Modified algorithms for signal nonlinear trend detection

There is a problem of nonlinear (abrupt) signal trend detection in many digital signals processing practical applications. In particular, in the field of biomedical signals processing, the actual task is the elimination of abrupt signal baseline distortions caused by the patient's movements. For processing such signals containing edges and other discontinues, linear filtering based on discrete Fourier or cosine transforms leads to significant smoothing of a signal. Median type algorithms related to nonlinear stable (robust) filters are successfully applied for filtering such signals, in particular, high efficiency is provided by median hybrid filters with finite impulse response (FIR). The article considers simple algorithms of the class of FIR-median hybrid filters used for signal nonlinear trend detection. It is proposed to modify these algorithms by replacing the operation of finding the median of the data in the sliding filter window with the calculation of their myriad, as well as adding weights (number of duplications) to certain window elements. Statistical estimates of filter efficiency according to the mean square error (MSE) criterion for test signals like “step” and “ramp” edges, and triangular peak and parabola have been obtained. The high efficiency of the investigated nonlinear filters for the listed test signals types and the improvements achieved as a result of the proposed filter modifications are shown based on the analysis of the filter output signals and statistical estimates of their quality. Some examples of processing biomedical signals of electroencephalograms which illustrate good quality of noise suppression and signal abrupt changes preservation, and motion artifacts removal without large signal distortions are given.

Reconstruction of distorted structures in fault shadow zone based on Full Connected Network

Lateral changes in velocity about faults can give rise to fault shadow (FS) zones on time-migrated data volumes, which can result in structural interpretation artifacts in the fault trap reservoir. To address this issue we proposed a new reconstruction method of FS distortion structures based on a deep learning fully connected network (FCN). We use the three dimensional (3D) stratigraphic dip attributes to quantitatively delineate the extend of the FS zone. Then, we train an model to construct a nonlinear trend surface based on the structures of the stratigraphic reflectors that fall outside the shadow zone. Finally, we use this nonlinear trend surface to compensate the distorted structure within the FS zone. We calibrate our method using synthetic data and show that the method can accurately recover the structural data within the FS distortion zone. We then test the effectiveness of our workflow by applying it to recover real FS distortation sturctures in the Pearl River Mouth Basin of the South China Sea. The results confirm that our method significantly reduces the drilling depth error in the FS zone. Compared with the traditional polynomial fitting method, the multi-layer, multi-parameter and flexible nonlinear activation function of FCN is more capable of reconstructing nonlinear geological structures in the FS zone. We find the FCN-based geological reconstruction method to be both efficient and effective for exploring the potential structures in the FS zone and thereby in avoiding the risks of structural failure.

A time series analysis of permanent GNSS stations in the northwest network of Iran

The aim of this study is to estimate reliable velocities along with their realistic uncertainties based on a robust time series analysis including analysis of deterministic and stochastic (noise) models. In the deterministic model analysis part, we use a complete station motion model comprised of jump effects, linear and nonlinear trend, periodic components, and post-seismic deformation model. This part also consists of jump detection, outlier detection, and statistical significance of jumps. We perform the deterministic model analysis in an iterative process to elevate its efficiency. In the noise analysis part, first, we remove the spatial correlation of observations using the weighted stacking method based on the common mode error (CME) parameter. Next, a combination of white and flicker noises is used to determine the stochastic model. This time series analysis is applied for 11-year time series of 25 permanent GNSS stations from 2006 to 2016 in the northwest network of Iran. We reveal that there is a nonlinear trend in some stations, although most stations have a linear trend. In addition, we found that a combination of logarithmic and exponential functions is the most appropriate post-seismic deformation model in our study region. The result of the noise analysis shows that the spatial filtering reduces the norm of post-fit residual vector by 19.34%, 17.51%, and 12.44% on average for the east, north, and up components, respectively. Furthermore, the uncertainties obtained from the combination of white and flicker noises at the east, north, and up components are 5.0, 4.8, and 4.4 times greater than those of the white noise model, respectively. The results indicate that the stations move horizontally with an average velocity of 36.0 ± 0.3 mm/yr in the azimuth of 52.66° NE which is compatible with velocities obtained from MIDAS. We obtained the vertical velocity of most stations in the range of -5 to 5 mm/yr. However, in three stations of GGSH, ORYH, and BNAB, which are in the proximity of Lake Urmia, the vertical velocities are estimated to be -80.9 mm/yr, -50.6 mm/yr, and -11.4 mm/yr, respectively. Moreover, we found that these three stations possess large periodic signal amplitudes in all three coordinate components as well as a nonlinear trend in the up component.

