[1]邓漆时超,吴佳成,俞键,等.深度学习心音信号分类研究进展[J].中国医学物理学杂志,2026,43(7):946-957.[doi:DOI:10.3969/j.issn.1005-202X.2026.07.016]
　DENG Qishichao,WU Jiacheng,et al.Advances in heart sound classification based on deep learning[J].Chinese Journal of Medical Physics,2026,43(7):946-957.[doi:DOI:10.3969/j.issn.1005-202X.2026.07.016]
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深度学习心音信号分类研究进展()

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
43卷
期数:
2026年第7期
页码:
946-957
栏目:
医学信号处理与医学仪器
出版日期:
2026-07-22

文章信息/Info

Title:
Advances in heart sound classification based on deep learning
文章编号:
1005-202X（2026）07-0946-12
作者:
邓漆时超1; 2; 吴佳成1; 2; 俞键1; 2; 张华2; 李川涛3; 林冬梅4; 陈扶明2
1.甘肃中医药大学医学信息工程学院， 甘肃 兰州 730000； 2.中国人民解放军联勤保障部队第九四〇医院医疗保障中心， 甘肃 兰州 730050；3.海军军医大学（第二军医大学）海军特色医学中心， 上海 200433；4.兰州理工大学电气与信息工程学院， 甘肃 兰州 730050
Author(s):
DENG Qishichao1;  2;  WU Jiacheng1;  2;  YU Jian1;  2;  ZHANG Hua2;  LI Chuantao3;  LIN Dongmei4;  CHEN Fuming2
1. School of Medical Information Engineering, Gansu University of Chinese Medicine, Lanzhou 730000, China 2. Medical Support Center, 940th Hospital of Joint Logistics Support Force of Chinese Peoples Liberation Army, Lanzhou 730050, China 3. Naval Characteristic Medical Center, Naval Medical University (Second Military Medical University), Shanghai 200433, China 4. School of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
关键词:
心音信号; 深度学习; 分类算法; 神经网络; 综述
Keywords:
Keywords: heart sound signal deep learning classification algorithm neural network review
分类号:
R318；TP183
DOI:
DOI:10.3969/j.issn.1005-202X.2026.07.016
文献标志码:
A
摘要:
心音是反映心脏健康状况的重要生理信号，心音分类可辅助心脏疾病的早期诊断。相比依赖人工特征提取的传统方法，深度学习能够自动学习心音特征，在分类精度和鲁棒性方面具有明显优势。本文通过系统检索国内外相关文献，围绕深度学习在心音分类领域的研究展开综述。首先对心音信号的特点、常用数据集及预处理方法进行梳理，其次归纳卷积神经网络、循环神经网络、Transformer及多模型融合等深度学习方法在心音分类任务中的应用现状，并进一步从模型结构、分类性能和临床适用性等方面进行比较与分析。最后，讨论深度学习在心音分类任务中的应用前景，以期促进深度学习方法在心音信号分类中的应用。
Abstract:
Abstract: Heart sounds are vital physiological signals that reflect cardiac status, and heart sound classification plays a crucial role in the early diagnosis of cardiovascular diseases. Compared with traditional methods that rely on handcrafted feature extraction, deep learning techniques can automatically learn representative features from heart sound signals, achieving superior classification accuracy and robustness. This study provides an overview of recent advances in deep learning-based heart sound classification by retrieving and analyzing relevant domestic and international research. This review systematical introduces the characteristics of heart sound signals, commonly-used datasets, and preprocessing methods, summarizes the application status of deep learning approaches, including convolutional neural networks, recurrent neural networks, Transformer-based models, and hybrid architectures, in heart sound classification, and further compares their model architectures, classification performance, and clinical applicability. Finally, the application prospects of deep learning in heart sound classification is discussed, aiming to promote the application of deep learning techniques in heart sound classification.

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备注/Memo

备注/Memo:
【收稿日期】2026-03-20 【基金项目】国家自然科学基金（61901515, 62361038）；兰州市科技发展指导性计划（2025-5-243）；兰州市青年科技人才创新项目（2025-QN-145） 【作者简介】邓漆时超，硕士研究生，研究方向：生物医学信号检测与处理，E-mail: 1095292703@qq.com 【通信作者】陈扶明，正高级工程师，研究方向：生物医学信号检测与处理，E-mail: cfm5762@126.com
更新日期/Last Update: 2026-07-23