Felipe Meneguitti Dias1, Henrique L M Monteiro2, Thales Wulfert Cabral3, Rayen Naji4, Michael Kuehni5, Eduardo José da S Luz6. 1. Electrical Engineering Department, Universidade Federal de Juiz de Fora, Juiz de Fora, MG, Brazil. Electronic address: felipe.dias@engenharia.ufjf.br. 2. Electrical Engineering Department, Universidade Federal de Juiz de Fora, Juiz de Fora, MG, Brazil. 3. Electrical Engineering Department, Universidade Estadual de Campinas, Campinas, SP, Brazil. 4. Medical School, Universidade Federal de Juiz de Fora, Juiz de Fora, MG, Brazil. 5. Illinois Institute of Technology, Chicago, IL, United States. 6. Computing Department, Universidade Federal de Ouro Preto, Ouro Preto, MG, Brazil.
Abstract
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.
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.
Authors: Congyu Zou; Alexander Muller; Utschick Wolfgang; Daniel Ruckert; Phillip Muller; Matthias Becker; Alexander Steger; Eimo Martens Journal: IEEE J Transl Eng Health Med Date: 2022-08-29