Interval Time Series Research Articles

Evaluation of Distraction Effect of Music Stimuli During Cycling Exercise With Low Intensity in Terms of Multiple Time Scale

There is a lack of research on the distraction effects of music stimuli with the multiple time scales of biological functions. It should be preferable to propose an effective strategy for evaluating the time varying behavior of psychological responses to music during exercise on autonomic nervous activity. From the RR interval time series of electrocardiograms recorded during cycling with low intensity, we estimated autonomic nervous activity related indices: time and frequency domain indices estimated from the fluctuations of the RR interval. Then we searched the temporal distribution of the stimulus response module appeared in the time varying behavior. The inspection of distraction effects was done for properly selected autonomic nervous activity. Then related indices and the ratings of perceived exertion for evaluating appropriate participants based on both the impression of favorability to music and the stimulus response module occurrences. The results inferred that the multiple time scales strategy could be of help to assess the suitable approach to identify the distraction effects of music stimuli.

A Bio-Inspired Chaos Sensor Model Based on the Perceptron Neural Network: Machine Learning Concept and Application for Computational Neuro-Science.

The study presents a bio-inspired chaos sensor model based on the perceptron neural network for the estimation of entropy of spike train in neurodynamic systems. After training, the sensor on perceptron, having 50 neurons in the hidden layer and 1 neuron at the output, approximates the fuzzy entropy of a short time series with high accuracy, with a determination coefficient of R2~0.9. The Hindmarsh-Rose spike model was used to generate time series of spike intervals, and datasets for training and testing the perceptron. The selection of the hyperparameters of the perceptron model and the estimation of the sensor accuracy were performed using the K-block cross-validation method. Even for a hidden layer with one neuron, the model approximates the fuzzy entropy with good results and the metric R2~0.5 ÷ 0.8. In a simplified model with one neuron and equal weights in the first layer, the principle of approximation is based on the linear transformation of the average value of the time series into the entropy value. An example of using the chaos sensor on spike train of action potential recordings from the L5 dorsal rootlet of rat is provided. The bio-inspired chaos sensor model based on an ensemble of neurons is able to dynamically track the chaotic behavior of a spike signal and transmit this information to other parts of the neurodynamic model for further processing. The study will be useful for specialists in the field of computational neuroscience, and also to create humanoid and animal robots, and bio-robots with limited resources.

Autonomous Planning Algorithm for Satellite Laser Ranging Tasks Based on Rolling Horizon Optimization Framework

Observation task planning is a key issue and the first step in the development of automated Satellite Laser Ranging (SLR) systems. Aiming at the problem of dynamic change of cloud cover during SLR operation, this paper proposes an autonomous mission planning algorithm for SLR based on the Rolling Horizon Optimization (RHO) framework. A hybrid event- and cycle-driven replanning mechanism is adopted, and four functional modules, rolling, planning, information acquisition and decision-making, are established to decompose the SLR observation task planning process into a series of static planning intervals. An improved ant colony algorithm is proposed and utilized to realize the autonomous planning of SLR system observation tasks, and the above autonomous planning algorithm is verified and analyzed based on the SLR system at station 7237. The results show that the above algorithm can effectively increase the number of observation satellites and revenue under cloud disturbance, solve the problems of low efficiency and poor interference resistance of conventional static algorithms, and provide a new research idea for the establishment of an unattended SLR system.

A forecasting method for non-equal interval time series based on recurrent neural network

Unknown data can be forecast by learning the patterns of change from the historical data at regular intervals. However, when samples are not available at a regular interval, the forecasting task becomes very challenging. This paper proposes an improved Gated Recurrent Unit model for non-equal interval time series, abbreviated as NITS-GRU, to model the data of the series without resampling and data imputation. NITS-GRU includes GRU-ODE module, multivariate-based sample correlation calculation module, and information fusion module. The GRU-ODE module generates forecast information of adjacent samples by calculating their hidden layer states, as well as the accumulated difference information between each adjacent sample and forecasting sample over time intervals. Meanwhile, we calculate the correlations between each adjacent sample and forecasting sample based on multiple variables, and adopt attention mechanism to obtain correlation weights of these adjacent samples. The information fusion module applies the correlation weights on forecast information of the adjacent samples generated by the GRU-ODE module, to obtain the final forecast results. Experiments on different datasets demonstrate that NITS-GRU outperforms the state-of-the-art baselines for forecasting unknown data in non-equal interval time series.

