scholarly journals Detection of Structural Changes in Tachogram Series for the Diagnosis of Atrial Fibrillation Events

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Francesca Ieva ◽  
Anna Maria Paganoni ◽  
Paolo Zanini
Keyword(s):  
Atrial Fibrillation ◽  
Time Series ◽  
Simulation Study ◽  
Chronic Condition ◽  
Structural Changes ◽  
Real Data ◽  
Chronic Atrial Fibrillation ◽  
Single Event ◽  
Arima Models ◽  
Risk Of Death

Atrial Fibrillation (AF) is the most common cardiac arrhythmia. It naturally tends to become a chronic condition, and chronic Atrial Fibrillation leads to an increase in the risk of death. The study of the electrocardiographic signal, and in particular of the tachogram series, is a usual and effective way to investigate the presence of Atrial Fibrillation and to detect when a single event starts and ends. This work presents a new statistical method to deal with the identification of Atrial Fibrillation events, based on the order identification of the ARIMA models used for describing the RR time series that characterize the different phases of AF (pre-, during, and post-AF). A simulation study is carried out in order to assess the performance of the proposed method. Moreover, an application to real data concerning patients affected by Atrial Fibrillation is presented and discussed. Since the proposed method looks at structural changes of ARIMA models fitted on the RR time series for the AF event with respect to the pre- and post-AF phases, it is able to identify starting and ending points of an AF event even when AF follows or comes before irregular heartbeat time slots.

scholarly journals Trends, structural changes, and assessment of time series models for forecasting hospital discharge due to death at a Mexican tertiary care hospital

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248277
Author(s):  
Edel Rafael Rodea-Montero ◽  
Rodolfo Guardado-Mendoza ◽  
Brenda Jesús Rodríguez-Alcántar ◽  
Jesús Rubén Rodríguez-Nuñez ◽  
Carlos Alberto Núñez-Colín ◽  
...  
Keyword(s):  
Time Series ◽  
Structural Change ◽  
Structural Changes ◽  
Tertiary Care Hospital ◽  
Tertiary Care ◽  
Training Dataset ◽  
Care Hospital ◽  
Arima Models ◽  
Hospital Discharges ◽  
Total Percentage

Background Data on hospital discharges can be used as a valuable instrument for hospital planning and management. The quantification of deaths can be considered a measure of the effectiveness of hospital intervention, and a high percentage of hospital discharges due to death can be associated with deficiencies in the quality of hospital care. Objective To determine the overall percentage of hospital discharges due to death in a Mexican tertiary care hospital from its opening, to describe the characteristics of the time series generated from the monthly percentage of hospital discharges due to death and to make and evaluate predictions. Methods This was a retrospective study involving the medical records of 81,083 patients who were discharged from a tertiary care hospital from April 2007 to December 2019 (first 153 months of operation). The records of the first 129 months (April 2007 to December 2017) were used for the analysis and construction of the models (training dataset). In addition, the records of the last 24 months (January 2018 to December 2019) were used to evaluate the predictions made (test dataset). Structural change was identified (Chow test), ARIMA models were adjusted, predictions were estimated with and without considering the structural change, and predictions were evaluated using error indices (MAE, RMSE, MAPE, and MASE). Results The total percentage of discharges due to death was 3.41%. A structural change was observed in the time series (March 2009, p>0.001), and ARIMA(0,0,0)(1,1,2)12 with drift models were adjusted with and without consideration of the structural change. The error metrics favored the model that did not consider the structural change (MAE = 0.63, RMSE = 0.81, MAPE = 25.89%, and MASE = 0.65). Conclusion Our study suggests that the ARIMA models are an adequate tool for future monitoring of the monthly percentage of hospital discharges due to death, allowing us to detect observations that depart from the described trend and identify future structural changes.

scholarly journals Robust Lag Weighted Lasso for Time Series Model

2021 ◽  
Vol 19 (1) ◽  
pp. 2-15
Author(s):  
Tahir R. Dikheel ◽  
Alaa Q. Yaseen
Keyword(s):  
Time Series ◽  
Simulation Study ◽  
Real Data ◽  
Time Series Model ◽  
The Other ◽  
The Real ◽  
Lag Effects ◽  
True Model

The lag-weighted lasso was introduced to deal with lag effects when identifying the true model in time series. This method depends on weights to reflect both the coefficient size and the lag effects. However, the lag weighted lasso is not robust. To overcome this problem, we propose robust lag weighted lasso methods. Both the simulation study and the real data example show that the proposed methods outperform the other existing methods.

Structural Changes of Atrial Myocardium During Chronic Atrial Fibrillation

2000 ◽  
Vol 9 (1) ◽  
pp. 17-28 ◽  
Author(s):  
Victor L.J.L. Thijssen ◽  
Jannie Ausma ◽  
Guo Shu Liu ◽  
Maurits A. Allessie ◽  
Guillaume J.J.M. van Eys ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Structural Changes ◽  
Chronic Atrial Fibrillation ◽  
Atrial Myocardium

USING PERMUTATIONS TO FIND STRUCTURAL CHANGES IN TIME SERIES

2011 ◽  
Vol 10 (01) ◽  
pp. 13-30 ◽  
Author(s):  
J. S. CÁNOVAS ◽  
A. GUILLAMÓN ◽  
M. C. RUIZ
Keyword(s):  
Time Series ◽  
Structural Changes ◽  
Real Data ◽  
Data Series ◽  
Embedding Dimension

The number of permutations appearing in data series is used for detecting changes in the structure of such series. We show the influence of the permutations length (embedding dimension) in order to get good results. We use permutations to analyze real data from medical and biological origins. Some problems that appear in applying these techniques are pointed out.

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