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Atrial fibrillation detection by heart rate variability in Poincare plot.

Park J, Lee S, Jeon M - Biomed Eng Online (2009)

Bottom Line: We made a Poincare plot using the inter-beat intervals.Mean sensitivity and mean specificity were 91.4% and 92.9% respectively.We visually displayed regularity of the inter-beat intervals by way of Poincare plot.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Information and Communications, Gwangju Institute of Science and Technology, 1 Oryong-dong, Buk-gu, Gwangju, Republic of Korea. jinho@gist.ac.kr

ABSTRACT

Background: Atrial fibrillation (AFib) is one of the prominent causes of stroke, and its risk increases with age. We need to detect AFib correctly as early as possible to avoid medical disaster because it is likely to proceed into a more serious form in short time. If we can make a portable AFib monitoring system, it will be helpful to many old people because we cannot predict when a patient will have a spasm of AFib.

Methods: We analyzed heart beat variability from inter-beat intervals obtained by a wavelet-based detector. We made a Poincare plot using the inter-beat intervals. By analyzing the plot, we extracted three feature measures characterizing AFib and non-AFib: the number of clusters, mean stepping increment of inter-beat intervals, and dispersion of the points around a diagonal line in the plot. We divided distribution of the number of clusters into two and calculated mean value of the lower part by k-means clustering method. We classified data whose number of clusters is more than one and less than this mean value as non-AFib data. In the other case, we tried to discriminate AFib from non-AFib using support vector machine with the other feature measures: the mean stepping increment and dispersion of the points in the Poincare plot.

Results: We found that Poincare plot from non-AFib data showed some pattern, while the plot from AFib data showed irregularly irregular shape. In case of non-AFib data, the definite pattern in the plot manifested itself with some limited number of clusters or closely packed one cluster. In case of AFib data, the number of clusters in the plot was one or too many. We evaluated the accuracy using leave-one-out cross-validation. Mean sensitivity and mean specificity were 91.4% and 92.9% respectively.

Conclusions: Because pulse beats of ventricles are less likely to be influenced by baseline wandering and noise, we used the inter-beat intervals to diagnose AFib. We visually displayed regularity of the inter-beat intervals by way of Poincare plot. We tried to design an automated algorithm which did not require any human intervention and any specific threshold, and could be installed in a portable AFib monitoring system.

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A relation between two features, mean stepping increment of inter-beat intervals and dispersion of the points in Poincaré plot when we got one or too many number of clusters. We used support vector machine method on the data whose number of clusters is one or too many to discriminate AFib from non-AFib. The mark X means non-AFib data and the diamond mark means AFib data.
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Figure 9: A relation between two features, mean stepping increment of inter-beat intervals and dispersion of the points in Poincaré plot when we got one or too many number of clusters. We used support vector machine method on the data whose number of clusters is one or too many to discriminate AFib from non-AFib. The mark X means non-AFib data and the diamond mark means AFib data.

Mentions: We represent a plot in Figure 9 which shows a relation between two features, the mean stepping increment of inter-beat intervals and dispersion of the points in Poincaré plot in case that we had to apply support vector machine. This is when we got one or too many number of clusters. From this plot we can assume that the Figure 4(b) has relatively low dispersion and mean stepping increment; in contrast, the Figure 6(b) has high values.


Atrial fibrillation detection by heart rate variability in Poincare plot.

Park J, Lee S, Jeon M - Biomed Eng Online (2009)

A relation between two features, mean stepping increment of inter-beat intervals and dispersion of the points in Poincaré plot when we got one or too many number of clusters. We used support vector machine method on the data whose number of clusters is one or too many to discriminate AFib from non-AFib. The mark X means non-AFib data and the diamond mark means AFib data.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2803479&req=5

Figure 9: A relation between two features, mean stepping increment of inter-beat intervals and dispersion of the points in Poincaré plot when we got one or too many number of clusters. We used support vector machine method on the data whose number of clusters is one or too many to discriminate AFib from non-AFib. The mark X means non-AFib data and the diamond mark means AFib data.
Mentions: We represent a plot in Figure 9 which shows a relation between two features, the mean stepping increment of inter-beat intervals and dispersion of the points in Poincaré plot in case that we had to apply support vector machine. This is when we got one or too many number of clusters. From this plot we can assume that the Figure 4(b) has relatively low dispersion and mean stepping increment; in contrast, the Figure 6(b) has high values.

Bottom Line: We made a Poincare plot using the inter-beat intervals.Mean sensitivity and mean specificity were 91.4% and 92.9% respectively.We visually displayed regularity of the inter-beat intervals by way of Poincare plot.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Information and Communications, Gwangju Institute of Science and Technology, 1 Oryong-dong, Buk-gu, Gwangju, Republic of Korea. jinho@gist.ac.kr

ABSTRACT

Background: Atrial fibrillation (AFib) is one of the prominent causes of stroke, and its risk increases with age. We need to detect AFib correctly as early as possible to avoid medical disaster because it is likely to proceed into a more serious form in short time. If we can make a portable AFib monitoring system, it will be helpful to many old people because we cannot predict when a patient will have a spasm of AFib.

Methods: We analyzed heart beat variability from inter-beat intervals obtained by a wavelet-based detector. We made a Poincare plot using the inter-beat intervals. By analyzing the plot, we extracted three feature measures characterizing AFib and non-AFib: the number of clusters, mean stepping increment of inter-beat intervals, and dispersion of the points around a diagonal line in the plot. We divided distribution of the number of clusters into two and calculated mean value of the lower part by k-means clustering method. We classified data whose number of clusters is more than one and less than this mean value as non-AFib data. In the other case, we tried to discriminate AFib from non-AFib using support vector machine with the other feature measures: the mean stepping increment and dispersion of the points in the Poincare plot.

Results: We found that Poincare plot from non-AFib data showed some pattern, while the plot from AFib data showed irregularly irregular shape. In case of non-AFib data, the definite pattern in the plot manifested itself with some limited number of clusters or closely packed one cluster. In case of AFib data, the number of clusters in the plot was one or too many. We evaluated the accuracy using leave-one-out cross-validation. Mean sensitivity and mean specificity were 91.4% and 92.9% respectively.

Conclusions: Because pulse beats of ventricles are less likely to be influenced by baseline wandering and noise, we used the inter-beat intervals to diagnose AFib. We visually displayed regularity of the inter-beat intervals by way of Poincare plot. We tried to design an automated algorithm which did not require any human intervention and any specific threshold, and could be installed in a portable AFib monitoring system.

Show MeSH
Related in: MedlinePlus