Arrhythmia classification from single-lead ECG signals using the inter-patient paradigm

Arrhythmia classification from single-lead ECG signals using the inter-patient paradigm

•Addition of artificially generated jitter to the R-wave position to test the robustness of the classification system against segmentation errors.•Individual performance analysis of each group of features against segmentation errors.•Competitive results in comparison with other state-of-the-art meth...

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Bibliographic Details
Published in:Computer methods and programs in biomedicine Vol. 202; p. 105948
Main Authors: Dias, Felipe Meneguitti, Monteiro, Henrique L.M., Cabral, Thales Wulfert, Naji, Rayen, Kuehni, Michael, Luz, Eduardo José da S.
Format: Journal Article
Language:English
Published: Ireland Elsevier B.V 01-04-2021
Subjects:
Algorithms
Arrhythmias, Cardiac - diagnosis
Databases, Factual
ECG classification
Electrocardiogram
Electrocardiography
Heart Diseases
Heart Rate
Humans
Inter-patient
Jitter
Machine learning
Segmentation error
Signal Processing, Computer-Assisted
Segmentation error
Jitter
Inter-patient
Electrocardiogram
ECG classification
Machine learning
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Description
Summary:•Addition of artificially generated jitter to the R-wave position to test the robustness of the classification system against segmentation errors.•Individual performance analysis of each group of features against segmentation errors.•Competitive results in comparison with other state-of-the-art methods for ECG classification. 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.
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ISSN:0169-2607
1872-7565
DOI:10.1016/j.cmpb.2021.105948