scholarly journals A Robust Detection Method of Atrial Fibrillation

Author(s):  
Jing Hu ◽  
Wei Zhao ◽  
Yanwu Xu ◽  
Jia Dongya ◽  
Cong Yan ◽  
...  
Author(s):  
Zouhair Haddi ◽  
Jean-Fran�ois Pons ◽  
St�phane Delliaux ◽  
Bouchra Ananou ◽  
Jean-Claude Deharo ◽  
...  

1990 ◽  
Vol 112 (2) ◽  
pp. 276-282 ◽  
Author(s):  
S. Tanaka ◽  
P. C. Mu¨ller

The detection of an abrupt change in the parameters of a linear discrete dynamical system is considered in the framework of the easily implemented generalized-likelihood-ratio (GLR) method. This paper proposes a robust detection method based on a pattern recognition of the maximum GLR provided by the conventional step-hypothesized GLR method. A numerical example demonstrates that the proposed method is highly superior to the conventional step-hypothesized GLR method and to the Chi-squared test in both detection rate and detection speed.


IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 110012-110022 ◽  
Author(s):  
Md Saiful Islam ◽  
Mohamed Maher Ben Ismail ◽  
Ouiem Bchir ◽  
Mohammed Zakariah ◽  
Yousef Ajami Alotaibi

2019 ◽  
Vol 245 (1) ◽  
pp. 42-53 ◽  
Author(s):  
Nan-Nan Shen ◽  
Chi Zhang ◽  
Zheng Li ◽  
Ling-Cong Kong ◽  
Xin-Hua Wang ◽  
...  

Association between microRNA (miRNA) expression signatures and atrial fibrillation has been evaluated with inconsistent findings in different studies. This study aims to identify miRNAs that actually play vital role in pathophysiological process of atrial fibrillation and explore miRNA-targeted genes and the involved pathways. Relevant studies were retrieved from the electronic databases of Embase, Medline, and Cochrane Library to determine the miRNA expression profiles between atrial fibrillation subjects and non-atrial fibrillation controls. Robustness of results was assessed using sensitivity analysis. Subgroup analyses were performed based on species, miRNA detection method, sample source, and ethnicity. Quality assessment of studies was independently conducted according to QUADAS-2. Bioinformatics analysis was applied to explore the potential genes and pathways associated with atrial fibrillation, which were targeted by differentially expressed miRNAs. Form of pooled results was shown as log10 odds ratios (logORs) with 95% confidence intervals (CI), and random-effects model was used. In total, 40 articles involving 283 differentially expressed miRNAs were reported. And 51 significantly dysregulated miRNAs were identified in consistent direction, with 22 upregulated and 29 downregulated. Among above-mentioned miRNAs, miR-223-3p (logOR 6.473; P < 0.001) was the most upregulated, while miR-1-5p (logOR 7.290; P < 0.001) was the most downregulated. Subgroup analysis confirmed 53 significantly dysregulated miRNAs (21 upregulated and 32 downregulated) in cardiac tissue, with miRNA-1-5p and miRNA-223-3p being the most upregulated and downregulated miRNAs, respectively. Additionally, miR-328 and miR-1-5p were highly blood-specific, and miR-133 was animal-specific. In the detection method sub-groups, miRNA-29b and miRNA-223-3p were differentially expressed consistently. Four miRNAs, including miRNA-223-3p, miRNA-21, miRNA-328, and miRNA-1-5p, were consistently dysregulated in both Asian and non-Asian. Results of sensitivity analysis showed that 47 out of 51 (92.16%) miRNAs were dysregulated consistently. Totally, 51 consistently dysregulated miRNAs associated with atrial fibrillation were confirmed in this study. Five important miRNAs, including miR-29b, miR-328, miR-1-5p, miR-21, and miR-223-3p may act as potential biomarkers for atrial fibrillation. Impact statement Atrial fibrillation (AF) is considered as the most common arrhythmia, and it subsequently causes serious complications including thrombosis and heart failure that increase the social burden. The definite mechanisms underlying AF pathogenesis remain complicated and unclear. Many studies attempted to discover the transcriptomic changes using microarray technologies, and the present studies for this hot topic have assessed individual miRNAs profiles for AF. However, results of different articles are controversial and not each reported miRNA is actually associated with the pathogenesis of AF. The present systematic review and meta-analysis identified that 51 consistently dysregulated miRNAs were associated with AF. Of these miRNAs, five miRNAs (miRNA-1-5p, miRNA-328, miRNA-29b, miRNA-21, and miRNA-223-3p) may act as novel biomarkers for AF. The findings could offer a better description of the biological characteristics of miRNAs, meanwhile might serve as new target for the intervention and monitoring AF in future studies.


