基于心机械图的心功能信号分析算法研究
摘要
现代医学技术和医疗信息与仪器的迅速发展,促进了心功能学发展成为一门崭新的学科,在心血管病诊疗工作中居重要地位并得到广泛应用。心功能诊断的技术主要涉及到医学影像学检测方法,如X线、CT、磁共振及超声等方面的研究进行的如火如荼,同时利用无创生理信号进行基层医院或家庭社区的心功能研究有很大的研究价值。多导生理参数检测来评价心功能的方法仍然有比较重要的应用价值,主要通过心电图,心音图,颈动脉波动图,心尖波动图、心阻抗等参数进行检测。本文主要是依据基于心机械图的心功能检测信号进行心功能分析,同时主要研究信号的检测和分析算法,通过检测心电、心音和颈动脉波三种生理信号的特征检测来计算STI,依据STI评价方法设计适用于便携式和小型化的心功能系统。
在心功能信号处理方面,对比各种数字滤波方法的优缺点以便选择最佳的方法进行信号处理。同时本文也突破了传统的经典信号处理方法,采用了小波分析的方法。小波分析是一种时间—尺度表示法,对于处理具有随机性和强噪声医学信号是行之有效的。文中简单的介绍了小波分析方法的原理和含义,并对心电图,心音图,和颈动脉搏动图的小波分析算法分别进行了详尽的阐述。最后根据算法对实际病历的颈动脉波的实验结果进行算法优越性的对比,显示了该方法的检测精度。
本系统的软件部分采用可视化的开发工具Visual Basic 6.0上进行开发,数据库设计在Access 2003上进行,并采用ADO方式访问数据库,为用户提供了功能强大的功能界面,不仅能通过数字信号处理进行信号的同步显示,还能对波形进行回放和手动分析等,同时还有病历数据库的管理功能,为医院管理和病人检索奠定了基础。
With the development of the medical science and modern engineering technology, the cardiac function occupies an important position at Ventricular function treatment as a new school subject . Cardiac diagnostic technique are mainly related to medical imaging detection methods, such as X ray, CT, MRI and ultrasound of research, while using non-invasive physiological signals, play great value on basic hospital or home community. Detection of multiple physiologic parameters to evaluate the cardiac function method is still of relatively important value, mainly detect through ECG, PCG, CAP, RICG etc. This article is based on the MCG to test the cardiac function of heart, while mainly study the signal detection and analysis of algorithms, by detecting the ECG, PCG and CAP physiological characteristics signals to calculate the STI, according to STI evaluation designed for portable and compact system of cardiac function.
Through comparing the advantages and disadvantages of various digital filtering methods it in order to choose the best method of signal processing for the MCG.. At the same time its also broke the traditional classical signal processing method, using wavelet analysis. Wavelet analysis is a time - scale representation and is effective to deal with strong noise with random and medical signal. The paper describes a simple method of wavelet analysis theory and meaning, and the MCG signal wavelet analysiswere carried out in detail. Finally, the algorithm records the actual superiority of the experimental results of algorithm comparison, shows the method of detection accuracy.
Part of the system software using a visual development tool of Visual Basic 6.0 on the development with the database designing in Access 2003 and using ADO access to databases. It provide users powerful function interface, not only display the signal but also waveform playback and manual analysis, as well as medical records database management and hospital management and made foundation for the patient search.
