关键词: 卷积神经网络（CNN）, 极限学习机（ELM）, 心电逆问题, 心肌跨膜电位

Abstract: Non-invasively reconstructing the cardiac TransMembrane Potentials（TMP） from Body Surface Potentials（BSP） can be acted as a regression problem with multi input and multi output. Data driven models based machine learning is an effective to approach the regression problem. Here, deep learning architecture is implemented by using Convolution Neural Network（CNN）, run on Caffe framework. Moreover, based on the realistic heart-torso models, ECGSim software is used to simulate the data on the ventricular activation of Kent’s bundle syndrome for training and testing regression model. The experimental results show that compared with the Extreme Learning Machine（ELM） and the kernelized extreme learning machine, the CNN method can perform better regression ability in terms of the TMP reconstruction accuracy.

Key words: Convolution Neural Network（CNN）, Extreme Learning Machine（ELM）, ECG inverse problem, cardiac transmembrane potentials