Department of Computer Engineering and Computer Science, California State University, Long Beach, 1250 Bellflower Boulevard-MS 8302, Long Beach, CA 90840, USA.
Department of Electrical Engineering, University of California, Los Angeles, 56-125B Engineering IV Building, Box 951594, Los Angeles, CA 90095, CA.
Comput Biol Med. 2015 May;60:132-42. doi: 10.1016/j.compbiomed.2015.03.005. Epub 2015 Mar 14.
Atrial fibrillation (AF) is the most common cardiac arrhythmia, and a major public health burden associated with significant morbidity and mortality. Automatic detection of AF could substantially help in early diagnosis, management and consequently prevention of the complications associated with chronic AF. In this paper, we propose a novel method for automatic AF detection.
Stationary wavelet transform and support vector machine have been employed to detect AF episodes. The proposed method eliminates the need for P-peak or R-Peak detection (a pre-processing step required by many existing algorithms), and hence its performance (sensitivity, specificity) does not depend on the performance of beat detection. The proposed method has been compared with those of the existing methods in terms of various measures including performance, transition time (detection delay associated with transitioning from a non-AF to AF episode), and computation time (using MIT-BIH Atrial Fibrillation database).
Results of a stratified 2-fold cross-validation reveals that the area under the Receiver Operative Characteristics (ROC) curve of the proposed method is 99.5%. Moreover, the method maintains its high accuracy regardless of the choice of the parameters' values and even for data segments as short as 10s. Using the optimal values of the parameters, the method achieves sensitivity and specificity of 97.0% and 97.1%, respectively.
The proposed AF detection method has high sensitivity and specificity, and holds several interesting properties which make it a suitable choice for practical applications.
心房颤动（AF）是最常见的心律失常，也是与重大发病率和死亡率相关的主要公共卫生负担。自动检测 AF 可以极大地帮助早期诊断、管理，并因此预防与慢性 AF 相关的并发症。在本文中，我们提出了一种用于自动 AF 检测的新方法。
采用平稳小波变换和支持向量机来检测 AF 发作。所提出的方法消除了对 P 峰或 R 峰检测的需求（许多现有算法所需的预处理步骤），因此其性能（灵敏度、特异性）不依赖于节拍检测的性能。在所提出的方法中，已经根据各种措施（包括性能、过渡时间（从非 AF 到 AF 发作的检测延迟）和计算时间（使用 MIT-BIH 心房颤动数据库））与现有方法进行了比较。
分层 2 折交叉验证的结果表明，所提出的方法的接收器操作特性（ROC）曲线下面积为 99.5%。此外，该方法无论参数值的选择如何，甚至对于短至 10s 的数据段，都能保持其高精度。使用参数的最优值，该方法的灵敏度和特异性分别为 97.0%和 97.1%。
所提出的 AF 检测方法具有较高的灵敏度和特异性，并且具有几个有趣的特性，使其成为实际应用的合适选择。
