Multi-channel Heart Sound Feature Characterization Method #br# Based on PCA Serial-merged Fusion

doi:10.3969/j.issn.1006-2475.2019.12.017

Abstract

Abstract: The multi-channel heart sound signal not only covers more general characteristics than the single-channel heart sound signals, but also can make up for the defect that the information carried by the single-channel heart sound data. Using a four-way heart sound sensor, a small four-way heart sound database was established. Based on it, firstly, the characteristics of multi-channel heart sound signals are clarified, and the relationship between heart murmur and auscultation position is discussed. Then, the single-channel and four-channel energy entropy are extracted as effective feature data by using PCA pair. The energy entropy feature is dimension-reduced to obtain serial features. The correlation features and mutual information features are extended from real vector space to complex vector space. Finally, serial parallel features are re-converged into multi-optimal combinations feature. The simulation results show that the feature representation of multivariate optimal combination obtained by multi-channel heart sound signals is better than that of single-channel heart sound signals, which is not only beneficial to the construction of classification models, but also to quickly screen for congenital heart disease and improve classification. The recognition rate has a positive meaning.

Key words: multi-channel heart sound, multi-feature, feature characterization, feature fusion

CLC Number:

TN911

CHENG Yu-han1, ZHANG You-xun2, SUN ke-xue2. Multi-channel Heart Sound Feature Characterization Method #br# Based on PCA Serial-merged Fusion[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2019.12.017.

References

［1］JABLOUN M, RAVIER P, BUTTELLI O, et al. A generating model of realistic synthetic heart sounds for performance assessment of phonocardiogram processing algorithms［J］. Biomedical Signal Processing and Control, 2013,8(5):455-465.
［2］黄际卫,唐宁. 先天性心脏病危险因素和防治的研究进展［J］. 中国儿童保健杂志, 2018,26(3):269-272.
［3］〖JP+2〗LURTON X. Modelling of the sound field radiated by multibeam echosounders for acoustical impact assessment［J］. Applied Acoustics, 2016,101:201-221.
［4］JABBARI S, GHASSEMIAN H. Modeling of heart systolic murmurs based on multivariate matching pursuit for diagnosis of valvular disorders［J］. Computers in Biology and Medicine, 2011,41(9):802-811.
［5］黄国英. 我国开展新生儿先天性心脏病筛查的重要性［J］. 中华儿科杂志, 2017,55(4):241-243.
［6］罗伟奋. 我国学者发现先心病简易筛查法［J］. 中华医学信息导报, 2014,29(9):6.
［7］HIGUCHI K, SATO K, MAKUUCHI H, et al. Automated diagnosis of heart disease in patients with heart murmurs: Application of a neural network technique［J］. Journal of Medical Engineering & Technology, 2006,30(2):61-68.
［8］黄梅,刘洪英,皮喜田,等. 计算机辅助诊断的新型电子听诊器系统［J］. 中国医疗器械杂志, 2017,41(3):161-165.
［9］王续博,张国军,郭楠,等. MEMS电子听诊器微结构设计与验证［J］. 微纳电子技术, 2016,53(1):31-35.
［10］吴云飞,周煜,陈天浩,等. 基于自相关函数的心音周期提取和识别［J］. 计算机与数字工程, 2017,45(10):2068-2073.
［11］周静,杨永明,何为. 心音信号的分析及其特征提取方法的研究［J］. 中国生物医学工程学报, 2005,24(6):685-689.
［12］LIU C Y, GAO R. Multiscale entropy analysis of the differential RR interval time series signal and its application in detecting congestive heart failure［J］. Entropy, 2017,19(6):251-269.
［13］CHENG X F, FENG S, LI Y Y, et al. Research on parallel compressive sensing and application of multi-channel synchronous acquisition of heart sound signals［J］. IEEE Access, 2019,7:30033-30041.
［14］PAYNE R A, SYMEONIDES C N, WEBB D J, et al. Pulse transit time measured from the ECG: An unreliable marker of beat-to-beat blood pressure［J］. Journal of Applied Physiology, 2006,100(1):136-141.
［15］成雨含,张学军,黄丽亚,等. 一种4通道心音同步定位采集装置: CN201510179382.3［P］. 2018-03-13.
［16］成谢锋,张正. 一种双正交心音小波的构造方法［J］. 物理学报, 2013,62(16):446-457.
［17］SUN S P, WANG H B, JIANG Z W, et al. Segmentation-based heart sound feature extraction combined with classifier models for a VSD diagnosis system［J］. Expert Systems with Applications, 2014,41(4):1769-1780.
［18］杨健,杨静宇,王正群,等. 一种组合特征抽取的新方法［J］. 计算机学报, 2002,25(6):570-575.
［19］成谢锋,杨贺. 5种小波在心音信号处理中的分析与比较［J］. 南京邮电大学学报(自然科学版), 2015,35(1):38-46.
［20］MOURA M D C, ZIO E, LINS I D, et al. Failure and reliability prediction by support vector machines regression of time series data［J］. Reliability Engineering & System Safety, 2011,96(11):1527-1534.
［21］安玲玲,于雷. 基于高斯混合模型的心音信号识别［J］. 吉林大学学报(理学版), 2016,54(5):1096-1102.
［22］WHITAKER B M, SURESHA P B, LIU C, et al. Combining sparse coding and time-domain features for heart sound classification［J］. Physiological Measurement, 2017,38(8):1701-1729.
［23］蔡胜胜,卜凡亮. 基于案例推理和规则推理的公安突发事件辅助决策算法［J］. 计算机与现代化, 2019(9):7-11.
［24］BOUTANA D, BENIDIR M, BARKAT B. Segmentation and identification of some pathological phonocardiogram signals using time-frequency analysis［J］. IET Signal Processing, 2011,5(6):527-537.

