多点听诊在心音诊断系统中的应用

Hadrina Sh-Hussain, S. Salleh, A. K. Ariff, Osama Alhamdani, T. T. Swee, A. M. Noor, H. Oemar, Khalid Yusoff
{"title":"多点听诊在心音诊断系统中的应用","authors":"Hadrina Sh-Hussain, S. Salleh, A. K. Ariff, Osama Alhamdani, T. T. Swee, A. M. Noor, H. Oemar, Khalid Yusoff","doi":"10.1109/ISSPA.2012.6310669","DOIUrl":null,"url":null,"abstract":"Humans are different in many ways: fat or thin, young or old, sick or healthy; they may differ in auscultation sites which may vary according to the patient's anatomy. Emphasis must be placed on the characteristics of heart sound based on its intensity which greatly depends on the location of the stethoscope to its pericardium. Each one of these areas will emphasize certain characteristics components of the heart sound. Grouping of the first heart sound (lub) is called the S1 features while the second heart sound (dub) is called the S2 features, the systolic or diastolic features are important factor to determine the types of murmurs. To this end, studies have been limited to reflect on the development and evaluation methods in order to detect the various components constituting signal of the heart sound at one specific auscultation point. The principle area of interest in this paper is, however placing the stethoscope at the semi lunar valve called aortic as position one and pulmonary as position two which will provide better quality of the S2 sound. The S1 heart sound can be heard more clearly in the atroventricle (AV) where the mitral valve as position three and tricuspid valve as position four. Comparative experiments with respect to MFCC feature, different number of HMM states and different number of gaussian mixtures were investigated to measure the influence of these factors on the classification performance at the four locations of auscultation of the heart sound. Interestingly, a five-state model outperformed the four-state model which was supposed to model the four basic components of the heart sounds. It can be said, a five-state average over all Gaussian mixtures model and at the four locations provide the best overall performance of 90.1% accuracy.","PeriodicalId":248763,"journal":{"name":"2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Application of multipoint auscultation for heart sound diagnostic system (MAHDS)\",\"authors\":\"Hadrina Sh-Hussain, S. Salleh, A. K. Ariff, Osama Alhamdani, T. T. Swee, A. M. Noor, H. Oemar, Khalid Yusoff\",\"doi\":\"10.1109/ISSPA.2012.6310669\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Humans are different in many ways: fat or thin, young or old, sick or healthy; they may differ in auscultation sites which may vary according to the patient's anatomy. Emphasis must be placed on the characteristics of heart sound based on its intensity which greatly depends on the location of the stethoscope to its pericardium. Each one of these areas will emphasize certain characteristics components of the heart sound. Grouping of the first heart sound (lub) is called the S1 features while the second heart sound (dub) is called the S2 features, the systolic or diastolic features are important factor to determine the types of murmurs. To this end, studies have been limited to reflect on the development and evaluation methods in order to detect the various components constituting signal of the heart sound at one specific auscultation point. The principle area of interest in this paper is, however placing the stethoscope at the semi lunar valve called aortic as position one and pulmonary as position two which will provide better quality of the S2 sound. The S1 heart sound can be heard more clearly in the atroventricle (AV) where the mitral valve as position three and tricuspid valve as position four. Comparative experiments with respect to MFCC feature, different number of HMM states and different number of gaussian mixtures were investigated to measure the influence of these factors on the classification performance at the four locations of auscultation of the heart sound. Interestingly, a five-state model outperformed the four-state model which was supposed to model the four basic components of the heart sounds. It can be said, a five-state average over all Gaussian mixtures model and at the four locations provide the best overall performance of 90.1% accuracy.\",\"PeriodicalId\":248763,\"journal\":{\"name\":\"2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)\",\"volume\":\"57 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-07-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSPA.2012.6310669\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSPA.2012.6310669","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

摘要

人类在很多方面都是不同的:胖或瘦,年轻或年老,生病或健康;听诊部位可能因患者的解剖结构而异。重点必须放在基于其强度的心音特征上,这在很大程度上取决于听诊器对心包的位置。这些区域中的每一个都会强调心音的某些特征组成部分。第一心音(lub)组称为S1特征,第二心音(dub)组称为S2特征,其中收缩期或舒张期特征是确定杂音类型的重要因素。为此,研究仅限于反思在某一听诊点检测构成心音信号的各种成分的开发和评价方法。然而,本文感兴趣的主要领域是,将听诊器放置在半月瓣(称为主动脉瓣的位置1和肺动脉瓣的位置2)上,这样可以提供更好的S2声音质量。在心室(AV)可以更清楚地听到S1心音,其中二尖瓣位于第3位,三尖瓣位于第4位。通过MFCC特征、不同数目的HMM状态和不同数目的高斯混合的对比实验,考察这些因素对心音听诊4个位置分类性能的影响。有趣的是,五状态模型比四状态模型表现得更好,四状态模型被认为是对心音的四个基本组成部分进行建模。可以说,在所有高斯混合模型和四个位置上的五状态平均值提供了90.1%的最佳整体性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Application of multipoint auscultation for heart sound diagnostic system (MAHDS)
Humans are different in many ways: fat or thin, young or old, sick or healthy; they may differ in auscultation sites which may vary according to the patient's anatomy. Emphasis must be placed on the characteristics of heart sound based on its intensity which greatly depends on the location of the stethoscope to its pericardium. Each one of these areas will emphasize certain characteristics components of the heart sound. Grouping of the first heart sound (lub) is called the S1 features while the second heart sound (dub) is called the S2 features, the systolic or diastolic features are important factor to determine the types of murmurs. To this end, studies have been limited to reflect on the development and evaluation methods in order to detect the various components constituting signal of the heart sound at one specific auscultation point. The principle area of interest in this paper is, however placing the stethoscope at the semi lunar valve called aortic as position one and pulmonary as position two which will provide better quality of the S2 sound. The S1 heart sound can be heard more clearly in the atroventricle (AV) where the mitral valve as position three and tricuspid valve as position four. Comparative experiments with respect to MFCC feature, different number of HMM states and different number of gaussian mixtures were investigated to measure the influence of these factors on the classification performance at the four locations of auscultation of the heart sound. Interestingly, a five-state model outperformed the four-state model which was supposed to model the four basic components of the heart sounds. It can be said, a five-state average over all Gaussian mixtures model and at the four locations provide the best overall performance of 90.1% accuracy.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Online mvbf adaptation under diffuse noise environments with mimo based noise pre-filtering Hierarchical scheme for Arabic text recognition Precoder selection and rank adaptation in MIMO-OFDM Head detection using Kinect camera and its application to fall detection Wavelength and code division multiplexing toward diffuse optical imaging
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1