The invention discloses an electrophysiological
signal fusion method and device, relates to the technical field of
biomedicine, and mainly aims to solve the problem of low
cardiac imaging evaluation accuracy caused by poor effectiveness of existing electrophysiological
signal fusion. The method includes the steps that electrophysiological signals obtained after cardiac organs are collected and subjected to active
magnetic field compensation and passive
magnetic field compensation are obtained, and active
magnetic field compensation signals of active magnetic field compensation are completed by controlling a main control magnetic field source based on a deep neural
network model; a passive magnetic field compensation
signal of the passive magnetic field compensation is completed based on
residual magnetic field data modeling; screening effective signals from the electrophysiological signals, and performing inversion reconstruction on the effective signals to obtain
current density distribution; cross-
modal fusion is conducted on the electrophysiological signals, the
current density distribution and the electrocardiosignals through a
deep learning model after model training is completed, a
signal fusion result is obtained, and the
deep learning model is trained based on Maxwell constraints.