). However, no objective and pragmatic methods have been developed to detect crossover points and reference channels in DFA. Results In this article, we propose: (i) two automatic approaches that successfully detect crossover points in DFA related methods on the log–log plot and (ii) a criteria to choose the reference channel for the log-amplitude function. Moreover, the DFA is applied to EEG signals of 10 epileptic patients collected from the CHB-MIT database, being the log-amplitude function used to compare the different brain hemispheres by making use of the methodology proposed in the article. The existence of long-range power-law correlations is demonstrated and indicates that the EEG signals of epileptic patients present three well-defined regions with the first region showing a 1/f noise (pink noise) for seven subjects and a random walk behaviour for three subjects. The second and third regions show anti-persistence behaviour. Moreover, the results of the log-amplitude function were divided in two groups: the first, including seven subjects, where the increase in the scales results in an increase in the fluctuation in the frontal channels and the second, included three subjects, where the fluctuation for large scales are greater for the parietal channels. Availability and implementation The functions used in this article are available in the R package DFA (Mesquita et al., 2020). Supplementary information Supplementary data are available at Bioinformatics online.

A financial time series, such as a stock market index, foreign exchange rate, or a commodity price, fluctuates heavily and shows scaling behaviors. Scaling and multi-scaling behaviors are measured for a nonstationary time series, such as stock market indices, high-frequency stock prices of individual stocks, or the volatility time series of a stock index. We review the fractality, multi-scaling, and multifractality of the financial time series of a stock market. We introduce a detrended fluctuation analysis of the financial time series to extract fluctuation patterns. Multifractality is measured using various methods, such as generalized Hurst exponents, the generalized partition function method, a detrended fluctuation analysis, the detrended moving average method, and a wavelet transformation.

Sudden cardiac death (SCD) is an unexpected death of a person with or without knowing cardiac causes are often occurring in less than an hour after the incidence of symptoms. In the case of physicians' knowledge of this incident, they can make appropriate decisions for the patients at-risk and reduce the number of such deaths significantly. The purpose of this paper is to examine different methods for predicting sudden cardiac death using electrocardiogram (ECG) signal from 1998 to recent years that can contribute to researchers to become familiar with the wide range of research conducted in this field.In this paper, studies using various methods to predict sudden cardiac death that has applied the data from the Physionet and MIT-BIH databases with a sampling frequency of 256 samples per second are reviewed. Both types of data have normal and abnormal sampling labels and the data recording time varies from a few seconds to minutes. In the field of SCD prediction, various studies have addressed the processing of the electrocardiogram (ECG) signal as well as the heart rate variability (HRV) signal in different domains, including time, time-frequency, and nonlinear domain. In time-domain processing the statistical characteristics of time signal such as the mean and standard deviation of heart rate, the mean and standard deviation of RR intervals, and Root Mean Square of the Successive Differences (RSSD) are used. Also, in the frequency domain, the power spectral density (PSD) of the signal energy is used in a very-low-frequency band, low-frequency band and, high-frequency band. Similarly, in the nonlinear domain, features such as Poincare plot, detrended fluctuation analysis (DFA), common entropy, wavelet transform coefficients (WTC), and features of the recursive graph including Lmax, Lmean, correlation dimension (CD), etc. are used. In all of the proposed algorithms so far, researchers have been trying to inform the sudden death alarm in a larger interval than the time of death by separating the signals into different time periods and extracting various features.To evaluate the results of the proposed methods, each of the researchers analyzed the a-few-minute intervals before the SCD. Different classification methods are available to identify the efficiency of the proposed algorithm, such as support vector machine (SVM), multilayer perceptron neural network (MLP), radial base function neural network (RBF), k-nearest neighbor (KNN) and mixture expert (ME). The use of features introduced in different domains and different classifiers has led to the observation of different horizons of prediction in various studies. The results of these predictions are often free from the interpretations of clinical symptoms, and their maximum presented time with acceptable validity eventually reaches 4 minutes before the event, which is not an acceptable time for people who have attacked outside the hospital. Accordingly, the most prominent of these evaluations is the mixture expert methodology in which the best feature extraction methods are used in a new method for selecting the optimal feature space locally. This method makes it possible to select different features every minute before the event by choosing the optimal features for each one-minute interval of the signal as an episode which increases the prediction time from 4 minutes before the death to 12 minutes and allows the interpretation of clinical symptoms in terms of multiplication of the presence of the features per minute.Given the non-linear nature of the HRV signal and the similarity of the ECG signal in many time intervals, the use of the HRV signal has become more popular among scholars. The analysis of various studies shows that by approaching the time of death, linear features (time and frequency) can be predictive of death according to the sensible behavior and variations in patients’ signal. Instead of moving away from the death interval, the use of chaotic and non-linear features is more effective. Therefore, a more precise selection of features in this area can be useful for increasing the horizon of prediction of death.

This paper proposes a strategy for suppressing noise via empirical mode decomposition (EMD)-based hierarchical multiresolution analysis. First, an intrinsic mode function (IMF) index that can differentiate the noise-dominant IMFs and the information-dominant IMFs in the first level of decomposition is determined by the conventional EMD-based denoising method. Next, the EMD is applied to those discarded IMFs in the first level of decomposition to obtain the IMFs in the second level of decomposition. Then, the detrended fluctuation analysis (DFA) is employed to define the IMF index separating the noise-dominant IMFs and the information-dominant IMFs in the second level of decomposition. Information-dominant IMFs in both the first and second levels of decompositions are summed together to obtain the denoised signal. The study is limited to signals that were corrupted by additive white Gaussian noise (AWGN). Extensive computer numerical simulations are conducted on both synthetic signals and practical signals. The results show that our proposed method is promising in some cases, especially at high-input SNR levels. For example, in the case of the CH4 signal with 12-dB input SNR, the processed SNR based on our proposed strategy with the consecutive mean squared error (CMSE) is 23.27 dB, while that based on the conventional CMSE method is only 23.14 dB. Also, our proposed method can extract information from more than one level of decomposition to reconstruct the denoised signal compared to only one level of decomposition in the conventional EMD-based denoising method.

