Practical fine-grained learning based anomaly classification for ECG image

As a widely used vital sign within cardiology, Electrocardiography (ECG) provides the basis for assessing heart function and diagnosing cardiovascular diseases. Automated anomaly detection for ECG plays an important role in improving patient diagnosis efficiency and reducing healthcare costs. Practically, due to the limits of electronics support or the medical system setting, image is a more common format for large-scale ECG storage in most clinical institutions. To guarantee an automated ECG detection model's scalability and practicality in clinical applications, taking good advantage of ECG images is crucial. However, existing time digital-based discriminative models fail to learn from images effectively for two reasons. First of all, the signals recorded on images have much lower resolution and higher noise, which makes it impractical to extract precise ECG signals following existing techniques. Meanwhile, the differences between abnormal signals are usually subtle, and they may be overwhelmed by the noises in the images as well. Towards this end, we design a novel neural framework that can be directly applied to massive ECG images determining various types of cardiology abnormalities. It classifies fine-grained ECG images based on weakly supervised strategy, in which case only image-level labeling is required. By eliminating the need for part annotations, the proposed method can result in significant savings in annotation time and cost. The effectiveness of the method is demonstrated by experimental results on two real ECG datasets.