Atrial fibrillation onset localization based on multi-resolution convolutional network
LI Qian
WANG Xingyao
GAO Hongxiang
ZHAO Lina
LI Jianqing
LIU Chengyu
Abstract：In order to enhance paroxysmal atrial fibrillation(PAF)localization,we proposed a convolutional network-based multi-resolution ECG understanding framework.By harnessing both local high-resolution morphological features and global low-resolution rhythmic characteristics,the framework consistently maintained high-resolution features while progressively incorporated low-resolution feature branches.Through continuous integration of features from each branch,the high-resolution branch discriminated changes in P-wave morphology,while the low-resolution branch detected rhythmic alterations in RR intervals,thereby facilitated multiple tasks in-cluding PAF localization,AF classification,and QRS-wave localization.We trained the model on the CPSC 2021-Train database and conducted tests using two clinical ECG databases.The PAF localization scores on the two databases were 1.818 2 and 3.487 0,AF clas-sification and QRS-wave localization achieved mean F1 scores of 88.36%and 99.47%,respectively.These results affirm the efficacy of our approach in PAF endpoints and QRS-wave localization.
Keywords：Paroxysmal atrial fibrillationMulti-resolution featuresWearable electrocardiogramMulti-task
Publication Date：2024-02-28
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：9( 24-32 )