Electroencephalogram (EEG) is one of the most important signals for diagnosis of Autism Spectrum Disorder (ASD). There are different challenges such as feature selection and the existence of artifacts in EEG signals. This article aims to present a robust method for early diagnosis of ASD from EEG signal. The study population consists of 34 children with ASD between 3–12 years and 11 healthy children in the same ranges of age. The proposed approach uses linear and nonlinear features such as Power Spectrum, Wavelet Transform, Fast Fourier Transform (FFT), Fractal Dimension, Correlation Dimension, Lyapunov Exponent, Entropy, Detrended Fluctuation Analysis and Synchronization Likelihood for describing the EEG signal. In addition Density Based Clustering is utilized for artifact removal and robustness. Besides, features selection is applied based on different criterions such as Mutual Information (MI), Information Gain (IG), Minimum-Redundancy Maximum-Relevancy (mRmR) and Genetic Algorithm (GA). Finally, the K-Nearest-Neighbor (KNN) and Support Vector Machines (SVM) classifiers are used for final decision. As a result, the investigation indicates that the classification accuracy of the approach using SVM is 90.57% while for KNN it is 72.77%. Moreover, the sensitivity of the proposed method is 99.91% for SVM and 91.96% for KNN. Also, experiments show that DFA, LE, Entropy and SL features have considerable influence in promoting the classification accuracy.

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.

Home‐based measures of lung function, inflammation, symptoms, and medication use are frequently collected in respiratory clinical trials. However, new statistical approaches are needed to make better use of the information contained in these data‐rich variables. In this work, we use data from two phase III asthma clinical trials demonstrating the benefit of benralizumab treatment to develop a novel longitudinal mixed effects model of peak expiratory flow (PEF), a lung function measure easily captured at home using a hand‐held device. The model is based on an extension of the mixed effects modeling framework to incorporate stochastic differential equations and allows for quantification of several statistical properties of a patient's PEF data: the longitudinal trend, long‐term fluctuations, and day‐to‐day variability. These properties are compared between treatment groups and related to a patient's exacerbation risk using a repeated time‐to‐event model. The mixed effects model adequately described the observed data from the two clinical trials, and model parameters were accurately estimated. Benralizumab treatment was shown to improve a patient's average PEF level and reduce long‐term fluctuations. Both of these effects were shown to be associated with a lower exacerbation risk. The day‐to‐day variability was neither significantly affected by treatment nor associated with exacerbation risk. Our work shows the potential of a stochastic model‐based analysis of home‐based lung function measures to support better estimation and understanding of treatment effects and disease stability. The proposed analysis can serve as a complement to descriptive statistics of home‐based measures in the reporting of respiratory clinical trials.

Background Individuals with chronic obstructive pulmonary disease (COPD) commonly experience exacerbations, which may require hospital admission. Early detection of exacerbations, and therefore early treatment, could be crucial in preventing admission and improving outcomes. Our previous research has demonstrated that the pattern analysis of peripheral oxygen saturation (SpO2) fluctuations provides novel insights into the engagement of the respiratory control system in response to physiological stress (hypoxia). Therefore, this pilot study tested the hypothesis that the pattern of SpO2 variations in overnight recordings of individuals with COPD would distinguish between stable and exacerbation phases of the disease. Methods Overnight pulse oximetry data from 11 individuals with COPD, who exhibited exacerbation after a period of stable disease, were examined. Stable phase recordings were conducted overnight and one night prior to exacerbation recordings were also analyzed. Pattern analysis of SpO2 variations was carried examined using sample entropy (for assessment of irregularity), the multiscale entropy (complexity), and detrended fluctuation analysis (self‐similarity). Results SpO2 variations displayed a complex pattern in both stable and exacerbation phases of COPD. During an exacerbation, SpO2 entropy increased (p = 0.029) and long‐term fractal‐like exponent (α2) decreased (p = 0.002) while the mean and standard deviation of SpO2 time series remained unchanged. Through ROC analyses, SpO2 entropy and α2 were both able to classify the COPD phases into either stable or exacerbation phase. With the best positive predictor value (PPV) for sample entropy (PPV = 70%) and a cut‐off value of 0.454. While the best negative predictor value (NPV) was α2 (NPV = 78%) with a cut‐off value of 1.00. Conclusion Alterations in SpO2 entropy and the fractal‐like exponent have the potential to detect exacerbations in COPD. Further research is warranted to examine if SpO2 variability analysis could be used as a novel objective method of detecting exacerbations., This report provides evidence that the pattern of peripheral oxygen saturation (SpO2) fluctuations detects exacerbations in individuals with COPD. The entropy of SpO2 signal increases a day prior to clinical diagnosis of exacerbation in COPD while mean and total variability of SpO2 signals remain unchanged. This finding has potential for development of a non‐invasive method for early detection of exacerbations in COPD.

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.

Eccentric cycling (ECC) has attracted attention as a method to improve muscle strength and aerobic fitness in populations unable to tolerate conventional methods. However, agreement on exercise prescription targets have been problematic. The current report is an initial exploration of a potentially useful tool, a nonlinear heart rate (HR) variability (HRV) index based on the short-term scaling exponent alpha1 of detrended fluctuation analysis (DFA a1), which has been previously shown to correspond to exercise intensity. Eleven male volunteers performed 45 min of concentric (CON) cycling and ECC separated by 1 month. Work rates were matched for HR (~50% of the maximal HR) during the first 5 min and remained stable thereafter. HRV, HR, oxygen consumption (VO2), and cycling power were monitored and evaluated at elapsed times of 10 (T10) and 45 (T45) minutes duration. HR significantly increased between ECC T10 and ECC T45 (p = 0.003, d = 1.485), while DFA a1 significantly decreased (p = 0.004, d = 1.087). During CON, HR significantly increased (p &lt, 0.001 d = 1.570) without significant DFA a1 change (p = 0.48, d = 0.22). Significantly higher HR was observed at T45 in ECC than in CON (p = 0.047, d = 1.059). A session of unaccustomed ECC lead to decreased values of DFA a1 at T45 in comparison to that seen with CON at similar VO2. ECC lead to altered autonomic nervous system balance as reflected by the loss of correlation properties compared to CON.

