scholarly journals ECG Analysis for HRV Detection

2021 ◽  
Vol 9 (VI) ◽  
pp. 2283-2286
Author(s):  
Ratnadeep Gawade
Keyword(s):  
Real Time ◽  
Power Line ◽  
Ar Model ◽  
Ecg Analysis ◽  
Regressive Model ◽  
Auto Regressive ◽  
Power Line Interference ◽  
Auto Regressive Model ◽  
Line Interference

In this paper an algorithm is proposed for estimation of HRV with better accuracy and results. We are making use of Auto Regressive Model (AR Model) for the estimation. Since ECG wave is also contaminated with a lot of noise such as Power Line Interference (PLI), EMG and just some common artifacts like breathing disturbance’s, so to filter out all this noise from the wave we are using Cumulant based AR model for filtering the wave. Using IoT we will later use real time ECG waves to estimate HRV.

Validation en temps réel des données hydrométriques

2003 ◽  
Vol 30 (1) ◽  
pp. 212-225
Author(s):  
Berrada Faouzi ◽  
Khalili Malika ◽  
Bennis Saad
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Multiple Regression Equation ◽  
Variation Trend ◽  
Regressive Model ◽  
Analytical Redundancy ◽  
Optimal Resource ◽  
Auto Regressive ◽  
Optimal Resource Management ◽  
Auto Regressive Model

The managers of hydrological systems take real-time level and discharge measurements on several reservoirs and river reaches. The measurements are frequently tainted with errors that are reflected by uncertainties in decision making and by non-optimal resource management. This article aims at developing a methodology for the validation of levels in real-time. The proposed approach is based on material and analytical redundancy, and uses two models. The first one is spatial and enables linking of the measurements carried out at different stations with the help of a multiple regression equation. The second one is temporal, which enables the determination of variation trend at the different stations with the help of an auto-regressive model. These two models are incorporated into a diagnosis system for breakdowns based on a logic vote principle. Among the values that are measured and estimated by the linear regression model, the one which is the most consistent with the variation trend indicated by the auto-regressive model is selected. The Kalman filter is used to filter the measurements and identify the parameters of the models used in real-time. The proposed methodology turned out to be conclusive when applied to both measured data and synthetic hydrographs.Key words: validation, real-time, material redundancy, analytical redundancy, regressive, auto-regressive, Kalman filter.

scholarly journals Backward-Auto-Regressive Model Based Rotor-Bearing System Stability Prediction

2011 ◽  
Vol 2-3 ◽  
pp. 683-687 ◽  
Author(s):  
Jia Li Yang ◽  
Wei Min Wang ◽  
Yong Jiang Zhu ◽  
Ya Zhang
Keyword(s):  
Damping Ratio ◽  
System Stability ◽  
Ar Model ◽  
Model Order ◽  
Regressive Model ◽  
Impulse Excitation ◽  
Rotor Bearing ◽  
Auto Regressive ◽  
Auto Regressive Model ◽  
Bearing System

This paper focus on rotor-bearing system parameter identification with impulse excitation in horizon and vertical which is based on Backward -auto-regressive model. Singular value decomposition is applied to reduce the noise and the proper AR model order and de-noising threshold are selected. In this paper, the damping ratio is identified within the different rotating speed and different impulse excitation, and the error is calculated within the different noise level and different AR model order when compared with the ideal model. Though the theoretical analysis, simulation analysis and experimental research, We can indicate that the BAR model has a good performance in system identification and elimination of false modal.

scholarly journals Estimation of Excess Mortality and Years of Life Lost to COVID-19 in Norway and Sweden between March and November 2020

2021 ◽  
Vol 18 (8) ◽  
pp. 3913
Author(s):  
Martin Rypdal ◽  
Kristoffer Rypdal ◽  
Ola Løvsletten ◽  
Sigrunn Holbek Sørbye ◽  
Elinor Ytterstad ◽  
...  
Keyword(s):  
Excess Mortality ◽  
Age Groups ◽  
Years Of Life Lost ◽  
Regressive Model ◽  
Epidemic Period ◽  
All Cause Mortality ◽  
Auto Regressive ◽  
Expected Mortality ◽  
Auto Regressive Model ◽  
Excess Deaths

We estimate the weekly excess all-cause mortality in Norway and Sweden, the years of life lost (YLL) attributed to COVID-19 in Sweden, and the significance of mortality displacement. We computed the expected mortality by taking into account the declining trend and the seasonality in mortality in the two countries over the past 20 years. From the excess mortality in Sweden in 2019/20, we estimated the YLL attributed to COVID-19 using the life expectancy in different age groups. We adjusted this estimate for possible displacement using an auto-regressive model for the year-to-year variations in excess mortality. We found that excess all-cause mortality over the epidemic year, July 2019 to July 2020, was 517 (95%CI = (12, 1074)) in Norway and 4329 [3331, 5325] in Sweden. There were 255 COVID-19 related deaths reported in Norway, and 5741 in Sweden, that year. During the epidemic period of 11 March–11 November, there were 6247 reported COVID-19 deaths and 5517 (4701, 6330) excess deaths in Sweden. We estimated that the number of YLL attributed to COVID-19 in Sweden was 45,850 [13,915, 80,276] without adjusting for mortality displacement and 43,073 (12,160, 85,451) after adjusting for the displacement accounted for by the auto-regressive model. In conclusion, we find good agreement between officially recorded COVID-19 related deaths and all-cause excess deaths in both countries during the first epidemic wave and no significant mortality displacement that can explain those deaths.

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