Archive | 2019
Automated Detection of First-Degree Atrioventricular Block Using ECGs
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%.