Determining Undersampled Coastal Tidal Harmonics Using Regularized Least Squares

AbstractSatellite altimetry, which measures water level with global coverage and high resolution, provides an unprecedented opportunity for a wide and refined understanding of the changing tides in the coastal area. But its sampling frequency is too low to satisfy the Nyquist frequency requirement and too few data points per year are available to recognize a sufficient number of tidal constituents to capture the trend of tidal changes on a yearly basis. To address these issues, a novel regularized least‐square approach is developed to relax the sampling interval limit to the range of 9–11 days, reaching the revisit time of the existing satellites. In this method, the prior information of the regional tidal amplitudes is used to support a least square analysis to obtain the amplitudes of the tidal constituents for water elevation time series of different lengths and time intervals. Synthetic data experiments performed in Delaware Bay and Galveston Bay showed that the proposed method can determine the tidal amplitudes with high accuracy and the sampling interval can be extended to the application level of major altimetry satellites. The proposed algorithm was further validated using the data of the altimetry mission, Jason‐3, to show its applicability to irregular and noisy data. The new method could help identify the changing tides with sea‐level rise and anthropogenic activities in coastal areas, informing coastal flooding risk assessment and ecosystem health analysis.

A combined model of Hawkes-ICEEMDAN-Catboost-Holt for interval-valued carbon price forecasting

Accurate carbon price forecasting is crucial for efficiently operating both carbon trading and financial markets. This study proposes a Hawkes-ICEEMDAN-Catboost-Holt combined interval forecasting model to improve the accuracy of carbon price forecasts. First, the t-SNE-LLE second-order dimensionality reduction was performed on the collected search index data to quantify the attention of netizens. Subsequently, the attention series was converted into an interval series using the Hawkes exponential decay model, and further decomposed into radius and center series. In addition, the radius and center series were decomposed using ICEEMDAN and then reconstructed. Finally, the carbon price interval combined forecast was based on the Catboost–Holt model. The proposed model exhibits high accuracy and strong stability in comparison to other forecast models. The Hawkes exponential decay of netizens’ attention is creatively included in the influence mechanism of carbon price fluctuations, effectively improving forecast accuracy. The interval decomposition and reconstruction methods provide an innovative development path for interval forecasting research. The residual and trend series obtained from interval reconstruction can be forecasted using Catboost and Holt, respectively, thus significantly improving the accuracy and stability of interval forecasts and enhancing the decision-making process for carbon trading and financial markets.

Novel AI-based HRV analysis (NAIHA) in healthcare automation and related applications.

Heart rate variability (HRV) analysis computed on R-R interval series of ECG records with heavy burden of ectopic beats or non-sinus rhythm can significantly distort HRV parameters and hence clinically ineligible for HRV analysis. Yet, existing algorithmic methods of HRV analysis do not check such eligibility and require manual identification of eligible window (portion of ECG record) to ensure reliability. We aimed to propose a robust algorithm with a sliding window feature to automate the identification of an eligible window, if available, which compute HRV parameters within that window obviating manual input. The proposed algorithm classifies each window as either eligible or ineligible. With a window classified eligible, we stop sliding through the record, otherwise we move to the next window and repeat the eligibility identification process, until either an eligible window is found, or all windows are exhausted. When evaluated on random subset of 100 records from MIMIC-III waveform database, the proposed algorithm excluded every ineligible record, and missed only 1.25% of eligible ones. The HRV parameters computed using proposed method closely approximated the standard HRV analysis with Pearson correlation coefficients (ideally one) and fractions of variance unexplained (ideally zero) ranging from 96.3% to 99.8% and 0.34% to 7.43%, respectively. When translated into practice, proposed algorithm will reduce clinicians'' burden without compromising the accuracy of HRV analysis, potentially leading to its wider adoption.

