Interactive ECG annotation: An artificial intelligence method for smart ECG manipulation

An electrocardiogram (ECG) consists of complex segments, such as P-QRS-T waves. Manual ECG annotation is challenging and time-consuming, even for specialist physicians. The shortage of labelled ECG data is one of the essential factors that affect ECG intelligent analysis's long-term development. This study proposes an intelligent ECG-assisted annotation system, that not only supplements labelled data, but also significantly reduces the workload compared with manual annotation. Since beat annotation is the most basic and important part, a GAN-based generation model that can generate 14 types of simulation beats and a CNN-based beat pre-annotation model are proposed. The experimental results show that the simulation beat has high similarity to real beat and the accuracy of the pre annotation model on the test set of 14 classes of beats is 99.28%. The proposed ECG intelligent annotation system's self-learning mechanism could improve pre-annotation performance and annotation efficiency by generating more labelled data. The proposed annotation system can also be extended to other data annotation applications. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

This study introduces an intelligent ECG-assisted annotation system that uses GAN and CNN models to generate simulated beats and achieve high accuracy in pre-annotation. The self-learning mechanism of the system improves pre-annotation performance and efficiency.