[1]	NING Juan, ZHOU Qing-hua, ZENG Xiao-wei. Application of Improved YOLOv7 Algorithm in Detection of Capping Defects of Vials [J]. Computer and Modernization, 2023, 0(12): 82-86.
[2]	CHEN Jun-yi. Health Event Prediction Model Based on Dynamic and Static Features of Graph Nodes [J]. Computer and Modernization, 2023, 0(10): 39-44.
[3]	XING Shi-shuai, LIU Dan-feng, WANG Li-guo, PAN Yue-tao, MENG Ling-hong, YUE Xiao-han. Image Super-resolution Reconstruction Based on Spatial Attention Residual Network [J]. Computer and Modernization, 2023, 0(10): 45-52.
[4]	CHEN Jia-min, ZHANG Bo-quan, MAI Hai-peng. Hippocampus Segmentation Based on Feature Fusion [J]. Computer and Modernization, 2023, 0(08): 1-6.
[5]	WANG Hong, GE Hong. Cross Modal Hash Retrieval Based on Attention Mechanism and Semantic Similarity [J]. Computer and Modernization, 2023, 0(08): 44-53.
[6]	WANG Jie, PAN Feng, ZHANG Yan-sha, TAN Mian, YAN Xiao-bo, WANG Lin, . Surface Defect Classification of Aluminum Profiles with Weighted Non-local Modules [J]. Computer and Modernization, 2023, 0(05): 86-92.
[7]	ZHU Li-qing, LI Xiang, . Vehicle Detection of Remote Sensing Images Based on Improved YOLOv5 Algorithm [J]. Computer and Modernization, 2023, 0(05): 117-121.
[8]	WANG Yong-di, LEI Gang. Chinese Short Text Entity Disambiguation Based on Multi-feature Factor Fusion [J]. Computer and Modernization, 2023, 0(01): 30-36.
[9]	ZHANG Xiao-dong, WANG Xu-ying, QIN Zi-xuan. Pump Detection Period Predicting of Pump Well Based on Feature Fusion [J]. Computer and Modernization, 2022, 0(12): 60-66.
[10]	WANG Chang-jun, PENG Cheng, LI Yong. Multi-feature Fusion Ship Target Detection Algorithm in Complex Environment [J]. Computer and Modernization, 2022, 0(11): 81-88.
[11]	LIU Li-ting, OU Yu-yi. DGA Domain Name Detection Combining Attention Mechanisms and Parallel Hybrid Network [J]. Computer and Modernization, 2022, 0(09): 119-126.
[12]	ZENG Yi-bin, GE Hong. Cross-modal Retrieval Based on Context Fusion and Multi-similarity Learning [J]. Computer and Modernization, 2022, 0(08): 50-56.
[13]	LI Yan, LU Zheng-song, LI Qing-yun , YANG Shi-hai, ZHANG Xiao-long. Mask Detection Algorithm Based on Lightweight Structure and Re-parameterized Network [J]. Computer and Modernization, 2022, 0(07): 40-46.
[14]	LIANG Zheng-you, GENG Jing-bang, SUN Yu. Traffic Sign Recognition Algorithm Based on Improved Residual Network [J]. Computer and Modernization, 2022, 0(04): 52-57.
[15]	SONG Shuang, LU Xin-da. Text Matching Model Based on BERT and Self-attention Mechanism of Image [J]. Computer and Modernization, 2021, 0(11): 12-16.