Zhaowei Zhu1, Xiang Lan2, Tingting Zhao1, Yangming Guo1, Pipin Kojodjojo3, Zhuoyang Xu1, Zhuo Liu1, Siqi Liu4, Han Wang2, Xingzhi Sun1, Mengling Feng2. 1. Ping An Technology, Beijing, Beijing, CHINA. 2. Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore, SINGAPORE. 3. Yong Loo Lin School of Medicine, National University Health System, National University of Singapore, Singapore, SINGAPORE. 4. NUS Graduate School for Integrative Sciences and Engineering, Singapore, SINGAPORE.
Abstract
OBJECTIVE: Cardiovascular disease is a major threat to health and one of the primary causes of death globally. The 12-lead ECG is a cheap and commonly accessible tool to identify cardiac abnormalities. Early and accurate diagnosis will allow early treatment and intervention to prevent severe complications of cardiovascular disease. Our objective is to develop an algorithm that automatically identifies 27 ECG abnormalities from 12-lead ECG databases. APPROACH: Firstly, a series of pre-processing methods were proposed and applied on various data sources in order to mitigate the problem of data divergence. Secondly, we ensembled two SE_ResNet models and one rule-based model to enhance the performance of various ECG abnormalities' classification. Thirdly, we introduce a Sign Loss to tackle the problem of class imbalance, and thus improve the model's generalisability. MAIN RESULTS: In the PhysioNet/Computing in Cardiology Challenge 2020, our proposed approach achieved a challenge validation score of 0.682, and a full test score of 0.514, placed us 3rd out of 40 in the official ranking. SIGNIFICANCE: We proposed an accurate and robust predictive framework that combines deep neural networks and clinical knowledge to automatically classify multiple ECG abnormalities. Our framework is able to identify 27 ECG abnormalities from multi-lead ECG signals regardless of discrepancies in data sources and the imbalance of data labelling. We trained our framework on five datasets and validated it on six datasets from various countries. The outstanding performance demonstrate the effectiveness of our proposed framework. Creative Commons Attribution license.
OBJECTIVE:Cardiovascular disease is a major threat to health and one of the primary causes of death globally. The 12-lead ECG is a cheap and commonly accessible tool to identify cardiac abnormalities. Early and accurate diagnosis will allow early treatment and intervention to prevent severe complications of cardiovascular disease. Our objective is to develop an algorithm that automatically identifies 27 ECG abnormalities from 12-lead ECG databases. APPROACH: Firstly, a series of pre-processing methods were proposed and applied on various data sources in order to mitigate the problem of data divergence. Secondly, we ensembled two SE_ResNet models and one rule-based model to enhance the performance of various ECG abnormalities' classification. Thirdly, we introduce a Sign Loss to tackle the problem of class imbalance, and thus improve the model's generalisability. MAIN RESULTS: In the PhysioNet/Computing in Cardiology Challenge 2020, our proposed approach achieved a challenge validation score of 0.682, and a full test score of 0.514, placed us 3rd out of 40 in the official ranking. SIGNIFICANCE: We proposed an accurate and robust predictive framework that combines deep neural networks and clinical knowledge to automatically classify multiple ECG abnormalities. Our framework is able to identify 27 ECG abnormalities from multi-lead ECG signals regardless of discrepancies in data sources and the imbalance of data labelling. We trained our framework on five datasets and validated it on six datasets from various countries. The outstanding performance demonstrate the effectiveness of our proposed framework. Creative Commons Attribution license.