[1]易民升,冷虹霞,刘星,等.一种基于频谱包络特征提取的心音信号分类方法[J].中国医学物理学杂志,2026,43(7):958-964.[doi:DOI:10.3969/j.issn.1005-202X.2026.07.017]
　YI Minsheng,LENG Hongxia,et al.Heart sound signal classification method based on spectral envelope feature extraction[J].Chinese Journal of Medical Physics,2026,43(7):958-964.[doi:DOI:10.3969/j.issn.1005-202X.2026.07.017]
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一种基于频谱包络特征提取的心音信号分类方法()

《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:
958-964
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:
Heart sound signal classification method based on spectral envelope feature extraction
文章编号:
1005-202X（2026）07-0958-07
作者:
易民升1; 2; 冷虹霞2; 刘星2; 房玉2
1.邵阳职业技术学院， 湖南 邵阳 422000； 2.西华大学电气与电子信息学院， 四川 成都 610039
Author(s):
YI Minsheng1;  2;  LENG Hongxia2;  LIU Xing2;  FANG Yu2
1. Shaoyang Polytechnic, Shaoyang 422000, China 2. School of Electrical and Electronic Information, Xihua University, Chengdu 610039, China
关键词:
心音分类; 功率谱强度; 特征提取; 心脏瓣膜疾病; 频域包络
Keywords:
Keywords: heart sound classification power spectrum intensity feature extraction heart valve disease frequency domain envelope
分类号:
心音分类；功率谱强度；特征提取；心脏瓣膜疾病；频域包络
DOI:
DOI:10.3969/j.issn.1005-202X.2026.07.017
文献标志码:
A
摘要:
针对心血管疾病易诱发心脏结构、功能异常，造成心肌收缩与血液泵血能力衰减、严重危及患者身心健康的问题，提出一种基于频率平衡功率谱强度特征的心音分析方法，可实现正常心音与病理性心音信号的有效区分。首先采用自适应小波阈值收缩降噪算法对心音信号进行预处理，提取其频率平衡功率谱强度（FBPSI）包络。再提取FBPSI包络的多维特征，融合功率谱密度、能量谱密度提取的特征，通过MRMR算法对特征进行重要性分数排序。最后应用机器学习模型对心音信号进行分类识别。实验采用Yaseen以及2016心音挑战赛（PhysioNet/CinC Challenge 2016）两个公开数据集进行验证。十折交叉验证结果表明，本研究提出的有效特征向量与K近邻分类器相结合在Yaseen数据集上5分类准确率为97.40%，在PhysioNet/CinC Challenge 2016数据集中正常/异常分类准确率为89.11%，可为心血管疾病的预防和诊断提供有效参考。
Abstract:
Abstract: Cardiovascular diseases can predispose the heart to structural and functional abnormalities, leading to weakened myocardial contractility and reduced blood-pumping capacity, which severely jeopardizes patients physical and psychological health. Therefore, this study proposes a heart sound analysis method based on frequency-balanced power spectrum intensity (FBPSI) features to enable effective differentiation of normal and pathological heart sound signals. An adaptive wavelet threshold shrinkage denoising algorithm is first employed to preprocess heart sound signals and extract FBPSI envelopes. Subsequently, multi-dimensional features are extracted from the FBPSI envelopes, which are then fused with features derived from power spectral density and energy spectral density. The importance of these features is ranked using the minimum redundancy maximum relevance algorithm. Finally, machine learning models are applied for the classification and identification of heart sound signals. Experimental validation is conducted using the Yaseen and the PhysioNet/CinC Challenge 2016 public datasets. Ten-fold cross-validation results demonstrate that the proposed effective feature vectors combined with the K-nearest neighbor classifier achieves a five-class classification accuracy of 97.40% on the Yaseen dataset and a binary classification accuracy of 89.11% on the PhysioNet/CinC Challenge 2016 dataset, providing valuable references for the prevention and diagnosis of cardiovascular diseases.

相似文献/References:

[1]马晶,蔡文杰,杨利.基于机器学习的心音识别分类研究[J].中国医学物理学杂志,2021,38(1):75.[doi:DOI:10.3969/j.issn.1005-202X.2021.01.013]
　MA Jing,CAI Wenjie,YANG Li.Research on heart sounds classification based on machine learning[J].Chinese Journal of Medical Physics,2021,38(7):75.[doi:DOI:10.3969/j.issn.1005-202X.2021.01.013]
[2]任灵,黄玉丹,陈颖.基于交叉对比神经网络的心音分类[J].中国医学物理学杂志,2021,38(10):1251.[doi:DOI:10.3969/j.issn.1005-202X.2021.10.012]
　REN Ling,HUANG Yudan,CHEN Ying.Heart sound classification based on cross-contrast neural network[J].Chinese Journal of Medical Physics,2021,38(7):1251.[doi:DOI:10.3969/j.issn.1005-202X.2021.10.012]
[3]冯帅,刘飞飞,伍昕宇,等.基于最小均方误差对数谱幅度估计的心音降噪算法[J].中国医学物理学杂志,2023,40(11):1370.[doi:DOI:10.3969/j.issn.1005-202X.2023.11.009]
　FENG Shuai,LIU Feifei,WU Xinyu,et al.Heart sound denoising algorithm based on minimum mean-square error log-spectral amplitude estimation[J].Chinese Journal of Medical Physics,2023,40(7):1370.[doi:DOI:10.3969/j.issn.1005-202X.2023.11.009]

备注/Memo

备注/Memo:
【收稿日期】2026-01-08 【基金项目】国家自然科学青年基金（61901393）；湖南省教育厅科学研究项目（24C1214） 【作者简介】易民升，硕士，副教授，研究方向：信号处理、电气工程，E-mail: yms_xhu@126.com 【通信作者】房玉，博士，副教授，研究方向：智能信息处理、生物医学信息工程，E-mail: Yfang_123@163.com
更新日期/Last Update: 2026-07-23