Journal Article
A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram
Zijian Ding; Guijin Wang; Huazhong Yang; Ping Zhang; Dapeng Fu; Zhen Yang; Xinkang Wang; Xia Wang; Zhourui Xia; Chiming Zhang; Wenjie Cai; Binhang Yuan; Dongya Jia; Bo Chen; Chengbin Huang; Jing Zhang; Yi Li; Shan Yang; Runnan He
Medical & Biological Engineering & Computing · Vol. 60, Issue 1 · pp. 33-45 · 2022
Abstract
Computerized interpretation of electrocardiogram plays an important role in daily cardiovascular healthcare. However, inaccurate interpretations lead to misdiagnoses and delay proper treatments. In this work, we built a high-quality Chinese 12-lead resting electrocardiogram dataset with 15,357 records, and called for a community effort to improve the performances of CIE through the China ECG AI Contest 2019. This dataset covers most types of ECG interpretations, including the normal type, 8 common abnormal types, and the other type which includes both uncommon abnormal and noise signals. Based on the Contest, we systematically assessed and analyzed a set of top-performing methods, most of which are deep neural networks, with both their commonalities and characteristics. This study establishes the benchmarks for computerized interpretation of 12-lead resting electrocardiogram and provides insights for the development of new methods.