Monthly runoff time series interval prediction based on WOA-VMD-LSTM using non-parametric kernel density estimation

Monthly runoff time series interval prediction based on WOA-VMD-LSTM using non-parametric kernel density estimation

Working memory for time intervals: Another manifestation of the central tendency effect.

The relationship between working memory and time perception has been typically investigated using dual-task paradigms (e.g., testing timing performance during a concurrent task). To our knowledge, none of these studies used time intervals as the target stimulus to be remembered. The current study investigated the working memory for time intervals by asking participants to reproduce durations they experienced at different orders in a series of experienced intervals (n-back task). One of the experiments was conducted online and the other one in the lab setting. Results showed a central tendency bias and additive elongation of time reproductions with increasing working memory load. Our results also showed that participants assigned different weights to experienced intervals based on their order of presentation (higher weight to the target interval). We conclude that the recall of intervals from working memory under high cognitive load leads to a central tendency effect, which is known to be induced by the temporal context and present particularly in aging and in those with Parkinson's disease.

Density Functions of Periodic Sequences of Continuous Events

Periodic Geometry studies isometry invariants of periodic point sets that are also continuous under perturbations. The motivations come from periodic crystals whose structures are determined in a rigid form, but any minimal cells can discontinuously change due to small noise in measurements. For any integer kge 0, the density function of a periodic set S was previously defined as the fractional volume of all k-fold intersections (within a minimal cell) of balls that have a variable radius t and centers at all points of S. This paper introduces the density functions for periodic sets of points with different initial radii motivated by atomic radii of chemical elements and by continuous events occupying disjoint intervals in time series. The contributions are explicit descriptions of the densities for periodic sequences of intervals. The new densities are strictly stronger and distinguish periodic sequences that have identical densities in the case of zero radii.

Detecting the subthreshold signal in a neural network via statistical complexity measure

This paper proposes an information theory approach for detecting the subthreshold signal in a small-world network composed of Fitz Hugh-Nagumo (FHN) neurons. Statistical complexity measure (SCM) and normalized Shannon-entropy (NSE) have been defined based on the specific and nonconsecutive firing time intervals series, and employed to quantify the stochastic multiresonance (SMR) phenomena in this small-world neural network. The results show that there are several maxima of SCM and several minima of NSE at various optimal noise levels, which is regarded as the signature of the occurrence of SMR. This also implies that the subthreshold signal can be accurately detected across multiple levels of noise. More intriguingly, we have discovered that the degree of this neural network can induce the generation of multiple resonance-like behaviors. In addition, our findings demonstrate that the SCM outperforms the traditional signal-to-noise ratio (SNR) in accurately identifying firing dynamical regularity under certain noisy conditions. Furthermore, the SCM can capture subtle resonance-like behavior induced by the degree, which the SNR is unable to detect. Thus, the SCM could have potential power on detecting weak signals in neural networks.

Epidemic situation of cataract prevalence in the Voronezh Region

Cataract is one of the most common causes of vision loss, which determines the relevance of epidemiological studies. The purpose of this work was to assess the epidemic situation of cataracts among the adult population of the Voronezh region for 2015–2019. The material of the study was the data of the reporting form No. 12 of Rosstat for 2015–2019. A retrospective epidemiological analysis of the indicators of general morbidity, growth/decline rates, average annual growth/loss rates in the Voronezh region, Voronezh, and the administrative territories of the region was carried out. To assess the distribution of indicators in the interval series, the percentile method was used. Data processing was carried out using the Microsoft Excel program, the Student’s t-test was used to test statistical hypotheses, the significance level (p) was less than 0.05. According to the results of the analysis, the prevalence of cataracts in the Voronezh region and Voronezh for 2015–2019 was characterized by a moderate upward trend. In Voronezh, the indicators of general morbidity were significantly higher than the regional average. The level of cataract prevalence in the Voronezh Region was characterized by pronounced unevenness. The ranking of the administrative districts of the Voronezh Region was carried out using the percentile method and by the level of the tendency to increase/decrease in the average annual indicator. Stabilization of the epidemic process of cataracts was observed only in one district of the region — Ostrogozhsky. The rest of the territory of the region was conditionally divided into 2 parts: in half of the districts there was a tendency to increase, in the second half — to decrease. The level of the average annual growth rate in areas with a pronounced tendency to growth averaged 43.49 ± 7.58 %, the level of the average annual rate of decline in areas with a pronounced tendency to decrease morbidity averaged –23.79 ± 2.69 %. Thus, the epidemiological analysis of the data made it possible to determine the main trends in the overall incidence of cataracts in the administrative districts and the Voronezh Region as a whole, to rank the territory by the level of prevalence and trends.

