Department of Information and Communications, Gwangju Institute of Science and Technology, 1 Oryong-dong, Buk-gu, Gwangju, Republic of Korea.
Biomed Eng Online. 2009 Dec 11;8:38. doi: 10.1186/1475-925X-8-38.
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.
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.
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.
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.
心房颤动（AFib）是中风的主要原因之一，其风险随着年龄的增长而增加。我们需要尽早正确检测出 AFib，以避免医疗灾难，因为它很可能在短时间内发展成更严重的形式。如果我们能制造出便携式 AFib 监测系统，这将对许多老年人有所帮助，因为我们无法预测患者何时会出现 AFib 痉挛。
我们通过基于小波的检测器从心跳间隔中分析心率变异性。我们使用心跳间隔制作了一个 Poincaré 图。通过分析该图，我们提取了三个特征度量来描述 AFib 和非 AFib：簇数、心跳间隔的平均步长增量和图中对角线周围点的离散度。我们将簇数的分布分为两部分，并通过 K-均值聚类方法计算下部的平均值。我们将簇数大于一且小于该平均值的数据分类为非 AFib 数据。在其他情况下，我们尝试使用支持向量机和其他特征度量（ Poincaré 图中的平均步长增量和点的离散度）来区分 AFib 和非 AFib。
我们发现非 AFib 数据的 Poincaré 图显示出一些模式，而 AFib 数据的图则显示出不规则的不规则形状。在非 AFib 数据的情况下，图中的明确模式表现为簇数有限或簇紧密堆积。在 AFib 数据的情况下，图中的簇数为一或太多。我们使用留一交叉验证评估了准确性。平均灵敏度和平均特异性分别为 91.4%和 92.9%。
由于心室的脉搏跳动不太可能受到基线漂移和噪声的影响，我们使用心跳间隔来诊断 AFib。我们通过 Poincaré 图直观地显示了心跳间隔的规律性。我们尝试设计一种不需要任何人工干预和任何特定阈值的自动化算法，并将其安装在便携式 AFib 监测系统中。
