Electrocardiogram (ECG) based biometric matching suffers from high misclassification error with lower sampling frequency data. This situation may lead to an unreliable and vulnerable identity authentication process in high security applications. In this paper, quality enhancement techniques for ECG data with low sampling frequency has been proposed for person identification based on piecewise cubic Hermite interpolation (PCHIP) and piecewise cubic spline interpolation (SPLINE). A total of 70 ECG recordings from 4 different public ECG databases with 2 different sampling frequencies were applied for development and performance comparison purposes. An analytical method was used for feature extraction. The ECG recordings were segmented into two parts: the enrolment and recognition datasets. Three biometric matching methods, namely, Cross Correlation (CC), Percent Root-Mean-Square Deviation (PRD) and Wavelet Distance Measurement (WDM) were used for performance evaluation before and after applying interpolation techniques. Results of the experiments suggest that biometric matching with interpolated ECG data on average achieved higher matching percentage value of up to 4% for CC, 3% for PRD and 94% for WDM. These results are compared with the existing method when using ECG recordings with lower sampling frequency. Moreover, increasing the sample size from 56 to 70 subjects improves the results of the experiment by 4% for CC, 14.6% for PRD and 0.3% for WDM. Furthermore, higher classification accuracy of up to 99.1% for PCHIP and 99.2% for SPLINE with interpolated ECG data as compared of up to 97.2% without interpolation ECG data verifies the study claim that applying interpolation techniques enhances the quality of the ECG data.
We present here a toolbox for the real-time motion capture of biological movements that runs in the cross-platform MATLAB environment (The MathWorks, Inc., Natick, MA). It provides instantaneous processing of the 3-D movement coordinates of up to 20 markers at a single instant. Available functions include (1) the setting of reference positions, areas, and trajectories of interest; (2) recording of the 3-D coordinates for each marker over the trial duration; and (3) the detection of events to use as triggers for external reinforcers (e.g., lights, sounds, or odors). Through fast online communication between the hardware controller and RTMocap, automatic trial selection is possible by means of either a preset or an adaptive criterion. Rapid preprocessing of signals is also provided, which includes artifact rejection, filtering, spline interpolation, and averaging. A key example is detailed, and three typical variations are developed (1) to provide a clear understanding of the importance of real-time control for 3-D motion in cognitive sciences and (2) to present users with simple lines of code that can be used as starting points for customizing experiments using the simple MATLAB syntax. RTMocap is freely available ( http://sites.google.com/site/RTMocap/ ) under the GNU public license for noncommercial use and open-source development, together with sample data and extensive documentation.
Plasma homocysteine (Hcy) levels may be associated with all-cause mortality risk. However, the results of this association are conflicting and the dose-response relationship between them has not been clearly defined. In this meta-analysis, we conducted a systematic literature search of the PubMed, Embase, Web of Science and Cochrane Library for the relevant articles dated up to February 2017. Pooled relative risks (RRs) and corresponding 95% confidence intervals (CIs) were calculated to evaluate the estimates, and the dose-response relationship was estimated using a restricted cubic spline model. Eleven prospective studies (4,110 deaths among 27,737 individuals) were included. The summary RR of all-cause mortality for the highest Hcy category vs. the lowest Hcy category was 1.80 (95% CI: 1.51, 2.14) with the random effects model. In dose-response meta-analysis, Hcy levels were significantly associated with all-cause mortality risk in a linear fashion (p nonlinearity = 0.255), and the risk of all-cause mortality increased by 33.6% for each 5 µmol/L increase in Hcy levels (RR = 1.336, 95% CI: 1.254-1.422, p < 0.001). Findings from this dose-response meta-analysis suggest that Hcy levels are linearly and positively associated with risk of all-cause mortality.
