The State Key Laboratory of Bioelectronics, Jiangsu Key Lab of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, People's Republic of China.
Physiol Meas. 2020 Jun 10;41(5):054004. doi: 10.1088/1361-6579/ab87b4.
Premature beats (PB), typically presenting as premature ventricular contractions (PVC) and premature atrial contractions (PAC), may foreshadow stroke or sudden cardiac death.
A rule-based real-time PB detection system was proposed for timely diagnosis of common PAC and PVC in an ambulatory setting and to reduce the cognitive load for physicians. The proposed method consists of three procedures: (1) extraction of the RR interval, QRS complex template, width and height; (2) rough detection of PB candidates using rules corresponding to abnormality in rhythm and morphology; and (3) refined detection using three types of correction. The method was trained using randomly selected single-lead waveforms sourced from the China Physiological Signal Challenge 2018 (CPSC2018) database, and the method was tested on the 12-lead CPSC2018 database, the MIT-BIH-AR database and the wearable ECG database.
Four quantitative parameters, namely sensitivity, positive predictive value, accuracy and F1 measure, were used to assess performance. The F1 measure for normal beats, PACs, and PVCs were 99.37, 90.6, and 90.85% in training data (93.61% across all beats). Satisfactory results on the 12-lead CPSC2018 database indicated that the method had a good generalization ability between leads. Although the results on the MIT-BIH-AR database were not comparable with other methods, it showed stability in different testing databases. In addition, the test results on wearable ECGs manifested that the method was robust and could provide a good algorithm basis for IoT applications.
We have developed a rule-based method for real-time PB detection in single-lead ECG, which balances the computational complexity and recognition accuracy, indicating the clinical significance of the method.
早搏（PB），通常表现为室性早搏（PVC）和房性早搏（PAC），可能预示着中风或心源性猝死。
提出了一种基于规则的实时 PB 检测系统，用于及时诊断动态心电图中的常见 PAC 和 PVC，并减轻医生的认知负担。该方法包括三个步骤：（1）提取 RR 间期、QRS 复合模板、宽度和高度；（2）使用对应节律和形态异常的规则粗略检测 PB 候选者；（3）使用三种校正方法进行精细检测。该方法使用来自中国生理信号挑战赛 2018 年（CPSC2018）数据库的随机选择的单导联波形进行训练，并在 12 导联 CPSC2018 数据库、麻省理工学院-贝斯以色列女执事医疗中心（MIT-BIH-AR）数据库和可穿戴心电图数据库上进行测试。
使用灵敏度、阳性预测值、准确率和 F1 度量四个定量参数来评估性能。在训练数据中，正常心搏、PAC 和 PVC 的 F1 度量分别为 99.37%、90.6%和 90.85%（所有心搏的平均值为 93.61%）。在 12 导联 CPSC2018 数据库上的令人满意的结果表明该方法在导联之间具有良好的泛化能力。尽管在 MIT-BIH-AR 数据库上的结果无法与其他方法进行比较，但它在不同的测试数据库中表现出了稳定性。此外，可穿戴心电图上的测试结果表明该方法具有鲁棒性，可为物联网应用提供良好的算法基础。
我们已经开发了一种用于单导联心电图实时 PB 检测的基于规则的方法，该方法在计算复杂度和识别准确性之间取得了平衡，表明了该方法的临床意义。
