Electrocardio Panorama: Synthesizing New ECG Views with Self-supervision

TL;DR

This paper introduces Electrocardio Panorama, a novel method for synthesizing ECG views from any angle using a neural electrocardio field network and self-supervised learning, enhancing ECG visualization and analysis.

Contribution

The paper proposes Nef-Net, a neural network that predicts electrocardio fields from limited views and synthesizes new ECG perspectives, including from scratch, with self-supervised training.

Findings

Nef-Net outperforms previous methods on ECG view transformation.

It effectively synthesizes ECG signals from limited input views.

The approach enables flexible ECG visualization from arbitrary viewpoints.

Abstract

Multi-lead electrocardiogram (ECG) provides clinical information of heartbeats from several fixed viewpoints determined by the lead positioning. However, it is often not satisfactory to visualize ECG signals in these fixed and limited views, as some clinically useful information is represented only from a few specific ECG viewpoints. For the first time, we propose a new concept, Electrocardio Panorama, which allows visualizing ECG signals from any queried viewpoints. To build Electrocardio Panorama, we assume that an underlying electrocardio field exists, representing locations, magnitudes, and directions of ECG signals. We present a Neural electrocardio field Network (Nef-Net), which first predicts the electrocardio field representation by using a sparse set of one or few input ECG views and then synthesizes Electrocardio Panorama based on the predicted representations. Specially, to better disentangle electrocardio field information from viewpoint biases, a new Angular Encoding is proposed to process viewpoint angles. Also, we propose a self-supervised learning approach called Standin Learning, which helps model the electrocardio field without direct supervision. Further, with very few modifications, Nef-Net can also synthesize ECG signals from scratch. Experiments verify that our Nef-Net performs well on Electrocardio Panorama synthesis, and outperforms the previous work on the auxiliary tasks (ECG view transformation and ECG synthesis from scratch). The codes and the division labels of cardiac cycles and ECG deflections on Tianchi ECG and PTB datasets are available at https://github.com/WhatAShot/Electrocardio-Panorama.