Trend and Variance of Continental Fresh Water Discharge over the Last Six Decades

Trend estimation of river discharge is an important but difficult task because discharge time series are nonlinear and nonstationary. Previous studies estimated the trend of discharge using a linear method, which is not applicable to nonstationary time series with a nonlinear trend. To overcome this problem, we used a recently developed wavelet-based method, ensemble empirical mode decomposition (EEMD), which can separate nonstationary variations from the long-term nonlinear trend. Applying EEMD to annual discharge data of the 925 world’s largest rivers from 1948–2004, we found that the global discharge decreased before 1978 and increased after 1978, which contrasts the nonsignificant trend as estimated by the linear method over the same period. Further analyses show that precipitation had a consistent and dominant influence on the interannual variation of discharge of all six continents and globally, but the influences of precipitation and surface air temperature on the trend of discharge varied regionally. We also found that the estimated trend using EEMD was very sensitive to the discharge data length. Our results demonstrated some useful applications of the EEMD method in studying regional or global discharge, and it should be adopted for studying all nonstationary hydrological time series.

Predictive values of ANGPTL8 on risk of all-cause mortality in diabetic patients: results from the REACTION Study

Background: ANGPTL8, an important regulator of lipid metabolism, is increased in diabetes and is associated with insulin resistance. However, the role of ANGPTL8 in the outcomes of diabetic patients remains unclear. This study aimed to investigate circulating levels of ANGPTL8 in participants with and without diabetes and its potential associations with clinical outcomes in a 5-year cohort study.Methods: Propensity-matched cohorts of subjects with and without diabetes from the Risk Evaluation of Cancers in Chinese Diabetic Individuals: A longitudinal (REACTION) study were generated on the basis of age, sex and body mass index at baseline. The primary outcome was all-cause mortality. The secondary outcomes were a composite of new-onset major adverse cardiovascular events, hospitalization for heart failure, and renal dysfunction (eGFR <60/min/1.73 m2).Results: We identified 769 matched pairs of diabetic patients and control subjects. Serum ANGPTL8 levels were elevated in patients with diabetes compared to control subjects (618.82 318.08 vs 581.20 299.54 pg/mL, p =0.03). Binary logistic regression analysis showed that elevated ANGPTL8 levels were associated with greater risk ratios (RRs) of death (RR in quartile 4 vs quartile 1, 3.54; 95% CI 1.32 – 9.50) and renal dysfunction (RR in quartile 4 vs quartile 1, 12.43; 95% CI 1.48 – 104.81) only in diabetic patients. Multivariable-adjusted restricted cubic spline analyses revealed a significant, linear relationship between ANGPTL8 and all-cause mortality in diabetic patients (p for nonlinear trend =0.99, p for linear trend =0.01) but not in control subjects (p for nonlinear trend =0.26, p for linear trend =0.80). According to ROC curve analysis, the inclusion of ANGPTL8 in QFrailty score significantly improved its predictive performance for mortality in patients with diabetes. Conclusion: Serum ANGPTL8 levels were associated with an increased risk of all-cause mortality and could be used as a potential biomarker for the prediction of death in patients with diabetes.

Predictive values of ANGPTL8 on risk of all-cause mortality in diabetic patients: from the REACTION Study

Background: ANGPTL8, an important regulator of glucose and lipid metabolism, is increased in diabetes and associated with insulin resistance. However, the role of ANGPTL8 in diabetes outcomes remains unclear. The study aimed to investigate circulating levels of ANGPTL8 in participants with and without diabetes and its potential association with death and cardiovascular and renal outcomes in a 5-year cohort study.Methods: Propensity-matched cohorts of subjects with and without diabetes from the Risk Evaluation of Cancers in Chinese Diabetic Individuals: A longitudinal (REACTION) study was generated on the basis of age, sex and body mass index at baseline. The primary outcome was death from any cause. The secondary outcome was a composite of new-onset major adverse cardiovascular events, hospitalization for heart failure and renal dysfunction (eGFR < 60/min/1.73 m2).Results: We identified 769 matched pairs of patients from the diabetes group and the control group. Serum ANGPTL8 levels were elevated in patients with diabetes compared to subjects in the control group (618.82 318.08 vs. 581.20 299.54, p = 0.03). Furthermore, increasing quartiles of ANGPTL8 were associated with increased all-cause mortality in both the control and diabetes groups (all p values < 0.05). Binary logistic regression analysis showed that elevated ANGPTL8 levels were associated with greater risk ratios (RRs) of death (RR in quartile 4 vs quartile 1, 3.47; 95% CI 1.30 – 9.29) and renal dysfunction (RR in quartile 4 vs quartile 1, 10.50; 95% CI 1.32 – 83.60) only in diabetic patients. Multivariable-adjusted restricted cubic spline analyses suggested a significant linear relationship between ANGPTL8 and all-cause mortality in diabetic patients (p for nonlinear trend = 0.99, p for linear trend = 0.01) but not in the controls (p for nonlinear trend = 0.26, p for linear trend = 0.80). According to the ROC curves, the QMortality and QFrailty scores, combined with ANGPTL8, showed better performance in predicting death, especially in diabetic patients.Conclusion: Serum ANGPTL8 levels were associated with an increased risk for all-cause mortality and renal dysfunction in subjects with diabetes. Furthermore, ANGPTL8 had good performance in predicting all-cause mortality in diabetic patients, which may contribute to the early detection of individuals with diabetes at high risk.