控制理论与应用2024，Vol.41Issue(8)：1451-1458,8.DOI:10.7641/CTA.2023.20486
基于多频段多任务编解码模型的心电图基准点联合检测
Joint detection of ECG fiducial points based on multi-band and multi-task encoding and decoding model
摘要
Abstract
Fiducial point detection is the basis of the electrocardiogram(ECG)diagnostic analysis.However,the ECG has waveform variability and is often disturbed by various artifacts and noises,limiting the detection accuracies of fiducial points.This paper first builds a probability graph model to analyze the inference relationships between different band ECG components and fiducial point detection tasks.Then,we propose a multi-band multi-task encoding-decoding network inspired by this probability graph model.The network first performs 1-D convolutions on each ECG component to extract features,then learns the attention masks to resist noise through temporal convolutional modules,and finally adopts the dependent multi-branch structure to realize the joint detection of ECG fiducial points.The experimental results with five-fold cross-validation on the MIT-BIH QT and LUDB databases show that the proposed method can effectively improve the detection accuracy of ECG fiducial points,comparable to the state-of-the-art level.关键词
心电图基准点检测/编解码模型/时域卷积神经网络Key words
ECG fiducial point detection/encoding-decoding model/temporal convolutional network引用本文复制引用
李磊,廖桂鑫,蔡瑞涵,李珍妮,吕俊..基于多频段多任务编解码模型的心电图基准点联合检测[J].控制理论与应用,2024,41(8):1451-1458,8.基金项目
国家自然科学基金项目(62073086,62273106),广东省自然科学基金项目(2022A1515011445)资助.Supported by the National Natural Science Foundation of China(62073086,62273106)and the Natural Science Foundation of Guangdong Province(2022A1515011445). （62073086,62273106）
