Arrhythmia classification from single-lead ECG signals using the inter-patient paradigm
Arrhythmia classification from single-lead ECG signals using the inter-patient paradigm
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DOI：
10.1016/j.cmpb.2021.105948
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发表时间：
2021-02-12
影响因子：
6.1
通讯作者：
Luz, Eduardo Jose da S.
中科院分区：
文献类型：
--
作者：
Dias, Felipe Meneguitti;Monteiro, Henrique L. M.;Luz, Eduardo Jose da S.
背景和目的：心律失常是一种以心跳规律性改变为特征的心脏病。由于这种疾病可以偶尔发生，霍尔特设备用于连续长期监测的主题？的心电图（ECG）。在这个过程中，产生了大量的数据。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，提出了一种自动系统，用于分类心律失常使用单导联ECG信号。方法：该系统使用三组特征的组合：RR间期、信号形态和高阶统计。为了验证该方法，采用MIT-BIH数据库，使用患者间范例。此外，通过在MIT-BIH数据库给出的R波位置上添加抖动来测试系统对分割错误的鲁棒性。此外，每组特征还测试了其对分割错误的鲁棒性。结果如下：实验结果表明，建议的分类系统与抖动的灵敏度为N，S，V类分别为93.7，89.7和87.9，分别。此外，相应的阳性预测值分别为99.2、36.8和93.9。结论：所提出的方法是能够优于几个国家的最先进的方法，即使R波的位置是由添加的抖动综合损坏。所得到的结果表明，我们的方法可以采用在真实的场景中的分割错误和患者间的范例。? 2021 Elsevier B. V.版权所有。背景和目的：心律失常是一种以心跳规律性改变为特征的心脏病。由于这种疾病可以偶尔发生，霍尔特设备用于连续长期监测的主题？的心电图（ECG）。在这个过程中，产生了大量的数据。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，提出了一种自动系统，用于分类心律失常使用单导联ECG信号。方法：该系统使用三组特征的组合：RR间期、信号形态和高阶统计。为了验证该方法，采用MIT-BIH数据库，使用患者间范例。此外，通过在MIT-BIH数据库给出的R波位置上添加抖动来测试系统对分割错误的鲁棒性。此外，每组特征还测试了其对分割错误的鲁棒性。结果如下：实验结果表明，建议的分类系统与抖动的灵敏度为N，S，V类分别为93.7，89.7和87.9，分别。此外，相应的阳性预测值分别为99.2、36.8和93.9。结论：所提出的方法是能够优于几个国家的最先进的方法，即使R波的位置是由添加的抖动综合损坏。所得到的结果表明，我们的方法可以采用在真实的场景中的分割错误和患者间的范例。
Background and objectives: 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. Methods: 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. Results: 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. Conclusions: 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. ? 2021 Elsevier B.V. All rights reserved.Background and objectives: 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. Methods: 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. Results: 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. Conclusions: 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.