在心功能信号处理方面,对比各种数字滤波方法的优缺点以便选择最佳的方法进行信号处理。同时本文也突破了传统的经典信号处理方法,采用了小波分析的方法。小波分析是一种时间—尺度表示法,对于处理具有随机性和强噪声医学信号是行之有效的。文中简单的介绍了小波分析方法的原理和含义,并对心电图,心音图,和颈动脉搏动图的小波分析算法分别进行了详尽的阐述。最后根据算法对实际病历的颈动脉波的实验结果进行算法优越性的对比,显示了该方法的检测精度。
本系统的软件部分采用可视化的开发工具Visual Basic 6.0上进行开发,数据库设计在Access 2003上进行,并采用ADO方式访问数据库,为用户提供了功能强大的功能界面,不仅能通过数字信号处理进行信号的同步显示,还能对波形进行回放和手动分析等,同时还有病历数据库的管理功能,为医院管理和病人检索奠定了基础。
With the development of the medical science and modern engineering technology, the cardiac function occupies an important position at Ventricular function treatment as a new school subject . Cardiac diagnostic technique are mainly related to medical imaging detection methods, such as X ray, CT, MRI and ultrasound of research, while using non-invasive physiological signals, play great value on basic hospital or home community. Detection of multiple physiologic parameters to evaluate the cardiac function method is still of relatively important value, mainly detect through ECG, PCG, CAP, RICG etc. This article is based on the MCG to test the cardiac function of heart, while mainly study the signal detection and analysis of algorithms, by detecting the ECG, PCG and CAP physiological characteristics signals to calculate the STI, according to STI evaluation designed for portable and compact system of cardiac function.
Through comparing the advantages and disadvantages of various digital filtering methods it in order to choose the best method of signal processing for the MCG.. At the same time its also broke the traditional classical signal processing method, using wavelet analysis. Wavelet analysis is a time - scale representation and is effective to deal with strong noise with random and medical signal. The paper describes a simple method of wavelet analysis theory and meaning, and the MCG signal wavelet analysiswere carried out in detail. Finally, the algorithm records the actual superiority of the experimental results of algorithm comparison, shows the method of detection accuracy.
Part of the system software using a visual development tool of Visual Basic 6.0 on the development with the database designing in Access 2003 and using ADO access to databases. It provide users powerful function interface, not only display the signal but also waveform playback and manual analysis, as well as medical records database management and hospital management and made foundation for the patient search.
引文
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[2]刘天虎,曾洪燕,袁启远等.心音图运动试验量化心功能分级的研究[J] .心血管康复医学杂志, 2004 ,13(2) :99～101
[3]刘天虎,刘芮,赖龙剑等.心功能分级的客观量化指标研究.中国心血管杂志2003(4),8(2) :121-122
[4]刘天虎,刘剑雄,肖守中等.心脏储备指数和射血分数量化心功能分级的相关性研究.四川医学2005,26(11):1197
[5] Yihua Xiao, Shouzhong Xiao, Zehan Cao, et al, The phonncardiogram exercise [J]. IEEE engineering in medicine and biology Magazine. 1999.18(4).111-115
[6] Tei C,Ling L H, Hong D O, etal, New index of combined systolic and diastolic myocardial performance, A simple and reproducible measure of cardiac function study in normals and dilated cardiomyopathy [J],Cardiol,1995,26(6),357-366
[7]张艳辉. Tei指数评价慢性心功能不全患者舒张功能的临床研究.延边大学硕士学位论文. 2006,4
[8]杨颖,王金锐,杨海萍等.定量组织速度成像对左室心肌收缩功能的研究.中国医学影像技术.2001,17(1)
[9]刘明春,纂美和,黄聪.房室平面位移法评价左心室舒张功能四川省攀枝花市第五人民医院彩超室(攀枝花617000),中国心血管杂志2002, 7(4)
[10]陈绍绍,齐国先,贾大林.用介入方法获得的两个参数在评价左心室舒张功能中的应用, Chinese Circulation Journal, February 2003,Vol.18,No.1(Serial No.119)
[11]周阳泱,韩萍,冯敢生.多层螺旋CT定量评估左心功能的研究进展, Foreign Medicine Sciences Clinical Radiological Fascicle 2005 May;28(3):150-152
[12] Cerqueira MD, Weissman NJ, Dilsizian V,et,al,Standardized myocardial segmentation and nomenclature for tomographic imageing of the heart:a ststement for healthcare professionals from the cardiac imaging committee of the council on clinical cardiology of the American heart ssociation, Circulation,2002,105(4):539-542
[13] Wintersperger BJ,Ruff CR,et al,Assessment of regional myocardial perfusion using multirow-detector computed tomography, ECR,2002,B-0847
[14]祝顺燕,心电信号的采集和便携式心电图机的设计[D].东南大学硕士学位论文.2005.3.28
[15]刁越.脉搏波分析方法及心血管功能参数检测系统的研究.北京工业大学硕士论文. 2005,5
[16]吕卓人,临床医学概论,北京,科学出版社, 2001
[17]吴延军,徐泾平等.心音的产生与传导机制[J].生物医学工程学杂志, 1996: 13 (3): 280-288.