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.

In this study, the effect of cardiac resynchronization therapy (CRT) on the relationship between the cardiovascular and respiratory systems in heart failure subjects was examined for the first time. We hypothesized that alterations in cardio-respiratory interactions, after CRT implantation, quantified by signal complexity, could be a marker of a favorable CRT response. Sample entropy and scaling exponents were calculated from synchronously recorded cardiac and respiratory signals 20 min in duration, collected in 47 heart failure patients at rest, before and 9 months after CRT implantation. Further, cross-sample entropy between these signals was calculated. After CRT, all patients had lower heart rate and CRT responders had reduced breathing frequency. Results revealed that higher cardiac rhythm complexity in CRT non-responders was associated with weak correlations of cardiac rhythm at baseline measurement over long scales and over short scales at follow-up recording. Unlike CRT responders, in non-responders, a significant difference in respiratory rhythm complexity between measurements could be consequence of divergent changes in correlation properties of the respiratory signal over short and long scales. Asynchrony between cardiac and respiratory rhythm increased significantly in CRT non-responders during follow-up. Quantification of complexity and synchrony between cardiac and respiratory signals shows significant associations between CRT success and stability of cardio-respiratory coupling.

Tunnel face stability constitutes a significant challenge for shield tunneling in urban areas. The use of a slurry pressure balanced shield machine in mixed ground conditions containing mudstone is generally disturbed by clogging, which results in noises and large fluctuations in the tunnel face pressure. These fluctuations add difficulties to the prediction of the slurry pressure. This paper proposes a denoising method to overcome this difficulty. This method is combined with variational mode decomposition and detrended fluctuation analysis. The method is coupled with cross-correlation analysis (CCA) and a long short-term memory (LSTM) network to predict the tunnel face pressure using both tunneling parameters and geological data as input. The paper proposes a predictive strategy that separates the trend component and fluctuation component from the denoised tunneling data via CCA. Two LSTM-based predictors are presented and combined for the development of a new modeling strategy. The performances of the proposed strategy are illustrated through an application to the Nanning metro. The LSTM model with the proposed strategy gave excellent results in both mudstone and round gravel grounds with an overall R2 value of 0.9974. The paper also presents a comparison of the proposed model with some traditional models as well as a discussion on the importance of input features in different ground conditions.

Although heart rate variability (HRV) indexes have been helpful for monitoring the fatigued state while resting, little data indicate that there is comparable potential during exercise. Since an index of HRV based on fractal correlation properties, alpha 1 of detrended fluctuation analysis (DFA a1) displays overall organismic demands, alteration during exertion may provide insight into physiologic changes accompanying fatigue. Two weeks after collecting baseline demographic and gas exchange data, 11 experienced ultramarathon runners were divided into two groups. Seven runners performed a simulated ultramarathon for 6 h (Fatigue group, FG) and four runners performed daily activity over a similar period (Control group, CG). Before (Pre) and after (Post) the ultramarathon or daily activity, DFA a1, heart rate (HR), running economy (RE) and countermovement jumps (CMJ) were measured while running on a treadmill at 3 m/s. In Pre versus Post comparisons, data showed a decline with large effect size in DFA a1 post intervention only for FG (Pre: 0.71, Post: 0.32; d = 1.34), with minor differences and small effect sizes in HR (d = 0.02) and RE (d = 0.21). CG showed only minor differences with small effect sizes in DFA a1 (d = 0.19), HR (d = 0.15), and RE (d = 0.31). CMJ vertical peak force showed fatigue‐induced decreases with large effect size in FG (d = 0.82) compared to CG (d = 0.02). At the completion of an ultramarathon, DFA a1 decreased with large effect size while running at low intensity compared to pre‐race values. DFA a1 may offer an opportunity for real‐time tracking of physiologic status in terms of monitoring for fatigue and possibly as an early warning signal of systemic perturbation., The aim of this report is to evaluate the change in exercise associated DFA a1 dynamics toward the end of a simulated ultramarathon and compare this to changes in HR and running economy while still performing dynamic exercise. At the completion of an ultramarathon, DFA a1 decreased with large effect size while running at low intensity compared to pre‐race values. Therefore, DFA a1 may offer an opportunity for assessment and real‐time tracking of physiologic status in terms of monitoring for fatigue and possibly as an early warning signal of systemic perturbation.