Precision Public Health Opportunity: Heat-Associated Mortality Greater among Pilgrims during Hajj

Precision Public Health Opportunity: Heat-Associated Mortality Greater among Pilgrims during Hajj

Examining the impact of the COVID-19 pandemic on hospital-associated Clostridioides difficile infection

Background: The epidemiology of Clostridioides difficile infection (CDI) is complex, and the COVID-19 pandemic has had extreme impacts on known risk factors such as comorbidity burden and antibiotic prescribing. However, whether these changes have affected the incidence of hospital-associated CDI (HA-CDI) remains unknown. We compared incidence and trends of HA-CDI before and after the pandemic onset, and we assessed the impact of changes in key CDI-related risk factors. Methods: We conducted an interrupted time-series study (March 2018–March 2021) of adult inpatients hospitalized 4 or more days with no known CDI on admission at a 576-bed academic medical center. Our primary outcome was monthly HA-CDI per 10,000 patient days. We performed segmented linear regression to compare the preinterruption trend in HA-CDI rate to the postinterruption slope and level change. We established a series of 30-day intervals before and after the interruption timepoint of March 23, 2020, which corresponds with the Oregon stay-at-home executive order. The data included 24 preinterruption time points and 12 postinterruption time points. We also assessed changes in slope and trend for known HA-CDI risk factors. Results: We included 34,592 inpatient encounters in our prepandemic period and 10,932 encounters in our postinterruption period. The mean prepandemic HA-CDI rate was 4.07 cases per 10,000 patient days. After the pandemic onset, the rate was 3.6 per 10,000 patient days. However, the observed differences in rate (both in terms of slope and level) were not statistically significant (P = .90 for level; P = .60 for slope change). We observed a significant decrease in admissions per 30 days (1,441 vs 911; level-change P < .0001) and a slight increase in the mean number of Elixhauser comorbidities (1.96 vs 2.07; level-change P = .05). We also observed significant increases in both frequency and intensity of antibiotic use, with an increase average days of therapy per encounter (5.8 vs 7.2; level-change P = .01; slope-change P < .0001) and in antibiotic spectrum index (ASI) points per antibiotic day (4.4 vs 4.9; P < .0001). We observed a consistent downward trend for case days of CDI colonization pressure per hospital day (preinterruption slope P < .0001), which remained consistent after the pandemic onset (P = 0.5 for postinterruption slope change) (Fig. 1). Conclusions: Despite significant increases in high-intensity antibiotic use and comorbidity burden, we did not observe significant differences in HA-CDI after the pandemic onset. This may be due to the significant decrease in colonization pressure in the postpandemic period. Further research is required to fully understand the impact of the pandemic-related changes onDisclosures: None

A new complex belief entropy of [formula omitted] divergence with its application in cardiac interbeat interval time series analysis

With outstanding capability to express uncertain information, complex evidence theory can be widely applied in a multiplicity of fields of representation and fusion of information. However, in complex evidence theory, conflict measurement is still an open issue. To address this problem, we propose a new complex belief χ2 divergence measure (CBχ2), which has the ability to measure the discrepancy between the complex basic belief assignments (CBBAs). In addition, the proposed CBχ2 divergence measure satisfies the properties of nonnegativity, nondegeneracy and symmetry. Particularly, when the CBBA degenerates to classical basic belief assignments (BBA), the proposed divergence can also measure the conflict well. On this basis, a novel BEoCBχ2D is proposed to measure the complexity of cardiac interbeat interval time series in biological system. An application in cardiac interbeat interval time series analysis demonstrates that BEoCBχ2D has better accuracy.

