Evaluation of an interpretable 12-lead ECG automatic diagnosis model based on deep feature fusion
LU Xueqi
CHEN Huayuan
WU Qiucen
WEN Yaoqi
LIU Guoguang
CHEN Chaomin
Abstract：Objective To enhance the accuracy and reliability of 12-lead electrocardiogram(ECG)automatic diagnosis.Methods Herein we propose a 12-lead ECG automatic diagnosis model based on deep feature fusion(MRHL-ECGNet),which consists of a multi-scale feature extraction front-end,ResNet-34,a global feature mixing module,and a time-series analysis module.The Hyena Hierarchy Convolution Operator was applied to the 12-lead ECG automatic diagnosis task for more efficient capture of long-range dependencies while reducing computational complexity.Integrated Gradients(IG)-based interpretability analysis technology was used to achieve visualization of the decision-making basis of MRHL-ECGNet.The CPSC2018 dataset was used to train and test MRHL-ECGNet,and its performance was assessed using multiple quantitative evaluation indicators and evaluation experiments.Results In the 9-class ECG classification task on the test set,MRHL-ECGNet achieved an accuracy of 0.972,an AUC of 0.983,an F1 score of 0.864,a precision of 0.873,and a recall of 0.857,all surpassing other comparative models.This model only took 0.007 s to output a diagnosis for a single sample on a GPU and 0.156 s on a CPU,with a memory footprint of 67.196 MB.Conclusion The proposed MRHL-ECGNet model demonstrates excellent classification performance in 12-lead ECG automatic diagnosis with a lightweight design and good interpretability,and thus has great potential for clinical application in ECG-aided diagnosis.
Keywords：electrocardiogram automatic diagnosisdeep learningHyena Hierarchy Convolution Operatorinterpretability of model
Publication Date：2026-01-20
Online Publishing Date：2026-01-22(First online date of this platform, not the publication date of the document)
Pages：11( 208-218 )