生物医学工程研究2025，Vol.44Issue(5)：288-296,9.DOI:10.19529/j.cnki.1672-6278.2025.05.03
基于渐进式分层提取的多任务心脏杂音检测
Multi-task heart murmur detection based on progressive layered extraction
摘要
Abstract
To address the problem that most of the existing murmur detection methods are limited to a single task and fail to fully u-tilize the correlation between heart murmur signals,we proposed a multi-task heart murmur model based on progressive layered extrac-tion(PLE),which could simultaneously complete the detection of small-segment heart murmurs and the automatic segmentation of heart sound signals.The model could effectively integrate the spatiotemporal information of heart sound signals by sharing the underlying features,thereby improving the accuracy of murmur detection and heart sound segmentation.On the CirCor Digiscope dataset 2022,the average recall rate and average F1 score of the model for small segment murmur detection reached 0.8033 and 0.6096,respectively,and the average recall rate and average F1 score for heart sound segmntation reached 0.9160 and 0.9100,respectively.The results showed that this model could provide new method and idea for the detection and analysis of heart murmur,offer potential technical sup-port for the early screening and diagnosis of cardiovascular diseases.关键词
心脏杂音/深度学习/小片段杂音检测/多任务学习/渐进式分层提取Key words
Heart murmurs/Deep learning/Small-segment murmur detection/Multi-task learning/Progressive layered extrac-tion分类
医药卫生引用本文复制引用
李虎浩,李世龙,首都,李莉,赵启军,潘帆..基于渐进式分层提取的多任务心脏杂音检测[J].生物医学工程研究,2025,44(5):288-296,9.基金项目
国家自然科学基金项目(62066042) （62066042）
四川省重点研发项目(2024YFFK0051). （2024YFFK0051）