Patterns of pulmonary vein activity that may help identify patients who need further ablation beyond pulmonary vein isolation in persistent AF

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Swiss National Science Foundation Background Pulmonary vein isolation (PVI) is the cornerstone interventional treatment for AF. While this strategy is effective in paroxysmal AF, the success rate of this approach is more limited in persistent AF (PsAF). In these pts, the main challenge is to discriminate those who will respond to PVI only, from those who may require an extended ablation strategy. Purpose We aimed to evaluate whether the analysis of the electrical activity recorded within the PV may provide the key to discriminate passive PV from PV playing an active role. Methods Three-subgroups of consecutive pts who underwent first-time PVI for PsAF were considered based on the mechanism of AF: 1) definitely related to PV, based on procedural AF termination during PVI (the "PV-term" group) 2) likely PV-dependent, based on the absence of AF recurrence > 18 months off-antiarrhythmic drugs after PVI only (the "PV-dep" group) 3) definitely/likely independent of the PV, based on PsAF recurrence despite isolated PV or at least one redo consisting of PV reisolation only, respectively ("PV-indep"). PV activity during AF was assessed using Lasso recordings before ablation. An automatic detection algorithm was developed to extract atrial activations from intra-cardiac electrograms in order to perform further analyses. Epochs of 60-sec were used. Different methods based on the atrial cycle length variability were assessed to best characterize the potential markers of PV activity such as the occurrence of intermittent bursts of rapid activity. Each PV and the left atrial appendage (LAA) were evaluated. Results A total of 40 pts were included (mean age: 60.0 ± 9.9 years, mean PsAF duration: 4.4 ± 4.3 years) (PV-term group (n=10), PV-dep group (n=17), PV-indep group (n= 13)). The dynamics of PV activation intervals was assessed using the envelope of the PV intervals time series (Figure 1A shows representative examples of each group). Among all features evaluated, the best feature that could discriminate similarly both the PV-term and the PV-dep group from the PV-indep group, was the standard deviation of the inferior envelope relative to the median CL of the vein (p <0.001 and p = 0.005, respectively) (Figure 1B). Cut-off selected by ROC analysis provided a 100% sensitivity and 85% specificity to discriminate the PV-term from the PV-indep group (AUC 0.931) and a 88% sensitivity and 77% specificity to discriminate the PV-dep from the PV-indep group (AUC 0.805). We compared this feature with the Fastest PV run (mean CL of the fastest 10-beat run) as recently described (p = 0.04, AUC = 0.762 and p = 0.03, AUC = 0.738, respectively) (Figure 2). Conclusions The assessment of the CL variations recorded within the PV before PsAF ablation may provide the key to discriminate pts who may be cured by PVI only.

Selection of statistical technique for imputation of single site-univariate and multisite-multivariate methods for particulate pollutants time series data with long gaps and high missing percentage.

Monitoring air contaminants has become essential to exposure science, toxicology, and public health research. However, missing values are common while monitoring air contaminants, especially in resource-constrained settings such as power cuts, calibration, and sensor failure. In contaminants monitoring, evaluating existing imputation techniques for dealing with recurrent periods of missing and unobserved data are limited. The proposed study aims to perform a statistical evaluation of six univariate and four multivariate time series imputation methods. The univariate methods are based on inter-time correlation characteristics, and the multivariate approach considers muti-site to impute missing data. The present study retrieved data from 38 ground-based monitoring stations for particulate pollutants in Delhi for 4 years. For univariate methods, missing values were simulated under 0-20% (5%, 10%, 15%, and 20%), and high 40%, 60%, and 80% missing levels having long gaps. Before evaluating multivariate methods, input data underwent pre-processing steps: selecting the target station to be imputed, choosing covariates based on the spatial correlation between multiple sites, and framing a combination of target and neighbouring stations (covariates) under 20%, 40%, 60%, and 80%. Next, the particulate pollutants data of 1480 days is provided as input to four multivariate techniques. Finally, the performance of each algorithm was evaluated using error metrics. The results show that the long interval time series data and spatial correlation of multiple stations significantly improved outcomes for univariate and multivariate time series methods. The univariate Kalman_arima performs well for long-missing gaps and all missing levels (except for 60-80%), yielding low error and high R2 and d values. In contrast, multivariate MIPCA performed better than Kalman-arima for all target stations with the highest missing percentage.

