Micro Systems Engineering, Inc., Lake Oswego, Oregon, USA.
Am J Cardiol. 2011 May 15;107(10):1494-7. doi: 10.1016/j.amjcard.2011.01.028. Epub 2011 Mar 17.
Implantable loop recorders have been developed for long-term monitoring of cardiac arrhythmia, but their accuracy for atrial fibrillation (AF) detection is unsatisfactory. We sought to develop and evaluate a simple method for detecting AF using RR intervals. The new AF detection algorithm is based on a map that plots RR intervals versus change of RR intervals (RdR). The map is divided by a grid with 25-ms resolution in 2 axes and nonempty cells are counted to classify AF and non-AF episodes. We evaluated the performance of the method using 4 PhysioNet databases: MIT-BIH AF database, MIT-BIH arrhythmia database, MIT-BIH normal sinus rhythm (NSR) database, and NSR RR interval database (total 145 patients, 1,826 hours NSR, 96 hours AF, and 11 hours other rhythms). Each record is divided into consecutive windows containing 32, 64, or 128 RR intervals. AF detection is performed for each window and classification results are compared to annotations. A window is labeled true AF if >1/2 of cycles in the window are annotated as AF or non-AF otherwise. The RdR map shows signature patterns corresponding to various heart rhythms. Optimal nonempty cell cut-off threshold for AF detection was determined by receiver operating characteristic curve analysis, which yields excellent sensitivity and specificity for window sizes 32 (94.4% and 92.6%, respectively), 64 (95.8% and 94.3%), and 128 (95.9% and 95.4%). In conclusion, a single metric derived from the RdR map can achieve robust AF detection within as few as 32 heart beats.
植入式环路记录器已被开发用于长期监测心律失常，但它们在检测心房颤动 (AF) 方面的准确性并不令人满意。我们试图开发和评估一种使用 RR 间隔检测 AF 的简单方法。新的 AF 检测算法基于 RR 间隔与 RR 间隔变化 (RdR) 的关系图。该图以 25-ms 分辨率在 2 个轴上划分网格，并计算非空单元格以分类 AF 和非 AF 发作。我们使用 4 个 PhysioNet 数据库评估了该方法的性能：MIT-BIH AF 数据库、MIT-BIH 心律失常数据库、MIT-BIH 正常窦性节律 (NSR) 数据库和 NSR RR 间隔数据库（共 145 名患者，1826 小时 NSR、96 小时 AF 和 11 小时其他节律）。每个记录分为包含 32、64 或 128 个 RR 间隔的连续窗口。为每个窗口执行 AF 检测，并将分类结果与注释进行比较。如果窗口中 >1/2 的周期被注释为 AF 或非 AF，则将该窗口标记为真 AF。RdR 图显示了与各种心律相对应的特征模式。通过接受者操作特征曲线分析确定了用于 AF 检测的最佳非空单元格截止阈值，对于窗口大小 32（分别为 94.4%和 92.6%）、64（分别为 95.8%和 94.3%）和 128（分别为 95.9%和 95.4%），该方法具有出色的灵敏度和特异性。总之，从 RdR 图中得出的单个指标可以在仅 32 个心跳内实现稳健的 AF 检测。