Early identification and diagnosis of mild cognitive impairment (MCI) in patients with parkinsonism (PDS) are critical. The aim of this study was to identify biomarkers of MCI in PDS using conventional electroencephalogram (EEG) power spectral analysis and detrended fluctuation analysis (DFA). In this retrospective study, patients with PDS who underwent an overnight polysomnography (PSG) study in our hospital from 2019 to 2020 were enrolled. Patients with PDS assessed by clinical examination and questionnaires were divided into two groups: the PDS with normal cognitive function (PDS-NC) group and the PDS with MCI (PDS-MCI) group. Sleep EEG signals were extracted and purified from the PSG and subjected to a conventional power spectral analysis, as well as detrended fluctuation analysis (DFA) during wakefulness, nonrapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. Forty patients with PDS were enrolled, including 25 with PDS-NC and 15 with PDS-MCI. Results revealed that compared with PDS-NC patients, patients with PDS-MCI had a reduced fast ratio ( alpha + beta / delta + theta ) and increased DFA during NREM sleep. DFA during NREM was diagnostic of PDS-MCI, with an area under the receiver operating characteristic curve of 0.753 (95% CI: 0.592–0.914) ( p < 0.05 ). Mild cognitive dysfunction was positively correlated with NREM-DFA ( r = 0.426 , p = 0.007 ) and negatively correlated with an NREM-fast ratio ( r = − 0.524 , p = 0.001 ). This suggested that altered EEG activity during NREM sleep is associated with MCI in patients with PDS. NREM sleep EEG characteristics of the power spectral analysis and DFA correlate to MCI. Slowing of EEG activity during NREM sleep may reflect contribution to the decline in NREM physiological function and is therefore a marker in patients with PDS-MCI.

Electroencephalogram (EEG) signals are widely used in diagnosis of epilepsy. Accurate classification of seizure types based on EEG signals can provide vital information for diagnosis and treatment. Since visual inspection and interpretation of seizure types are time consuming and prone to errors, a novel classification method combining wavelet packet decomposition (WPD) and local detrended fluctuation analysis (L-DFA) is proposed for the computer-aided diagnostic system. The proposed method is able to classify a wide variety of seizures automatically and accurately. As the first step towards this goal, raw EEG signals are decomposed by WPD according to intrinsic frequency bands of human brain. Then L-DFA is applied to characterise the dynamical fractal structure of sub-band signals. Finally, EEG signals are classified by support vector machine based on the combined fractal spectrum features. The experimental results on Temple University Hospital database show that the proposed method achieves a total classification accuracy of 97.80%, outperforming existing methods based on the same database.

Temporal cardiac properties provide alternative information in analyzing heart rate variability (HRV), which may be disregarded by the standard HRV analyses. Patients with congestive heart failure (CHF) are known to have distinct temporal features from the healthy individuals. However, the underlying mechanism leading to the variation remains unclear. Whether or not these parameters can finely classify the severity for CHF patients is uncertain as well. In this work, an electrocardiogram was monitored in advanced CHF patients using 24-h Holter in four conditions, including baseline, one and three months after atenolol therapy, and healthy individuals. Slope and area under the curve (AUC) of multiscale entropy (MSE) curve over short (scales 1–5) and long (scales 6–20) scales, and detrended fluctuation analysis (DFA) scaling exponents at short (4–11 beats) and intermediate (>11 beats) window sizes were calculated. The results show that short-time scale MSE-derived parameters (slope: −0.08 ± 0.10, −0.03 ± 0.10, 0.02 ± 0.06, 0.08 ± 0.06; AUC: 4.03 ± 2.11, 4.69 ± 1.28, 4.73 ± 0.94, and 6.17 ± 1.23) and short-time scale DFA exponent (0.79 ± 0.16, 0.95 ± 0.22, 1.11 ± 0.19, and 1.35 ± 0.20) can hierarchically classify all four conditions. More importantly, simulated R-R intervals with different fractions and amplitude of respiratory sinus arrhythmia (RSA) components were examined to validate our hypothesis regarding the essentiality of RSA in the improvement of cardiovascular function, and its tight association with unpredictability and fractal property of HRV, which is in line with our hypothesis that RSA contributes significantly to the generation of the unpredictability and fractal behavior of HR dynamics. NEW & NOTEWORTHY Temporal cardiac properties provide useful diagnostic parameters for patients with congestive heart failure (CHF). Our study hierarchically classified CHF patients with β-blocker treatment by using multiscale entropy and detrended fluctuation analysis. Also, we provided the evidence to validate the critical role of respiratory sinus arrhythmia in the fractal properties of heart rate variability.

Research on complexity and uncertainty of nonlinear signals has great significance in dynamic system analysis. In order to further analyze the detailed information from the financial data to acquire deeper insight into the complex system and improve the ability of prediction, we propose the entropy of scale exponential spectrum (EOSES) as a new measure. Combined with multiscale theory, we get multiscale EOSES. The scale exponential spectrum (SES) is derived from the scale exponent of detrended fluctuation analysis. As for the entropy, we choose Renyi entropy and fractional cumulative residual entropy to compare and analyze the results. Simulated data and financial time series are used to obtain further in-depth information on the EOSES. Compared with traditional methods, we find that Renyi EOSES over moving window can provide more details of complexity which include fractal structure and scale properties. Also, it reduces the influence of degree of fitting polynomial and has higher noise immunity. In addition, through the SES and EOSES, we can better research the properties and stages of stock markets and distinguish stock markets with different characteristics.

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 activi...

OBJECTIVES Physiologic signals are typically measured continuously in the critical care unit, but only recorded at intermittent time intervals in the patient health record. Low frequency data collection may not accurately reflect the variability and complexity of these signals or the patient's clinical state. We aimed to characterize how increasing the temporal window size of observation from seconds to hours modifies the measured variability and complexity of basic vital signs. DESIGN Retrospective analysis of signal data acquired between April 1, 2013, and September 30, 2015. SETTING Critical care unit at The Hospital for Sick Children, Toronto. PATIENTS Seven hundred forty-seven patients less than or equal to 18 years old (63,814,869 data values), within seven diagnostic/surgical groups. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Measures of variability (SD and the absolute differences) and signal complexity (multiscale sample entropy and detrended fluctuation analysis [expressed as the scaling component α]) were calculated for systolic blood pressure, heart rate, and oxygen saturation. The variability of all vital signs increases as the window size increases from seconds to hours at the patient and diagnostic/surgical group level. Significant differences in the magnitude of variability for all time scales within and between groups was demonstrated (p < 0.0001). Variability correlated negatively with patient age for heart rate and oxygen saturation, but positively with systolic blood pressure. Changes in variability and complexity of heart rate and systolic blood pressure from time of admission to discharge were found. CONCLUSIONS In critically ill children, the temporal variability of physiologic signals supports higher frequency data capture, and this variability should be accounted for in models of patient state estimation.