The antihypertensive effect of magnesium (Mg) supplementation remains controversial. We aimed to quantify the effect of oral Mg supplementation on blood pressure (BP) by synthesizing available evidence from randomized, double-blind, placebo-controlled trials. We searched trials of Mg supplementation on normotensive and hypertensive adults published up to February 1, 2016 from MEDLINE and EMBASE databases; 34 trials involving 2028 participants were eligible for this meta-analysis. Weighted mean differences of changes in BP and serum Mg were calculated by random-effects meta-analysis. Mg supplementation at a median dose of 368 mg/d for a median duration of 3 months significantly reduced systolic BP by 2.00 mm Hg (95% confidence interval, 0.43-3.58) and diastolic BP by 1.78 mm Hg (95% confidence interval, 0.73-2.82); these reductions were accompanied by 0.05 mmol/L (95% confidence interval, 0.03, 0.07) elevation of serum Mg compared with placebo. Using a restricted cubic spline curve, we found that Mg supplementation with a dose of 300 mg/d or duration of 1 month is sufficient to elevate serum Mg and reduce BP; and serum Mg was negatively associated with diastolic BP but not systolic BP (all P<0.05). In the stratified analyses, a greater reduction in BP tended to be found in trials with high quality or low dropout rate (all P values for interaction <0.05). However, residual heterogeneity may still exist after considering these possible factors. Our findings indicate a causal effect of Mg supplementation on lowering BPs in adults. Further well-designed trials are warranted to validate the BP-lowering efficacy of optimal Mg treatment.
Background: Infant body mass index (BMI) peak characteristics and early childhood BMI are emerging markers of future obesity and cardiometabolic disease risk, but little is known about their maternal nutritional determinants.Objective: We investigated the associations of maternal macronutrient intake with infant BMI peak characteristics and childhood BMI in the Growing Up in Singapore Towards healthy Outcomes study.Design: With the use of infant BMI data from birth to age 18 mo, infant BMI peak characteristics [age (in months) and magnitude (BMIpeak; in kg/m(2)) at peak and prepeak velocities] were derived from subject-specific BMI curves that were fitted with the use of mixed-effects model with a natural cubic spline function. Associations of maternal macronutrient intake (assessed by using a 24-h recall during late gestation) with infant BMI peak characteristics (n = 910) and BMI z scores at ages 2, 3, and 4 y were examined with the use of multivariable linear regression.Results: Mean absolute maternal macronutrient intakes (percentages of energy) were 72 g protein (15.6%), 69 g fat (32.6%), and 238 g carbohydrate (51.8%). A 25-g (∼100-kcal) increase in maternal carbohydrate intake was associated with a 0.01/mo (95% CI: 0.0003, 0.01/mo) higher prepeak velocity and a 0.04 (95% CI: 0.01, 0.08) higher BMIpeak These associations were mainly driven by sugar intake, whereby a 25-g increment of maternal sugar intake was associated with a 0.02/mo (95% CI: 0.01, 0.03/mo) higher infant prepeak velocity and a 0.07 (95% CI: 0.01, 0.13) higher BMIpeak Higher maternal carbohydrate and sugar intakes were associated with a higher offspring BMI z score at ages 2-4 y. Maternal protein and fat intakes were not consistently associated with the studied outcomes.Conclusion: Higher maternal carbohydrate and sugar intakes are associated with unfavorable infancy BMI peak characteristics and higher early childhood BMI. This trial was registered at clinicaltrials.gov as NCT01174875.
- American journal of respiratory and critical care medicine
- Published about 7 years ago
Rationale: Processes of care are potential determinants of outcomes in patients with severe sepsis. Whether hospitals with more experience caring for patients with severe sepsis also have improved outcomes is unclear. Objectives: To determine associations between hospital severe sepsis caseload and outcomes. Methods: We analyzed data from US academic hospitals provided through University HealthSystem Consortium. We used University HealthSystem Consortium’s sepsis mortality model (c-statistic 0.826) for risk-adjustment. Validated International Classification of Disease, 9th Edition, Clinical Modification algorithms were used to identify hospital severe sepsis case volume. Associations between risk-adjusted severe sepsis case volume and mortality, length of stay, and costs were analyzed using spline regression and analysis of covariance. Results: We identified 56,997 patients with severe sepsis admitted to 124 US academic hospitals during 2011. Hospitals admitted 460±216 patients with severe sepsis, with median length of stay 12.5 days (IQR 11.1-14.2), median direct costs $26,304 (IQR $21,900-32,090), and average hospital mortality 25.6±5.3%. Higher severe sepsis case volume was associated with lower unadjusted severe sepsis mortality (R2 =0.10, p=0.01) and risk-adjusted severe sepsis mortality (R2=0.21, p<0.001). After further adjustment for geographic region, number of beds, and long-term acute care referrals, hospitals in the highest severe sepsis case volume quartile had an absolute 7% (95% CI 2.4-11.6%) lower hospital mortality than hospitals in the lowest quartile. We did not identify associations between case volume and resource utilization. Conclusions and Relevance: Academic hospitals with higher severe sepsis case volume have lower severe sepsis hospital mortality without higher costs.
