基于OMAP的心音分析仪研究与设计
摘要
近些年以来,随着生活环境的恶化和工作压力加大,心血管疾病已经成为严重危害人类健康的疾病之一。心音信号是检测心血管疾病检测的一个重要手段,目前心音信号的检测一般通过听诊器,凭借医疗工作者的经验进行判断,这种方式具有很大的主观性和局限性。为了能够快速、客观的检测心音信号,本文设计了基于开放式多媒体应用平台(Open Multimedia Applications Platform,简称OMAP)的心音分析仪。
本文首先介绍了心音信号的特性和基于心音检测心血管疾病技术的发展基本情况,然后根据心音信号目前的发展状况和趋势提出了基于OMAP的心音分析仪的思想;本文在对心音分析仪的关键技术进行了研究的基础上,确定了基于OMAP3530的心音分析仪的硬件设计方案和软件设计方案。
在心音信号的算法处理上,经过仔细对比当前的心音信号的处理技术,提出采用自相关系数的方法对心音信号分段,以心音信号的小波包能量熵为特征,采用DTW分类算法对心音信号进行分类。
心音分析仪的硬件设计采用核心板加底板的设计方案,这样方便升级核心芯片和扩展功能。为了方便软件的开发和系统的升级,方便管理OMAP3530内存和硬件资源,选取了开源的Linux操作系统,并根据心音分析仪的需求,对Linux内核裁剪和移植。心音分析仪的软件开发基于Nokia公司Qt图形界面集成开发工具,采用分层设计的思想,为心音分析仪预留了升级的接口,方便心音分析仪的调试和升级。论文在最后采用Ti公司的C6runapp编译工具对心音分析仪的软件进行了优化,并对心音分析仪的功能做了测试。
本文基于OMAP平台实现了心音分析仪的设计,为心音信号的自动化处理提供了一种新平台,为心音信号用于心血管疾病领域的辅助诊断奠定了基础。
Recently, with the deterioration of living conditions and increased work pressure, cardiovascular disease has become one of the serious diseases that do harm to human health. Heart sound is an important measure for cardiovascular disease detection, however, heart sound signal detection generally through a stethoscope currently, which depends on the judgment of medical workers’experience. This method may have lots of subjectivity. In order to detect cardiovascular diseases quickly and objectively, we designed a heart sound analyzer based on OMAP (Open Multimedia Applications Platform) in this paper.
This paper introduces the characteristics of heart sounds and the development of heart sounds detection of cardiovascular diseases technology firstly. Subsequently, we studied the key technologies of heart sound analyzer, and then we proposed the design idea of heart sound analyzer based on OMAP hardware.
In heart sound processing, we carefully compared the current heart sound signal segmentation technology, and then adopted the correlation coefficients of the heart sound for heart sound segmentation. The wavelet packet energy entropy was extracted as feature of heart sound signal, and DTW was used for heart sound classification.
In order to upgrade the core chip and extend functions of heart sound analyzer, we employed the core board and bottom board design scheme. In order to develop software, upgrade systems conveniently, manage memory and hardware resources of OMAP3530, we selected the open source Linux operating system in our project. Linux kernel has been cut and transplanted according to the demand of heart sound analyzer. We adopted Nokia Qt GUI development tools for the design of heart sound analyzer software. In order to upgrade and debug the software easily, we took the hierarchical design ideas, and reserved the interface to upgrade the program.
Finally, we optimized the heart sound analyzer software by the use of Ti's C6runapp compiler tools, and tested the function of heart sounds analyzer, and achieved the goals of this heart sound analyzer system.
In this paper, we realized the design of heart sound analyzer based on OMAP, which provides a new platform for the automated processing of heart sounds and plays a basis role for the assistant diagnosis of cardiovascular disease.