One critical issue in brain computer interface (BCI) studies is to extract imaginary movement patterns from electroencephalograph (EEG). In this study, two different techniques —detrended fluctuation analysis (DFA) and discrete wavelet packet transform— are combined (DWPT-DFA) for feature extraction. Both approaches are known as self-similarity quantifier techniques. In wavelet technique, mother wavelets play an important role. Herein, A customized mother wavelet utilizing event related desynchronization (ERD) potential patterns are extracted and updated automatically for individual subjects. Also, three predefined mother wavelets are used, and the results are compared with the customized mother wavelet. The predefined mother wavelets are db4, db8 and coiflet 4. The soft margin support vector machine with the generalized radial basis function (SSVM-GRBF) is employed to classify the DWPT-DFA features. For the efficiency of the method, nine subjects have participated to record EEG based on the imaginary hand movements. The ERDs and features are extracted from FC1 and CP6 channels. Results show that the combination of the DWPT and DFA with the personalize ERD mother wavelet gives the best accuracy of 85.33% with p

The knowledge of geological strata sequences and their exact boundaries depths is of a great importance in the characterization of oil and gas reservoirs. The aim of automated well-to-well correlation approaches is to facilitate and accelerate the procedure of geological boundary detection. In this study, we implement an automatic well-to-well correlation approach based on pattern extraction from well-logs. The well-log patterns are recognized by calculating several statistical and fractal parameters. As a fractal parameter, we select the wavelet standard deviation exponent, calculated by the discrete wavelet transform. Furthermore, to select the proper wavelet function, as the heart of wavelet transform, the energy to Shannon entropy ratio criterion is implemented. The statistical pattern recognition parameters of this study include average value, maximum to minimum ratio, coefficient of variation, and the trend angle of well-log data (i.e., Gamma ray log) in a window around the geological boundary. Moreover, the analysis of variance (ANOVA) tool and the Tukey multiple comparison method are implemented to evaluate the effectiveness of each parameter (i.e., fractal or statistical parameters) during the determination of boundary depths. The gamma ray logs from three wells of an oil field are used as a dataset for the evaluation of our algorithm. The outputs of our methodology are also compared with the new detrended fluctuation analysis (DFA) method, which show promising outcomes over it. The results show that the average value of the signal, among the analyzed parameters, is the most effective parameter; however, implementing the combination of all fractal and statistical parameters can improve the accuracy of the geological boundary detection.

We discuss the use of fractal analysis of geophysical big data as a precursory method for the estimation of current seismic hazard. We begin by analyzing GPS time series of land surface displacements recorded in various regions of the planet exhibiting different degrees of seismic activity. We show how the quantitative measure of chaoticity based on a modified detrended fluctuation analysis (DFA) method can be used to distinguish critical and noncritical regimes of evolution of the Earth’s crust. Having established the reference values for the measure of chaoticity, we then choose California as the case study region and apply modified DFA to over 1200 time series for determining local areas currently exposed to higher seismic hazard. We comprehensively analyze the results obtained and compare them with those produced by conventional risk assessment methods. We demonstrate consistency of our results and yet discover that the suggested approach allows us to quantify two qualitatively different mechanisms involved in the growth of seismic hazards. We propose a hypothesis which relates these mechanisms to the known mechanisms of earthquake generation that evince a growth of collective behavior in the Earth’s crust on the eve of an earthquake. In order to verify the hypothesis, we analyze the same GPS data by an independent statistical method, canonical coherence analysis (CCA), which permits us to quantify the degree of synchronized behavior of constituents of the Earth’s crust. The CCA method confirms our previously obtained estimates, thus supporting fractal analysis of GPS time series as a suitable precursory method for current seismic hazard assessment.

Experimental time series often contain bad segments that arise from artifacts, changes in experimental conditions, or failures in recording equipment. Such segments are usually removed from the time series during the preprocessing stage that can alter the correlation or other properties of the signals. Aiming to reveal how the effects of data loss depend on the amount of missing data, we consider here different regimes of regular and chaotic dynamics with excluded segments. Using several data processing techniques, namely, the wavelet-transform modulus maxima (WTMM) approach, the detrended fluctuation analysis (DFA) and the multiresolution analysis based on the discrete wavelet-transform (DWT), we demonstrate essentially different effects of the missing data for positively correlated time series and anti-correlated signals. All the techniques show that positively correlated time series are significantly less sensitive to excluded segments and enable the characterization of the object’s properties even under the condition of an extreme data loss. We verify the ability of characterizing physiological systems using an example of the cerebrovascular dynamics based on time series with missing data. A weak sensitivity of the cerebral blood flow to data loss is an important issue for diagnostic-related studies.

Pulmonary hypertension (PH) is a fatal disease—even with state-of-the-art medical treatment. Non-invasive clinical tools for risk stratification are still lacking. The aim of this study was to investigate the clinical utility of heart rhythm complexity in risk stratification for PH patients. We prospectively enrolled 54 PH patients, including 20 high-risk patients (group A, defined as WHO functional class IV or class III with severely compromised hemodynamics), and 34 low-risk patients (group B). Both linear and non-linear heart rate variability (HRV) variables, including detrended fluctuation analysis (DFA) and multiscale entropy (MSE), were analyzed. In linear and non-linear HRV analysis, low frequency and high frequency ratio, DFAα1, MSE slope 5, scale 5, and area 6–20 were significantly lower in group A. Among all HRV variables, MSE scale 5 (AUC: 0.758) had the best predictive power to discriminate the two groups. In multivariable analysis, MSE scale 5 (p = 0.010) was the only significantly predictor of severe PH in all HRV variables. In conclusion, the patients with severe PH had worse heart rhythm complexity. MSE parameters, especially scale 5, can help to identify high-risk PH patients.