Statistical models for assessing risk of type 2 diabetes are usually additive with linear terms that use non-nationally representative data. The objective of this study was to use nationally representative data on diabetes risk factors and spline regression models to determine the ability of models with nonlinear and interaction terms to assess the risk of type 2 diabetes.
Background: During the past decade, an increasing number of prospective studies have focused on the association between vitamin D and cardiovascular disease (CVD). However, the evidence on the relation between serum 25-hydroxyvitamin D [25(OH)D] and the risk of overt CVD is inconclusive.Objective: We performed a dose-response meta-analysis to summarize and prospectively quantify the RR of low serum 25(OH)D concentration and total CVD (events and mortality).Design: We identified relevant studies by searching PubMed and EMBASE up to December 2015 and by hand-searching reference lists. Prospective studies based on the general population and reported RRs and 95% CIs were included. A random-effects model was used to calculate the pooled RRs. Nonlinear association was assessed by using restricted cubic spline analyses.Results: A total of 34 publications with 180,667 participants were eligible for the meta-analysis. We included 32 publications (27 independent studies) for total CVD events and 17 publications (17 independent studies) for CVD mortality. We observed an inverse association between serum 25(OH)D and total CVD events and CVD mortality, and the pooled RRs per 10-ng/mL increment were 0.90 (95% CI: 0.86, 0.94) for total CVD events and 0.88 (95% CI: 0.80, 0.96) for CVD mortality. A nonlinear association was detected for total CVD events (P-nonlinear < 0.001) and CVD mortality (P-nonlinear = 0.022).Conclusion: Serum 25(OH)D concentration was inversely associated with total CVD events and CVD mortality from the observed studies.
Based on the geo-statistical theory and ArcGIS geo-statistical module, datas of 30 groundwater level observation wells were used to estimate the decline of groundwater level in Beijing piedmont. Seven different interpolation methods (inverse distance weighted interpolation, global polynomial interpolation, local polynomial interpolation, tension spline interpolation, ordinary Kriging interpolation, simple Kriging interpolation and universal Kriging interpolation) were used for interpolating groundwater level between 2001 and 2013. Cross-validation, absolute error and coefficient of determination (R(2)) was applied to evaluate the accuracy of different methods. The result shows that simple Kriging method gave the best fit. The analysis of spatial and temporal variability suggest that the nugget effects from 2001 to 2013 were increasing, which means the spatial correlation weakened gradually under the influence of human activities. The spatial variability in the middle areas of the alluvial-proluvial fan is relatively higher than area in top and bottom. Since the changes of the land use, groundwater level also has a temporal variation, the average decline rate of groundwater level between 2007 and 2013 increases compared with 2001-2006. Urban development and population growth cause over-exploitation of residential and industrial areas. The decline rate of the groundwater level in residential, industrial and river areas is relatively high, while the decreasing of farmland area and development of water-saving irrigation reduce the quantity of water using by agriculture and decline rate of groundwater level in agricultural area is not significant.
We introduce current home Internet of Things (IoT) technology and present research on its various forms and applications in real life. In addition, we describe IoT marketing strategies as well as specific modeling techniques for improving air quality, a key home IoT service. To this end, we summarize the latest research on sensor-based home IoT, studies on indoor air quality, and technical studies on random data generation. In addition, we develop an air quality improvement model that can be readily applied to the market by acquiring initial analytical data and building infrastructures using spectrum/density analysis and the natural cubic spline method. Accordingly, we generate related data based on user behavioral values. We integrate the logic into the existing home IoT system to enable users to easily access the system through the Web or mobile applications. We expect that the present introduction of a practical marketing application method will contribute to enhancing the expansion of the home IoT market.