Visibility graph approach to extreme event series

An extreme event may lead to serious disaster to a complex system. In an extreme event series there exist generally non-trivial patterns covering different time scales. Investigations on extreme events are currently based upon statistics, where the patterns are merged into averages. In this paper from extreme event series we constructed extreme value series and extreme interval series. And the visibility graph is then adopted to display the patterns formed by the increases/decreases of extreme value or interval faster/slower than the linear ones. For the fractional Brownian motions, the properties for the constructed networks are the persistence, threshold, and event-type-independent, e.g., the degree distributions decay exponentially with almost identical speeds, the nodes cluster into modular structures with large and similar modularity degrees, and each specific network has a perfect hierarchical structure. For the volatilities of four stock markets (NSDQ, SZI, FTSE100, and HSI), the properties for the former three’s networks are threshold- and market-independent. Comparing with the factional Brownian motions, their degree distributions decay exponentially but with slower speeds, their modularity behaviors are significant but with smaller modularity degrees. The fourth market behaves similar qualitatively but different quantitatively with the three markets. Interestingly, all the transition frequency networks share an identical backbone composed of nine edges and the linked graphlets. The universal behaviors give us a framework to describe extreme events from the viewpoint of network.

The protective effect of inhaled corticosteroid on lung inflammation and breathing pattern complexity in a rat model of asthma

Asthma is a heterogeneous disease in which the complexity of the breathing pattern reduces as the severity of the disease increases. Since the pathophysiological basis of reduced breathing pattern complexity in asthma is unclear, in this study, we investigated the effect of reducing inflammation using an inhaled corticosteroid (fluticasone propionate) on the breathing pattern of a rat model of asthma. Detrended fluctuation analysis, sample entropy, and cross-sample entropy analysis of both inter-breath interval and respiratory volume time series showed that early treatment with inhaled corticosteroids not only diminishes lung inflammation and airway hyper-responsiveness, but also has a protective effect against the reduction of breathing pattern complexity due to asthma. However, late treatment had a partial effect on asthma-induced respiratory pattern changes. Since inflammation is a key factor in shifting breathing dynamics away from normal fluctuations, these findings further emphasize the importance of early treatment of asthma with corticosteroids.

Fingerprinting walking using wrist-worn accelerometers

BackgroundIdentifying an individual from accelerometry data collected during walking without reliance on step-cycle detection has not been achieved with high accuracy. Research questionWe propose an open-source reproducible method to: (1) create a unique, person-specific “walking fingerprint” from a sample of un-landmarked high-resolution data collected by a wrist-worn accelerometer; and (2) predict who an individual is from their walking fingerprint. MethodsAccelerometry data were collected during walking from 32 individuals (23–52 y.o., 19 females) for at least 380 s each. For this study's purpose, data are not landmarked, nor synchronized. Individual walking fingerprints were created by: (1) partitioning the accelerometer time series in adjacent, non-overlapping one-second intervals; (2) transforming all one-second interval data for a given individual into a three-dimensional (3D) image obtained by plotting each one-second interval time series by the lagged time series for a series of lags; (3) partitioning these resulting participant-specific 3D images into a grid of cells; and (4) identifying the combinations of cells (areas in the 3D image) that best predict the individual. For every participant, the first 200 s of data were used as training and the last 180 s as testing. This approach does not use segmentation methods for individual strides, which reduces dependence on complementary algorithms and increases its generalizability. ResultsThe method correctly identified 100 % of the participants in the test data and highlighted unique features of walking that characterize the individuals. SignificancePredicting the identity of an individual from their walking pattern has immediate implications that can complement or replace those of actual fingerprinting, voice, and image recognition. Furthermore, as walking may change with age or disease burden, individual walking fingerprints may be used as biomarkers of change in health status with potential clinical and epidemiologic implications.