Study Objectives Kleine-Levin syndrome (KLS) is a rare recurrent hypersomnia. Our study aimed at monitoring the movements of patients with KLS using actigraphy and evaluating their circadian rhythm. Methods Twenty young patients with KLS and 14 age-matched controls were recruited. Each individual wore an actigraphy for more than 6 months to monitor at least two attacks. Controls kept wearing the device for at least 7 days. The activity counts were averaged in hourly basis and the day-to-night amplitude was quantified by the differences of the averaged activity counts during daytime and nighttime. The hourly activities of different days were aligned and averaged to construct the circadian profile. Parametric and nonparametric estimation of circadian rhythm was calculated. We applied detrended fluctuation analysis to evaluate the temporal correlations beneath the activity fluctuations at multiple time scales. Results Circadian rhythm in asymptomatic period showed no significant difference compared to the controls. During hypersomnia attack, the amplitude of the circadian rest–active rhythms drastically decreased and decreased interdaily stability (IS) was found, as well as significant decreased M10 and short-time fractal correlation (α1). Drastically decreased mean and standard deviation of activity were noted, compared to the pre-attack phase and recovery phase. α1 and M10 increased during the late attack phase, and overcompensated IS was noted in the recovery phase. Conclusions This study confirmed that circadian rest–active rhythms was affected when KLS hypersomnia attack. Several parameters including M10, IS, and α1 may be physiological markers of KLS, which can help to predict the end of hypersomnia episodes.

OBJECTIVE Favorable neurodevelopmental outcomes in epileptic spasms (ES) are tied to early diagnosis and prompt treatment, but uncertainty in the identification of the disease can delay this process. Therefore, we investigated five categories of computational electroencephalographic (EEG) measures as markers of ES. METHODS We measured 1) amplitude, 2) power spectra, 3) Shannon entropy and permutation entropy, 4) long-range temporal correlations, via detrended fluctuation analysis (DFA) and 5) functional connectivity using cross-correlation and phase lag index (PLI). EEG data were analyzed from ES patients (n = 40 patients) and healthy controls (n = 20 subjects), with multiple blinded measurements during wakefulness and sleep for each patient. RESULTS In ES patients, EEG amplitude was significantly higher in all electrodes when compared to controls. Shannon and permutation entropy were lower in ES patients than control subjects. The DFA intercept values in ES patients were significantly higher than control subjects, while DFA exponent values were not significantly different between the groups. EEG functional connectivity networks in ES patients were significantly stronger than controls when based on both cross-correlation and PLI. Significance for all statistical tests was p < 0.05, adjusted for multiple comparisons using the Benjamini-Hochberg procedure as appropriate. Finally, using logistic regression, a multi-attribute classifier was derived that accurately distinguished cases from controls (area under curve of 0.96). CONCLUSIONS Computational EEG features successfully distinguish ES patients from controls in a large, blinded study. SIGNIFICANCE These objective EEG markers, in combination with other clinical factors, may speed the diagnosis and treatment of the disease, thereby improving long-term outcomes.

Covid-19 is a dangerous communicable virus which lets down the world economy. Severe respiratory syndrome SARS-COV-2 leads to Corona Virus Disease (COVID-19) and has the capability of transmission through human-to-human and surface-to-human transmission leads the world to catastrophic phase. Computational system based biological signal analysis helps medical officers in handling COVID-19 tasks like ECG monitoring at Intensive care, fatal ventricular fibrillation, etc., This paper is on diagnosing heart dysfunctions such as tachycardia, bradycardia, ventricular fibrillation, cardiac arrhythmia using fuzzy relations and artificial intelligence algorithm. In this study, the heart pulse base signal and features like spectral entropy, largest lyapunov exponent, Poincare plot and detrended fluctuation analysis are extracted and presented for classification purpose. The RR intervals of Poincare plot summarize RR time series obtained from an ECG in one picture, and a time interval quantities derives information duration of HRV. This analysis eases the prediction of heart rate fluctuation due to Covid or other heart disorders. The better accuracy level in diagnosing heart pulse irregularity using Artificial Neural network(ANN) is an integer value (0 to 4)but for Fuzzy Classifier, it is 0.8 to 0.9.The processing time for analyzing heart dysfunctionalties is 0.05 s using ANN which is far better than Fuzzy classifier.

This paper performs the Multiracial Detrended Fluctuation Analysis (MFDFA) of fine resolution (0.25°x0.25°) daily gridded rainfall of Kerala Meteorological subdivision, India. First the MFDFA method is applied for long term rainfall datasets of different grid points for 1901-2013 period. The results show that rainfall time series of majority of the grid points (∼66 %) exhibits short term persistence with strong multifractal characteristics. Except at three locations, the asymmetry index of rainfall time series displays right skewed spectra indicating local high fluctuations and fine structured series. Further, MFDF analysis was performed after splitting the time series into two with respect to the global climatic shift of 1976/77. The results of pre and post climatic shift period shows that 34 grids (out of 65) exhibited an increase multifractal spectral width while 41 grids exhibited a increase in persistence after the shift. The study further showed that the persistence property changed from short to long term in 23 % grids, which also indicated a change in pattern of rainfall within the subdivision. The results showed that a strong correlation 0.994 exists between Hurst exponent and Holder exponent, reinforcing the universal association between the two multifractal exponents.

