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小波和频谱分析正常和异常心音诊断心脏疾病。

Wavelet and Spectral Analysis of Normal and Abnormal Heart Sound for Diagnosing Cardiac Disorders.

机构信息

Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh.

Department of Electrical and Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh.

出版信息

Biomed Res Int. 2022 Jul 27;2022:9092346. doi: 10.1155/2022/9092346. eCollection 2022.

DOI:10.1155/2022/9092346
PMID:35937404
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9348924/
Abstract

Body auscultation is a frequent clinical diagnostic procedure used to diagnose heart problems. The key advantage of this clinical method is that it provides a cheap and effective solution that enables medical professionals to interpret heart sounds for the diagnosis of cardiac diseases. Signal processing can quantify the distribution of amplitude and frequency content for diagnostic purposes. In this experiment, the use of signal processing and wavelet analysis in screening cardiac disorders provided enough evidence to distinguish between the heart sounds of a healthy and unhealthy heart. Real-time data was collected using an IoT device, and the noise was reduced using the REES52 sensor. It was found that mean frequency is sufficiently discriminatory to distinguish between a healthy and unhealthy heart, according to features derived from signal amplitude distribution in the time and frequency domain analysis. The results of the present study indicate the adequate discrimination between the characteristics of heart sounds for automatic detection of cardiac problems by signal processing from normal and abnormal heart sounds.

摘要

中文译文：

体听诊是一种常用于诊断心脏问题的临床诊断程序。这种临床方法的主要优势在于它提供了一种廉价有效的解决方案，使医疗专业人员能够解释心脏声音，以诊断心脏病。信号处理可以量化幅度和频率内容的分布，用于诊断目的。在这个实验中，信号处理和小波分析在筛选心脏疾病中的应用提供了足够的证据，可以区分健康和不健康心脏的心脏声音。使用物联网设备实时收集数据，并使用 REES52 传感器降低噪声。根据信号幅度在时域和频域分析中的分布得出的特征，发现平均频率足以区分健康和不健康的心脏。本研究的结果表明，通过对正常和异常心音的信号处理，可以对心音特征进行充分的区分，从而自动检测心脏问题。

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