Improving Automatic Detection of ECG Abnormality with Less Manual Annotations using Siamese Network

Electrocardiography is a very common, non-invasive diagnostic procedure and its interpretation is increasingly supported by automatic interpretation algorithms. Recently many works also focused on the design of automatic ECG abnormality detection algorithms. However, clinical electrocardiogram datasets often suffer from their heavy needs for expert annotations, which are often expensive and hard to obtain. In this work, we proposed a weakly supervised pretraining method based on the Siamese neural network, which utilizes the original diagnostic information written by physicians to produce useful feature representations of the ECG signal which improves performance of ECG abnormality detection algorithms with fewer expert annotations. The experiment showed that with the proposed weekly supervised pretraining, the performance of ECG abnormality detection algorithms that was trained with only 1/8 annotated ECG data outperforms classical models that was trained with fully annotated ECG data, which implies a large proportion of annotation resource could be saved. The proposed technique could be easily extended to other tasks beside abnormality detection provided that the text similarity metric is specifically designed for the given task.Clinical Relevance—This work proposes a novel framework for the automatic detection of cardiovascular disease based on electrocardiogram