Weak fault signals are always embedded in mass vibration noise in many gear systems, thus making difficulty in gear fault diagnosis. In order to extract weak fault signals, a new multi-scale-based multi-fractal analysis (MMA) method is introduced in this paper, which is based on classical multi-fractal detrended fluctuation analysis (MFDFA) framework. Firstly, the Hurst surface features are utilized to describe the characteristics with multifractal of the vibration signal, which have been proved to be sensitive to the dynamical responses of the various gear faults. Secondly, a moving fitting window is added to the MFDFA framework to sweep through all the range of the scales, and then obtain final multi-scale features, whose purpose is to magnify those features in some important scales and weaken the rest scales. In addition, other techniques, such as the distance-based feature selection and the random forest (RF) classifier, are also introduced into the gearbox fault diagnosis procedure to verify the effectiveness of extracted features for differentiating various gear states. Experiments using the Qianpeng testbed (QT) prove that the MMA method can effectively extract weak signals, and has higher diagnostic accuracy than other algorithms, such as empirical mode decomposition (EMD), wavelet transform (WT), and classical MFDFA.

Refereed/Peer-reviewed Immersive virtual reality (VR) can cause acute sickness, visual disturbance, and balance impairment. Some manufacturers recommend intermittent breaks to overcome these issues; however, limited evidence examining whether this is beneficial exists. The aim of this study was to examine whether taking breaks during VR gaming reduced its effect on postural sway during standing balance assessments. Twenty-five people participated in this crossover design study, performing 50 min of VR gaming either continuously or with intermittent 10 min exposure/rest intervals. Standing eyes open, two-legged balance assessments were performed immediately pre-, immediately post- and 40 min post-exposure. The primary outcome measure was total path length; secondary measures included independent axis path velocity, amplitude, standard deviation, discrete and continuous wavelet transform-derived variables, and detrended fluctuation analysis. Total path length was significantly (p < 0.05) reduced immediately post-VR gaming exposure in the intermittent rest break group both in comparison to within-condition baseline values and between-condition timepoint results. Conversely, it remained consistent across timepoints in the continuous exposure group. These changes consisted of a more clustered movement speed pattern about a lower central frequency, evidenced by signal frequency content. These findings indicate that caution is required before recommending rest breaks during VR exposure until we know more about how balance and falls risk are affected

Purpose: Falls are one of the main causes of injuries in older adults. This study evaluated a low-cost footswitch device that was designed to measure gait variability and investigates whether there are any relationships between variability metrics and clinical balance tests for individuals with a history of previous falls. Methods: Sixteen older adults completed a history of falls questionnaire, three functional tests related to fall risk, and walked on a treadmill with the footswitch device. We extracted the stride times from the device and applied two nonlinear variability analyses: coefficient of variation and detrended fluctuation analysis. Results: The temporal variables and variability metrics from the footswitch device correlated with gold-standard measurements based on ground reaction force data. One variability metric (detrended fluctuation analysis) showed a significant relationship with the presence of past falls with a sensitivity of 43%. Conclusion: This feasibility study demonstrates the basis for using low-cost footswitch devices to predict fall risk.

Wind farms can be regarded as complex systems that are, on the one hand, coupled to the nonlinear, stochastic characteristics of weather and, on the other hand, strongly influenced by supervisory control mechanisms. One crucial problem in this context today is the predictability of wind energy as an intermittent renewable resource with additional non-stationary nature. In this context, we analyze the power time series measured in an offshore wind farm for a total period of one year with a time resolution of 10 min. Applying detrended fluctuation analysis, we characterize the autocorrelation of power time series and find a Hurst exponent in the persistent regime with cross-over behavior. To enrich the modeling perspective of complex large wind energy systems, we develop a stochastic reduced-form model ofpower time series. The observed transitions between two dominating power generation phases are reflected by a bistable deterministic component, while correlated stochastic fluctuations account for the identified persistence. The model succeeds to qualitatively reproduce several empirical characteristics such as the autocorrelation function and the bimodal probability density function., Comment: 20 pages, 8 figures

Parkinson’s Disease patients suffer from gait impairments such as reduced gait speed, shortened step length, and deterioration of the temporal organization of stride duration variability (i.e., breakdown in Long-Range Autocorrelations). The aim of this study was to compare the effects on Parkinson’s Disease patients’ gait of three Rhythmic Auditory Stimulations (RAS), each structured with a different rhythm variability (isochronous, random, and autocorrelated). Nine Parkinson’s Disease patients performed four walking conditions of 10–15 min each: Control Condition (CC), Isochronous RAS (IRAS), Random RAS (RRAS), and Autocorrelated RAS (ARAS). Accelerometers were used to assess gait speed, cadence, step length, temporal organization (i.e., Long-Range Autocorrelations computation), and magnitude (i.e., coefficient of variation) of stride duration variability on 512 gait cycles. Long-Range Autocorrelations were assessed using the evenly spaced averaged Detrended Fluctuation Analysis (α-DFA exponent). Spatiotemporal gait parameters and coefficient of variation were not modified by the RAS. Long-Range Autocorrelations were present in all patients during CC and ARAS although all RAS conditions altered them. The α-DFA exponents were significantly lower during IRAS and RRAS than during CC, exhibiting anti-correlations during IRAS in seven patients. α-DFA during ARAS was the closest to the α-DFA during CC and within normative data of healthy subjects. In conclusion, Isochronous RAS modify patients’ Long-Range Autocorrelations and the use of Autocorrelated RAS allows to maintain an acceptable level of Long-Range Autocorrelations for Parkinson’s Disease patients’ gait.

To effectively utilize a feature set to further improve fault diagnosis of a rolling bearing vibration signal, a method based on multi-fractal detrended fluctuation analysis (MF-DFA) and smooth intrinsic time-scale decomposition (SITD) was proposed. The vibration signal was decomposed into several proper rotation components by applying this new SITD method to overcome noise effects, preserve the effective signal, and improve the signal-to-noise ratio. Wavelet analysis was embedded in iteration procedures of intrinsic time-scale decomposition (ITD). For better results, an adaptive threshold function was used for signal recovery from noisy proper rotation components in the wavelet domain. Additionally, MF-DFA was used to reveal the multi-fractality present in the instantaneous amplitude of the proper rotation components. Finally, linear local tangent space alignment was applied for feature dimension reduction and to obtain fault characteristics of different types, further improving identification accuracy. The performance of the proposed method is determined to be superior to that of the ITD-MF-DFA method.