[18]裴驭力.心音信号的数字化处理系统[D].重庆:重庆大学, 2005:5-7.
[19]王保华.生物医学测量与仪器[M].上海:复旦大学出版社, 2004:101-103.
[20]熊政纲,阳奇志.实用预防医学[J ] .测量指端脉搏波的实验设计, 2003 ,10 (1) :104 - 105.
[21]杨福生,高上凯.生物医学信号处理[M].北京:高等教育出版社,1989
[22]孟兆辉,白净.高血压病人的光电容积脉搏波的频域分析[J ] .北京生物医学工程, 2002 ,21 (1) :1 - 4.
[23]程佩青.数字信号处理(第2版) [M].北京:清华大学出版社,2003.
[24]楼顺天,李博菡.基于MATLAB的系统分析与设计—信号处理[ M ] .西安:西安电子科技大学出版社, 2001.
[25]吴凯,吴效明.多生理参数远程虚拟检测仪的设计与实现[J].微计算机信息, 2006, 1-1:145
[26]庄天戈.计算机在生物医学中的应用(第二版)[M].北京:科学出版社,2000
[27]孙延奎.小波分析及其应用.第1版.北京:机械工业出版社,2005.
[28] Cuiwei Li, Chongxun Zheng. Detection of ECG Characteristic Points Using Wavelet Transforms. IEEE Transactions on Biomedical Engineering. VOL.42, NO.1, JAN., 1995.
[29] S.Mallat. Zero-crossings of a wavelet transform. IEEE Trans. Inform.Theory, vol.37, pp.1019-1033, 1991.
[30] Olivier Rioul, Martin Vetterli. Wavelets and Signal Processing. IEEE Signal Processing Magazine: 14-38, Oct.1991.
[31] Hamilton PS, Tompkins WJ. Adaptive matched filtering for QRS detection[C]. Proc of Annual Int Conf of IEEE EMBS,1988:47-148
[32] DUANHuilong, FENG Jingyi, HONG Wei. An algorithm for premature ventricularcontraction classification based on the combination of template matching and character is tic recognition [J].Space Medicine & Medical Engineering, 2002, 15(2):98-102.
[33] HEXin, ZHANGYonghong, BAIJing. Detection of QRS complex basing on wavelet transform template [J].Space Medicine & Medical Engineering, 2003, 16(6):438-442.
[34] CHENGPeijun, CHENGShigui, YUYaorong, etal. Detecting character is tic points of ECG signals with binomial wavelet [J].Space Medicine & Medical Engineering, 2004,17(1):59-63.
[35] ChenHC, ChenSW.A moving average based filtering system with its application to real time QRS detection[C].IEEEComputersinCardiology, 2003, 30:585-588.
[36] MoraesJCTB, FreitasMM, VilaniFN, etal. A QRS complex detection algorithm using electrocardiogram leads [J].IEEEcomputersincardiology, 2002,29:205-208.
[37] NevesRodriguesJ, OwallV, SornmoL. QRS detection for pace makers in anoisy environment using a time lagged artificial neural network[C].IEEEInternSymonCS,2001.596-599
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