本文首先介绍了心音信号的特性和基于心音检测心血管疾病技术的发展基本情况,然后根据心音信号目前的发展状况和趋势提出了基于OMAP的心音分析仪的思想;本文在对心音分析仪的关键技术进行了研究的基础上,确定了基于OMAP3530的心音分析仪的硬件设计方案和软件设计方案。
在心音信号的算法处理上,经过仔细对比当前的心音信号的处理技术,提出采用自相关系数的方法对心音信号分段,以心音信号的小波包能量熵为特征,采用DTW分类算法对心音信号进行分类。
心音分析仪的硬件设计采用核心板加底板的设计方案,这样方便升级核心芯片和扩展功能。为了方便软件的开发和系统的升级,方便管理OMAP3530内存和硬件资源,选取了开源的Linux操作系统,并根据心音分析仪的需求,对Linux内核裁剪和移植。心音分析仪的软件开发基于Nokia公司Qt图形界面集成开发工具,采用分层设计的思想,为心音分析仪预留了升级的接口,方便心音分析仪的调试和升级。论文在最后采用Ti公司的C6runapp编译工具对心音分析仪的软件进行了优化,并对心音分析仪的功能做了测试。
本文基于OMAP平台实现了心音分析仪的设计,为心音信号的自动化处理提供了一种新平台,为心音信号用于心血管疾病领域的辅助诊断奠定了基础。
Recently, with the deterioration of living conditions and increased work pressure, cardiovascular disease has become one of the serious diseases that do harm to human health. Heart sound is an important measure for cardiovascular disease detection, however, heart sound signal detection generally through a stethoscope currently, which depends on the judgment of medical workers’experience. This method may have lots of subjectivity. In order to detect cardiovascular diseases quickly and objectively, we designed a heart sound analyzer based on OMAP (Open Multimedia Applications Platform) in this paper.
This paper introduces the characteristics of heart sounds and the development of heart sounds detection of cardiovascular diseases technology firstly. Subsequently, we studied the key technologies of heart sound analyzer, and then we proposed the design idea of heart sound analyzer based on OMAP hardware.
In heart sound processing, we carefully compared the current heart sound signal segmentation technology, and then adopted the correlation coefficients of the heart sound for heart sound segmentation. The wavelet packet energy entropy was extracted as feature of heart sound signal, and DTW was used for heart sound classification.
In order to upgrade the core chip and extend functions of heart sound analyzer, we employed the core board and bottom board design scheme. In order to develop software, upgrade systems conveniently, manage memory and hardware resources of OMAP3530, we selected the open source Linux operating system in our project. Linux kernel has been cut and transplanted according to the demand of heart sound analyzer. We adopted Nokia Qt GUI development tools for the design of heart sound analyzer software. In order to upgrade and debug the software easily, we took the hierarchical design ideas, and reserved the interface to upgrade the program.
Finally, we optimized the heart sound analyzer software by the use of Ti's C6runapp compiler tools, and tested the function of heart sounds analyzer, and achieved the goals of this heart sound analyzer system.
In this paper, we realized the design of heart sound analyzer based on OMAP, which provides a new platform for the automated processing of heart sounds and plays a basis role for the assistant diagnosis of cardiovascular disease.
引文
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[3]董承良,陶寿琪,陈灏珠.实用心脏病学[M].上海科技出版社, 1994.
[4]徐晓飞.心音信号分析和识别系统的开发[D].山东大学,生物医学工程专业,2008.
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[6] C. Ahlstrom, T. Lanne, P. Ask, A. Johansson. A method for accurate localization of the first heart sound and possible applications[J]. Physiological Measurement, 2008, 29(3):117‐122.
[7] Lehner RJ, Rangayyan RM. A three‐channel microcomputer system for segmentation and characterization of the phonocardiogram[J]. IEEE Transactions on Biomedical Engineering, 1987, 34(6): 485‐489.
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[9]赵治栋,赵知劲,张嵩,陈裕泉.心音自动分段研究[J].航天医学与医学工程, 2004, 17(6):452‐456.