2019 ◽  
Vol 8 (8) ◽  
pp. 363-368
Author(s):  
Congshuang Xie ◽  
Junjie Li ◽  
Qin Chen ◽  
Zihao Zhao ◽  
Chunyi Song ◽  
...  

2020 ◽  
Author(s):  
Yusong Hu ◽  
Yantao Zhao ◽  
Jihong Liu ◽  
Jin Pang ◽  
Chen Zhang ◽  
...  

Abstract Background: Atrial fibrillation is a type of persistent arrhythmia that can lead to serious complications. Therefore, accurate and quick detection of atrial fibrillation by surface electrocardiogram has great importance on further treatment. The practical electrocardiogram signals contain various interferences in different frequencies, such as myoelectricity interference, power interference and so on. Detection speed and accuracy largely depend on the atrial fibrillation signal features extracted by the algorithm. But some of the discovered atrial fibrillation features are not well distinguishable, resulting in poor classification effect. Methods: This paper proposed a high distinguishable frequency feature - the frequency corresponding to the maximum amplitude in the frequency spectrum. We used the R-R interval detection method optimized with the mathematical morphology method and combined with the wavelet transform method for analysis. According to the two features - the maximum amplitude in the frequency spectrum and R-R interval irregular, we could recognize atrial fibrillation signals in electrocardiogram signals by decision tree classification algorithm. Results: The data used in the experiment come from the MIT-BIH database, which is publicly accessible via the web and with ethical approval and consent. Based on the input of time-domain and frequency-domain features, we classified sinus rhythm signals and AF signals using the decision tree generated by classification and regression tree (CART) algorithm. From the confusion matrix, we got the accuracy was 98.9%, sensitivity was 97.93% and specificity was 99.63%. Conclusions: The experimental results can prove the validity of the maximum amplitude in the frequency spectrum and the practicability and accuracy of the detection method, which applied this frequency-domain feature. Through the detection method, we obtained good accuracy of classifying sinus rhythm signals and atrial fibrillation signals. And the sensitivity and specificity of our method were pretty good by comparison with other studies.


2020 ◽  
Author(s):  
Yusong Hu ◽  
Yantao Zhao ◽  
Jihong Liu ◽  
Jin Pang ◽  
Chen Zhang ◽  
...  

Abstract Background: Atrial fibrillation is a type of persistent arrhythmia that can lead to serious complications. Therefore, accurate and quick detection of atrial fibrillation by surface electrocardiogram has great importance on further treatment. The practical electrocardiogram signals contain various interferences in different frequencies, such as myoelectricity interference, power interference and so on. Detection speed and accuracy largely depend on the atrial fibrillation signal features extracted by the algorithm. But some of the discovered atrial fibrillation features are not well distinguishable, resulting in poor classification effect. Methods: This paper proposed a high distinguishable frequency feature - the frequency corresponding to the maximum amplitude in the frequency spectrum. We used the R-R interval detection method optimized with the mathematical morphology method and combined with the wavelet transform method for analysis. According to the two features - the maximum amplitude in the frequency spectrum and R-R interval irregular, we could recognize atrial fibrillation signals in electrocardiogram signals by decision tree classification algorithm. Results: The data used in the experiment come from the MIT-BIH database, which is publicly accessible via the web and with ethical approval and consent. Based on the input of time-domain and frequency-domain features, we classified sinus rhythm signals and AF signals using the decision tree generated by classification and regression tree (CART) algorithm. From the confusion matrix, we got the accuracy was 98.9%, sensitivity was 97.93% and specificity was 99.63%. Conclusions: The experimental results can prove the validity of the maximum amplitude in the frequency spectrum and the practicability and accuracy of the detection method, which applied this frequency-domain feature. Through the detection method, we obtained good accuracy of classifying sinus rhythm signals and atrial fibrillation signals. And the sensitivity and specificity of our method were pretty good by comparison with other studies.


Genes ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1199
Author(s):  
Faisal Ramzan ◽  
Mehmet Gültas ◽  
Hendrik Bertram ◽  
David Cavero ◽  
Armin Otto Schmitt

The authors would like to make a correction to the published paper [...]


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