心音特征的诊断谱分离

V. Zeljkovic, C. Druzgalski, M. Bojic, P. Mayorga, D. Zhang
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引用次数: 1

摘要

全球心血管疾病发病率的上升是初级卫生保健面临的挑战之一,需要进行更广泛的人群筛查,以便进行早期干预。目前的心血管功能评估技术包括广泛的诊断工具,在大多数情况下涉及高、中级成本仪器。其中包括作为核心脏病学的一部分的仪器,心导管插入术,心脏超声,相对基本的多通道或简单通道ECG采集系统等。这些技术和相关仪器是诊断和/或处理特定心脏异常非常有用和强大的工具;然而,它们不适合广泛的人群,低成本的基本补充心脏筛查。因此,这些研究的重点是对心音特征进行结构化评估,因为相关听诊是标准医学检查和全球公认程序的一部分。基于这些原因,我们开发了一个专门的心音评估算法,并在包含565个信号的心音数据库上进行了测试,其中包括202个正常音和363个异常音。广泛多样的心音指标、数据库的多功能性和衍生特征验证了可能的临床有用性和低成本广泛诊断筛查的潜力。特别地,异常心音组包含35种不同的心脏异常样本,分为两组,第一组包含28种不同的心脏异常,所开发的算法成功检测到81.81%,第二组包含7种不同的心脏异常,不表现出S1音的频率变化,未被本文方法分类。97.03%的正常心音病例通过所开发的方法成功分类,该方法解决了在心血管疾病日益流行的情况下选择心脏筛查的需要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Diagnostic spectral segregation of cardiac sounds features
Globally increasing rate of cardiovascular diseases represents one of the primary health care challenges and necessitates broader population screening for earlier intervention. Present cardiovascular function assessment techniques encompass a broad range of diagnostic tools which in most cases involve high and mid level cost instrumentation. They include among others instrumentation as a part of nuclear cardiology, cardiac catheterization, cardiac ultrasound, relatively basic multiple or simple channel ECG acquisition systems, and other. These techniques and related instrumentation represent very useful and powerful tools to diagnose and/or manage particular cardiac abnormalities; however they are not suitable for broader population low cost basic supplemental cardiac screening. Therefore, these studies focused on a structured assessment of cardiac sound traits as related auscultation is a part of standard medical exams and globally accepted procedures. For these reasons a dedicated cardiac sounds assessment algorithm was developed and has been tested on heart sound database that contains 565 signals encompassing 202 normal and 363 abnormal sounds. A broad range of diverse cardiac sound indicators, versatility of database, and derived characteristics validate potency of possible clinical usefulness and potential for a low cost broader diagnostic screening. In particular, the group of abnormal heart sounds contains samples of 35 different heart abnormalities which are divided into two groups including Group I that contains 28 different heart abnormalities that are 81.81% successfully detectable by the developed algorithm and Group II that contains 7 different heart abnormalities that do not exhibit the frequency change of S1 sound and are not classified by the proposed method. Normal heart sounds in 97.03% of cases were successfully classified with the developed method which is addressing the needs for a selected cardiac screening in light of increasing prevalence of cardiovascular diseases.
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