生物医学工程研究2024，Vol.43Issue(1)：24-32,9.DOI:10.19529/j.cnki.1672-6278.2024.01.04
基于多分辨率卷积网络的房颤起始点定位
Atrial fibrillation onset localization based on multi-resolution convolutional network
摘要
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.关键词
阵发性房颤/多分辨率特征/穿戴式心电/多任务Key words
Paroxysmal atrial fibrillation/Multi-resolution features/Wearable electrocardiogram/Multi-task分类
医药卫生引用本文复制引用
李茜,王星尧,高鸿祥,赵莉娜,李建清,刘澄玉..基于多分辨率卷积网络的房颤起始点定位[J].生物医学工程研究,2024,43(1):24-32,9.基金项目
国家自然科学基金资助项目(62171123,62201144,62211530112,62071241) （62171123,62201144,62211530112,62071241）
国家重点研发计划项目(2023YFC3603600). （2023YFC3603600）
