Abstract

Info

Author(s):: ZHANG Yidan; LIU Wenhan; ZHANG Mengxin; LIAO Yuan; HUANG Qijun; CHANG Sheng; WANG Hao; HE Jin; School of Physics and Technology, Wuhan University, Wuhan 430000, China

Keywords:: ; Keywords: premature ventricular contraction; left bundle branch block; Cascade classifier; support vector machine; weighted minimum distance classifier

Abstract:: Abstract: As common cardiac arrhythmias, premature ventricular contraction (PVC) and left bundle branch block (LBBB) have great significance in the diagnosis and prognosis of cardiovascular diseases. For the automatic detection of PVC and LBBB is proposed. By extracting the time-domain and morphological features, support vector machine (SVM) is utilized to distinguish PVC and non-PVC. The labeled non-PVC is then divided into normal (N) and LBBB using weighted minimum distance classifier (W-MDC). The proposed algorithm is evaluated using MIT-BIH arrhythmia database. The overall accuracy of N, LBBB and PVC classification is 96.28%. The sensitivity and specificity are 98.59% and 97.15% for N class, 81.41% and 91.89% for LBBB, 89.22% and 84.87% for PVC, respectively, which inter-patient heartbeat classification and the generalization ability of the proposed algorithm among different individuals. In addition, the synthesis of multi-leads information is also proved to be able to improve the LBBB detection performance.