Abstract
Automated detection of first-degree atrioventricular block (I-AVB) using electrocardiogram (ECG) has been paid more and more attraction since it is very helpful for the timely and efficient diagnosis and treatment of AVB-related heart diseases. In this paper, a novel automated I-AVB detection method FPR\(_{dur}\)-SVM is proposed, where the I-AVB feature FPR\(_{dur}\) is extracted from ECGs and then fed into the support vector machine (SVM) to differentiating I-AVB ECG from normal ECG. Performances of the proposed method FPR\(_{dur}\)-SVM are verified on the China Physiological Signal Challenge 2018 Database (CPSC2018). Simulation results show that the accuracy, sensitivity and specificity are reached 98.5%, 98.7% and 98.3%.
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This work was supported by the Innovative Talents Promotion Plan of Shaanxi Province under Grant 2018TD-016.
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Mao, L., Chen, H., Bai, J., Wei, J., Li, Q., Zhang, R. (2019). Automated Detection of First-Degree Atrioventricular Block Using ECGs. In: Wang, H., Siuly, S., Zhou, R., Martin-Sanchez, F., Zhang, Y., Huang, Z. (eds) Health Information Science. HIS 2019. Lecture Notes in Computer Science(), vol 11837. Springer, Cham. https://doi.org/10.1007/978-3-030-32962-4_15
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