Zhang Xiangyu, Li Jianqing, Cai Zhipeng, Zhao Lina, Liu Chengyu
State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, China.
Front Physiol. 2022 Apr 1;13:890139. doi: 10.3389/fphys.2022.890139. eCollection 2022.
Paroxysmal atrial fibrillation (PAF) may related to the risk of thromboembolism and is the most common cardiac risk factor of cryptogenic stroke (CS). Due to its paroxysmal characteristics, it is usually diagnosed by continuous long-term ECG. Patients with paroxysmal atrial fibrillation usually have premature beats at the same time which is easy to be confused with the rhythm of atrial fibrillation. Therefore, in this article, we designed a screening algorithm for single premature beat, multi premature beats, bigeminy and trigeminy premature beats, according to their rhythm characteristics to reduce false detection caused by premature beats during the PAF detection process. The proposed elimination method was verified on ECG segments with different types of premature beats, and tested on long-term ECG data of PAF patients. ECG segments of different kinds of premature beats were selected from MIT Atrial Fibrillation database (MIT-AFDB), MIT-BIH Arrhythmia database (MIT-AR) and wearable ECG data from the China Physiological Signal Challenge 2021 (CPSC 2021). The proposed method can effectively eliminate single premature beat segments with 99.5% accuracy, and it also can eliminate more than 95% of ECG segments with other types of premature beats. We designed PAF-score as a new index to evaluate the accuracy of detection, and we also calculate the misjudged and missed segments to comprehensively evaluate the PAF detection algorithm. The proposed method get a PAF-score of 0.912 on MIT-AFDB. The proposed method also has the potential to implant low computing power wearable devices for real-time analysis.
阵发性心房颤动（PAF）可能与血栓栓塞风险相关，并且是隐源性卒中（CS）最常见的心脏危险因素。由于其阵发性特征，通常通过连续长期心电图进行诊断。阵发性心房颤动患者通常同时伴有早搏，这容易与心房颤动的节律相混淆。因此，在本文中，我们根据单早搏、多早搏、二联律和三联律早搏的节律特征设计了一种筛查算法，以减少PAF检测过程中由早搏引起的误检。所提出的消除方法在具有不同类型早搏的心电图片段上进行了验证，并在PAF患者的长期心电图数据上进行了测试。从麻省理工学院心房颤动数据库（MIT-AFDB）、麻省理工学院-比哈尔心律失常数据库（MIT-AR）以及2021年中国生理信号挑战赛（CPSC 2021）的可穿戴心电图数据中选取了不同类型早搏的心电图片段。所提出的方法能够以99.5%的准确率有效消除单早搏片段，并且还能够消除超过95%的具有其他类型早搏的心电图片段。我们设计了PAF评分作为评估检测准确性的新指标，并且还计算了误判和漏判片段以全面评估PAF检测算法。所提出的方法在MIT-AFDB上获得了0.912的PAF评分。所提出的方法还有潜力植入低计算能力的可穿戴设备进行实时分析。
