Dias Felipe Meneguitti, Monteiro Henrique L M, Cabral Thales Wulfert, Naji Rayen, Kuehni Michael, Luz Eduardo José da S
Electrical Engineering Department, Universidade Federal de Juiz de Fora, Juiz de Fora, MG, Brazil.
Electrical Engineering Department, Universidade Federal de Juiz de Fora, Juiz de Fora, MG, Brazil.
Comput Methods Programs Biomed. 2021 Apr;202:105948. doi: 10.1016/j.cmpb.2021.105948. Epub 2021 Jan 26.
Arrhythmia is a heart disease characterized by the change in the regularity of the heartbeat. Since this disorder can occur sporadically, Holter devices are used for continuous long-term monitoring of the subject's electrocardiogram (ECG). In this process, a large volume of data is generated. Consequently, the use of an automated system for detecting arrhythmias is highly desirable. In this work, an automated system for classifying arrhythmias using single-lead ECG signals is proposed.
The proposed system uses a combination of three groups of features: RR intervals, signal morphology, and higher-order statistics. To validate the method, the MIT-BIH database was employed using the inter-patient paradigm. Besides, the robustness of the system against segmentation errors was tested by adding jitter to the R-wave positions given by the MIT-BIH database. Additionally, each group of features had its robustness against segmentation error tested as well.
The experimental results of the proposed classification system with jitter show that the sensitivities for the classes N, S, and V are 93.7, 89.7, and 87.9, respectively. Also, the corresponding positive predictive values are 99.2, 36.8, and 93.9, respectively.
The proposed method was able to outperform several state-of-the-art methods, even though the R-wave position was synthetically corrupted by added jitter. The obtained results show that our approach can be employed in real scenarios where segmentation errors and the inter-patient paradigm are present.
心律失常是一种以心跳节律变化为特征的心脏病。由于这种疾病可能偶尔发生，因此采用动态心电图监测仪对受试者的心电图（ECG）进行连续长期监测。在此过程中会产生大量数据。因此，非常需要使用自动系统来检测心律失常。在这项工作中，提出了一种使用单导联心电图信号对心律失常进行分类的自动系统。
所提出的系统使用三组特征的组合：RR间期、信号形态和高阶统计量。为了验证该方法，采用患者间范式使用麻省理工学院 - 贝斯以色列女执事医疗中心（MIT - BIH）数据库。此外，通过向MIT - BIH数据库给出的R波位置添加抖动来测试系统对分割误差 的鲁棒性。此外，还测试了每组特征对分割误差的鲁棒性。
带有抖动的所提出分类系统的实验结果表明，类别N、S和V的灵敏度分别为93.7、89.7和87.9。而且，相应的阳性预测值分别为99.2、36.8和93.9。
所提出的方法能够优于几种现有技术方法，即使R波位置因添加抖动而被综合破坏。获得的结果表明，我们的方法可用于存在分割误差和患者间范式的实际场景中。
