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基于功率谱第三心音特征提取研究
PSD-Based Feature Extraction for the Third Heart Sound Analysis

DOI: 10.12677/JISP.2020.92011, PP. 86-92

Keywords: 功率谱密度函数,第三心音,小波变换,包络线,阈值线,特征提取
Power Spectrum Density
, Third Heart Sound, Wavelet Transform, Envelope Line, Threshold Line, Feature Extraction

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Abstract:

针对第三心音在频域上的复杂特征,本研究采用基于功率谱密度的频率特征提取方法,对第三心音的特征进行评价,并将正常和异常的第三心音进行区分。与本研究相对应的阶段安排如下:1) 首先通过电子听诊器采集心音,并基于小波分解对其进行预处理,消除背景噪声,保留有效信号。2) 人为选择的第三心音生成基于PSD的功率谱。然后,基于功率谱包络提取给定阈值线上的频率特征。3) 通过对正常第三心音和来自二尖瓣回流心脏病患者的异常第三心音对比分析,以阈值–(平均值 ± 标准差) Hz表示的统计结果显示,正常第三心音的特征分布在0.2~(56.0832 ± 6.3373) Hz、0.8~(21.0824 ± 2.2393) Hz,异常S3为0.2~(126.7094 ± 27.0634) Hz,0.8~(64.7820 ± 11.6584) Hz。
In view of the complex characteristics of the third heart sound (S3) in the frequency-domain, this study employs a power spectrum density (PSD)-based frequency features extraction meth-od to evaluate the characteristics of the S3 and to discriminate normal from abnormal S3. The stages corresponding to this study are arranged as follows: 1) The heart sound is firstly collect-ed via an electronic stethoscope and is preprocessed based on wavelet decomposition to elimi-nate the background noise and retain the effective signal. 2) The PSD-based power spectrum is generated for the artificially selected S3. And then, an envelope for the power spectrum is based to extract the frequency features over a given threshold value (Thv) line. 3) By comparatively analyzing the normal S3 and abnormal S3 from the patient with mitral regurgitation heart dis-ease, the statistical results expressed as Thv-Frequency range (mean ± standard deviation) Hz show that the features of normal S3 are distributed in 0.2 - (56.0832 ± 6.3373) Hz, 0.8 - (21.0824 ± 2.2393) Hz, while those of abnormal S3 are in 0.2 - (126.7094 ± 27.0634) Hz, 0.8 - (64.7820 ± 11.6584) Hz.

References

[1]  胡盛寿, 高润霖, 刘力生, 等. 《中国心血管病报告2018》概要[J]. 中国循环杂志, 2019, 34(3): 209-220.
[2]  徐少平. 心瓣膜病患者第三心音的意义[J]. 国外医学心血管疾病分册, 1993(3): 170.
[3]  Tseng, Y.L., Ko, P.Y. and Jaw, F.S. (2012) Detection of the Third and Fourth Heart Sounds Using Hilbert-Huang Transform. BioMedical Engineering OnLine, 11, 8.
https://doi.org/10.1186/1475-925X-11-8
[4]  王鹏巨, 何意亭. 第三心音产生机理和临床意义的研究进展[J]. 心血管病学进展, 1988(3): 25-27.
[5]  杨永玲, 解少柏, 王彦成. 对第三心音的评估[J]. 河北新医药, 1979(1): 38-39.
[6]  邓万俊. 第三心音的病理生理决定因素[J]. 国外医学(内科学分册), 2002(5): 220.
[7]  Minami, Y., Kajimoto, K., Sato, N., et al. (2015) Third Heart Sound in Hospitalised Patients with Acute Heart Failure: Insights from the ATTEND Study. International Journal of Clinical Practice, 69, 820-828.
https://doi.org/10.1111/ijcp.12603
[8]  姚瑶. 基于小波变换的语音信号去噪研究[J]. 信息通信, 2013(2): 13-14.
[9]  刘学, 李婷, 孙宸. 基于小波变换的心音信号去噪方法[J]. 科技信息, 2013(2): 189-190.
[10]  寇俊克, 魏连鑫. 一种改进的小波阈值图像去噪方法[J]. 现代电子技术, 2012, 35(4): 102-104.
[11]  杨勇, 郭兴明. 基于心音信号的生物识别技术研究[J]. 山西警官高等专科学校学报, 2013(2): 90-93.
[12]  刘娅, 李春明, 李栋. 基于小波分析的图像去噪处理[J]. 电脑开发与应用, 2009, 22(9): 33-34.
[13]  余进. 数字信号处理技术的应用与发展[J]. 数字技术与应用, 2015(12): 224.
[14]  Sun, S. (2015) An Innovative Intelligent System Based on Auto-matic Diagnostic Feature Extraction for Diagnosing Heart Diseases. Knowledge-Based Systems, 75, 224-238.
https://doi.org/10.1016/j.knosys.2014.12.001
[15]  Littmann Library, Ventricular Septal Defect Database, 3M Company. http://www.3m.com/healthcare/littmann/pn111.html, 2019-05-25.

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