Nef-Net v2: Adapting Electrocardio Panorama in the wild

Zehui Zhan, Yaojun Hu, Jiajing Zhang, Wanchen Lian, Wanqing Wu, Jintai Chen

Keywords: ECG representation, Cardiac Diagnosis

TL;DR: An enhanced variant of Nef-Net to generate panoramic ECG views, including previously unseen views.

Abstract:

Conventional multi-lead electrocardiogram (ECG) systems capture cardiac signals
from a fixed set of anatomical viewpoints defined by lead placement. However, cer-
tain cardiac conditions (e.g., Brugada syndrome) require additional, non-standard
viewpoints to reveal diagnostically critical patterns that may be absent in standard
leads. To systematically overcome this limitation, Nef-Net was recently introduced
to reconstruct a continuous electrocardiac field, enabling virtual observation of
ECG signals from arbitrary views (termed Electrocardio Panorama). Despite
its promise, Nef-Net operates under idealized assumptions and faces in-the-wild
challenges, such as long-duration ECG modeling, robustness to device-specific
signal artifacts, and suboptimal lead placement calibration. This paper presents
NEF-NET V2, an enhanced framework for realistic panoramic ECG synthesis that
supports arbitrary-length signal synthesis from any desired view, generalizes across
ECG devices, and compensates for operator-induced deviations in electrode place-
ment. These capabilities are enabled by a newly designed model architecture that
performs direct view transformation, incorporating a workflow comprising offline
pretraining, device calibration tuning steps as well as an on-the-fly calibration step
for patient-specific adaptation. To rigorously evaluate panoramic ECG synthe-
sis, we construct a new Electrocardio Panorama benchmark, called Panobench,
comprising 4470 recordings with 48 views per subject, capturing the full spatial
variability of cardiac electrical activity. Experimental results show that NEF-NET
V2 delivers substantial improvements over Nef-Net, yielding an increase of around
6 dB in PSNR in real-world settings. Our data and code are publicly available at
https://github.com/HKUSTGZ-ML4Health-Lab/NEFNET-v2.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

Submission Number: 9610