Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang University, Hangzhou 310058, China.
J Zhejiang Univ Sci B. 2012 Sep;13(9):751-6. doi: 10.1631/jzus.B1200107.
Atrial fibrillation (AF) has been considered as a growing epidemiological problem in the world, with a substantial impact on morbidity and mortality. Ambulatory electrocardiography (e.g., Holter) monitoring is commonly used for AF diagnosis and therapy and the automated detection of AF is of great significance due to the vast amount of information provided. This study presents a combined method to achieve high accuracy in AF detection. Firstly, we detected the suspected transitions between AF and sinus rhythm using the delta RR interval distribution difference curve, which were then classified by a combination analysis of P wave and RR interval. The MIT-BIH AF database was used for algorithm validation and a high sensitivity and a high specificity (98.2% and 97.5%, respectively) were achieved. Further, we developed a dataset of 24-h paroxysmal AF Holter recordings (n=45) to evaluate the performance in clinical practice, which yielded satisfactory accuracy (sensitivity=96.3%, specificity=96.8%).
心房颤动（AF）已被认为是世界上一个日益严重的流行病学问题，对发病率和死亡率有重大影响。动态心电图（如 Holter）监测常用于 AF 的诊断和治疗，自动检测 AF 具有重要意义，因为它提供了大量信息。本研究提出了一种联合方法，以实现 AF 检测的高精度。首先，我们使用 delta RR 间隔分布差异曲线检测 AF 和窦性节律之间疑似的转变，然后通过 P 波和 RR 间隔的组合分析对其进行分类。使用 MIT-BIH AF 数据库对算法进行验证，实现了高灵敏度和高特异性（分别为 98.2%和 97.5%）。此外，我们开发了一个 24 小时阵发性 AF Holter 记录数据集（n=45），以评估其在临床实践中的性能，结果具有令人满意的准确性（灵敏度=96.3%，特异性=96.8%）。
