A Novel Detection Method of Bundle Branch Block from Multi-Lead ECG

Jing Hu1, Wei Zhao1, Dongya Jia2, Cong Yan1, Hongmei Wang1, Zhenqi Li1, Tianyuan You2

  • 1Guangzhou Shiyuan Electronics Co., Ltd
  • 2CVTE, Guangdong Province, China

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Oral Session

Sessions

08:30 - 10:00 | Wed 24 Jul | Hall A2 - Level 1 | WeA05

Signal Processing and Classification of Cardiac Signals

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Abstract

Bundle branch block (BBB) is a common conduction block disease and can be diagnosed using electrocardiogram (ECG) signal in clinical practice. In this paper, a novel method was proposed to detect two types of BBB: right BBB (RBBB) and left BBB (LBBB) based on the combination of deep features and several kinds of expert features. We evaluated the proposed method on the MIT-BIH Arrhythmia database (AR) and China Physiological Signal Challenge 2018 database (CPSC). The proposed method achieved an accuracy of 99.96% (AR) in the class-oriented evaluation and an accuracy of 98.76% (AR) and 97.88% (CPSC) in the subject-oriented evaluation, better than the baseline methods. Experimental results show that our method would be a good choice for the detection of the BBB.

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