Epilepsy is a psychological issue that causes ridiculous, repetitive seizures. A seizure is an unexpected surge of electrical activity in the cerebrum. Since traditional time or recurrence, area examination is discovered insufficient to portray the qualities of non-fixed signals, for example, electroencephalography (EEG) signal. In this paper, we propose to change the EEG information utilizing Detrended Fluctuation Analysis, Singular Value Decomposition Entropy, Higuchi Fractal Dimension, and Petrosian Fractal Dimension works and plan vectors. These vectors are utilized as highlights for ten distinctive Machine Learning classifiers: Logistic Regression, Support Vector Classifier, Linear Support Vector Classifier, K Nearest Neighbors Classifier, Naive Bayes Classifier, Random Forest Classifier, Decision Tree Classifier, Stochastic Gradient Descent Classifier, Multilayer Perceptron Classifier, Gaussian Process Classifier, to group epileptic seizure starting from various parts and condition of the cerebrum. Our outcomes show that the proposed strategy gives better exactness, accuracy, and review in contrast with the current strategies for multiclass Epileptic Seizure Classification.

ObjectiveFavorable neurodevelopmental outcomes in epileptic spasms (ES) are tied to early diagnosis and prompt treatment, but uncertainty in the identification of the disease can delay this process. Therefore, we investigated five computational electroencephalographic (EEG) measures as markers of ES.MethodsWe measured 1) amplitude, 2) power spectra, 3) entropy, 4) long-range temporal correlations, via detrended fluctuation analysis (DFA) and 5) functional connectivity of EEG data from ES patients (n=40 patients) and healthy controls (n=20 subjects), with multiple blinded measurements during wakefulness and sleep for each patient.ResultsIn ES patients, EEG amplitude was significantly higher in all electrodes. Shannon and permutation entropy were lower in ES patients than control subjects, while DFA intercept values in ES patients were significantly higher than control subjects. DFA exponent values were not significantly different between the groups. EEG functional connectivity networks in ES patients were significantly stronger than controls. Using logistic regression, a multi-attribute classifier was derived that accurately distinguished cases from controls (area under curve of 0.96).ConclusionsComputational EEG features successfully distinguish ES patients from controls in a large, blinded study.SignificanceThese objective EEG markers, in combination with other clinical factors, may speed the diagnosis and treatment of the disease, thereby improving long-term outcomes.HighlightsObjective computational EEG features may aid diagnosis of epileptic spasms (ES)ES EEG has increased delta and theta power and decreased entropy relative to controlsStronger functional connectivity networks differentiate ES patients from controls

Rolling bearings are the key component of rotating machinery. In the early stage of bearing fault, the faint feature is susceptible to environmental noise. Therefore, weak fault characteristics are difficult to be extracted. An early fault diagnosis method based on minimum entropy deconvolution (MED), detrended fluctuation analysis (DFA), and improved K-nearest neighbor (IKNN) is proposed in this paper. First, the MED filter is employed for the enhancement of the fault components which are drowned in the time-domain vibration signal. The DFA method is then used for the extraction of fault features from the enhanced signals. Finally, the proposed IKNN algorithm is applied as the classifier to identify the fault types. Three kinds of early failures occurred in the inner race, cage, and outer race are performed on the bearing test rig. The experimental results show that the average classification accuracy of the proposed diagnosis method can reach 97.5%.

Objectives To investigate the use of a set of dynamical features, extracted from surface electromyography, to study upper motor neuron (UMN) degeneration in amyotrophic lateral sclerosis (ALS). Methods We acquired surface EMG signals from the upper limb muscles of 13 ALS patients and 20 control subjects and classified them according to a novel set of muscle activity features, describing the temporal and frequency dynamic behavior of the signals, as well as measures of its complexity. Using a battery of classification approaches, we searched for the most discriminating combination of those features, as well as a suitable strategy to identify ALS. Results We observed significant differences between ALS patients and controls, in particular when considering features highlighting differences between forearm and hand recordings, for which classification accuracies of up to 94% were achieved. The most robust discriminations were achieved using features based on detrended fluctuation analysis and peak frequency, and classifiers such as decision trees, random forest and Adaboost. Conclusion The current work shows that it is possible to achieve good identification of UMN changes in ALS by taking into consideration the dynamical behavior of surface electromyographic (sEMG) data.

In this paper, a detrended fluctuation analysis (DFA) method is applied to assess the depth of anesthesia. General assessment of the depth of anesthesia (DoA) method is monitoring the cerebral cortex activity. The purpose of this study is to develop a new index which is different from the cerebral cortex activity monitoring index. Forty surgery patients (25 males + 15 females) were investigated. For every patient, the procedure is divided into three stages, before surgery 5 minute (stage A), during the surgery (stage B) and after the surgery 5 minute (stage C). The DFA values of each patient were calculated, and the mean ± standard deviation of DFA in 40 patients were calculated. Calculated results show that patients before surgery α, α1 and α2 values were 0.795 ± 0.188, 0.862 ± 0.165 and 0.839 ± 0.200, during surgery were 0.696 ± 0.353, 0.650 ± 0.107 and 0.690 ± 0.215, and after surgery were 0.803 ± 0.195, 0.871 ± 0.172 and 0.846 ± 0.209. The results have been shown significant differences (P < 0.05) in α and α2 values (including stages A and B, stages A and C) and none significant difference in α1 value (any two stages combination A, B and C). Therefore, the value of α and α2 have index effect in distinguish the physiological characteristics of patients before and after anesthesia.