FM-ECG: A fine-grained multi-label framework for ECG image classification

Recently, increasingly more methods are proposed to automatically detect the abnormalities in Electrocardiography (ECG). Despite their success on public golden standard datasets, two challenges hinder the adoption of existing methods on real-world clinical ECG data in practice. To start with, most methods are designed based on digital signal data while most ECG data in the hospital are stored as images. Additionally, they ignore the correlation among different abnormal cardiac patterns and hence cannot detect multiple abnormalities at the same time. To practically address these challenges, we propose a Fine-grained Multi-label ECG (FM-ECG) framework to effectively detect the abnormalities from the real clinical ECG data in the following two aspects. Firstly, we propose to directly detect the abnormalities on the ECG images via a weakly supervised fine-grained classification mechanism, which can discover the potential discriminative parts and adaptively fuse them via image-level annotations only. Secondly, we take the ECG label dependencies into consideration by inferencing with a recurrent neural network (RNN). Experimental results on two real-world large-scale ECG datasets prove the capability of FM-ECG comparing with other state-of-the-art methods in ECG abnormally detection. Moreover, visualization analyses on attention parts show that meaningful spatial attention can be effectively learned by FM-ECG. (C) 2020 Elsevier Inc. All rights reserved.

Automated summary

The FM-ECG framework addresses the challenges of detecting abnormalities in real-world clinical ECG data by directly detecting abnormalities on ECG images with weakly supervised fine-grained classification and considering ECG label dependencies, outperforming other state-of-the-art methods.