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[12]李桥,赵玲,邵庆余,朱兴雷,张玉华,梦延,刘毅,李新,周洪军.应用小波变换进行心音三维时频分析的研究[J].中国医学物理学杂志,2001,18 (2):110‐112.
[13] Bentley P M, Donnel J, etc. Classification of native hearts valve sounds using the Choi., Wliams time frequency distribution[J]. Proe IEEE EMBS16th Annual International Conference.1995(2):1186—1188.
[14]郭兴明,林辉杰,肖守中.复杂度在心音信号分析中的应用[J].仪器仪表学报, 2010, 31(2):259‐263.
[15]卢耘,粟载福.心音信号的分形研究[J].中国生物学医学工程学报, 1993, 12[1]:50‐55.
[16]贾丽会,张修如.基于盒维数的心音信号分形特征研究[J].生物数学学报, 2009, 2: 379‐383.
[17] Y Chung. Classification of Continuous HeartSound Signals Using the Ergodic Hidden Markov Model[J], Lecture Notes in Computer Science, 2007:177‐181.
[18] Kao C.W., Wei C.C., Liu J.J., et al. Automatic heart sound analysis with short‐time fourier transform and support vector machines[J]. IEEE International Midwest Symposium on Circuits and Systems, 2009: 188‐191.
[19] Harun U uz, Ahmet Arslan, etc. Detection of heart valve diseases by using fuzzy discrete hidden Markov model[J], Expert Systems with Applications, 2008(34):2799‐2811.
[20] Harun U uz, Ahmet Arslan, etc. A biomedical system based on hidden Markov model for diagnosis of the heart valve diseases[J]. Pattern Recognition Letters, 2007, 28(4):395‐404.
[21] Yong‐Joo Chung. Classification of Continuous Heart Sound Signals Using the Ergodic Hidden Markov Model[J]. Pattern Recognition and Image Analysis Lecture Notes in Computer Science, 2007:563‐570.
[22] C.H.Wu. On the analysis and classification of heart sounds based on segmental Bayesian networks and time analysis[J], Journal of the Chinese Institute of Electrical Engineering, 1997,4:343–350.
[23] Ilias Maglogiannis, Euripidis Loukis, EliasZ firopoulos,Antonis Stasis. Support Vectors Machine‐based identification of heart valve diseases using heart sounds[J], computer methods and programs in biomedicine,2009,95:47–61.
[24]肖仪华,裴驭力,曹泽翰,周世勇,肖守中.基于笔记本的心音分析仪[J].北京生物医学工程, 1999, 1:34‐38.
[25]王文辉,施民.便携式心音分析仪的研制[J].中国医疗器械杂志, 1994, 18(1):9‐13.
[26]张孝桂,何为,周静,李杰,石小波.基于嵌入式系统的便携式心音分析仪的研究[J].仪器仪表学报, 2007, 28(2):303‐307.
[27] Matias Brusco, Homer Nazeran. Development of an Intelligent PDA‐based Wearable Digital Phonocardiograph[C]. Proceedings of the 2005 IEEE Engineering In Medicine and Biology 27th Annual Conference. 2005, 27:3506‐3509.
[28] Malarvili, M.B. Kamarulafizam, I. Hussain, S. Helmi, D. Heart sound segmentation algorithm based on instantaneous energy of electrocardiogram[J]. Computer in Cardiology,2003,9:327‐330.
[29]宋明明,成谢锋,王厚大. HILBERT包络法在心音身份识别中的应用[J].微型机与应用, 2010,2:79‐82.
[30]王新沛,刘常春,李远洋,孙处然.基于高阶香农熵的心音分段算法[J].吉林大学学报, 2010,40(5): 1433‐1437.
[31]于倩,赵加祥,虎乐乐,倪虹.基于简单度的包络提取算法在心音分段中的应用[J].生物医学工程研究, 2009,28(1):11‐15.
[32]陈萌辉,叶大田,陈江天.基于信号包络及短时过零率的心音分段算法[J].北京生物医学工程, 2007,26(1):48‐51.
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