Suppr超能文献

利用希尔伯特-黄变换检测第三和第四心音。

Detection of the third and fourth heart sounds using Hilbert-Huang transform.

机构信息

Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan.

出版信息

Biomed Eng Online. 2012 Feb 14;11:8. doi: 10.1186/1475-925X-11-8.

Abstract

BACKGROUND

The third and fourth heart sound (S3 and S4) are two abnormal heart sound components which are proved to be indicators of heart failure during diastolic period. The combination of using diastolic heart sounds with the standard ECG as a measurement of ventricular dysfunction may improve the noninvasive diagnosis and early detection of myocardial ischemia.

METHODS

In this paper, an adaptive method based on time-frequency analysis is proposed to detect the presence of S3 and S4. Heart sound signals during diastolic periods were analyzed with Hilbert-Huang Transform (HHT). A discrete plot of maximal instantaneous frequency and its amplitude was generated and clustered. S3 and S4 were recognized by the clustered points, and performance of the method was further enhanced by period definition and iteration tracking.

RESULTS

Using the proposed method, S3 and S4 could be detected adaptively in a same method. 90.3% of heart sound cycles with S3 were detected using our method, 9.6% were missed, and 9.6% were false positive. 94% of S4 were detected using our method, 5.5% were missed, and 16% were false positive.

CONCLUSIONS

The proposed method is adaptive for detecting low-amplitude and low-frequency S3 and S4 simultaneously compared with previous detection methods, which would be practical in primary care.

摘要

背景

第三和第四心音(S3 和 S4)是两种异常心音成分,已被证明是舒张期心力衰竭的指标。将舒张期心音与标准心电图结合起来作为心室功能障碍的测量指标,可能会提高心肌缺血的无创诊断和早期检测。

方法

本文提出了一种基于时频分析的自适应方法来检测 S3 和 S4 的存在。使用希尔伯特-黄变换(HHT)对舒张期的心音信号进行分析。生成并聚类最大瞬时频率及其幅度的离散图。通过周期定义和迭代跟踪,通过聚类点识别 S3 和 S4,并进一步提高方法的性能。

结果

使用所提出的方法,可以自适应地检测相同方法中的 S3 和 S4。我们的方法检测到 90.3%的具有 S3 的心音周期,9.6%被错过,9.6%为假阳性。我们的方法检测到 94%的 S4,5.5%被错过,16%为假阳性。

结论

与以前的检测方法相比,所提出的方法对检测低频和低振幅的 S3 和 S4 具有适应性,在初级保健中具有实际意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/437a/3305384/92ccdae045b1/1475-925X-11-8-1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验