多参数网络监护系统的研究
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摘要
本课题旨在研制出多参数监护仪的基础上进行中央监护系统的研究，实现一台中央机与多台床边监护仪的有线实时通讯，以建立实用的医院网络监护系统，用于长距离地连续监测多床病人的各种生理信号。同时，对心电信号的噪声去除和检测识别进行研究，利用小波变换实现心电信号的优化处理。本课题是博士论文《多参数中央监护系统关键技术研究》的重要组成部分。
在多参数监护仪的研制方面，重点设计了心电模块，详细阐述了各个环节硬件电路的组成结构与性能，针对系统的电气安全要求和心电信号的特点，选择了适宜的芯片，使心电监护系统具有输入信号灵敏度高、抗干扰性能强、电气安全特性好和低功耗的特征。
在心电信号处理方面，利用小波变换多尺度多分辨的特点对心电信号进行了预处理，并进一步对 QRS、P、T波进行检测识别，取得了良好的仿真效果。
在中央网络监护方面，利用多线程方法实现了RS232串口间的高速实时通讯，并编写出功能强大的中央监护系统软件，具有很强的实用价值，临床使用效果良好。
The aim of this paper is to research a central monitoring network system on the basis of the development of the physiological multi-parameter monitor. The central monitoring system can be used to complete the real-time communication between a central machine and several bedside monitors and construct a useful hospital monitoring network so that many different physiological signals of patients can be monitored continuously and transmitted for a long distance. Meanwhile, with the research in moving noise of ECG signals and detecting them, the wavelet transform technique is selected to dispose the ECG signal.
In the aspect of the development of the multi-parameter monitor, the ECG module is designed chiefly. The structure and performance of hardware circuits are explained in detail. And in consideration of the electric safe specification and characteristics of ECG signals, suitable chips are selected so that the ECG monitoring system has many characteristics such as higher sensitivity, preferable anti-interference, good electric safety and low power dissipation.
Then, in the aspect of ECG signals disposing, the pre-processing of ECG signals and the recognition of the typical waveform such as QRS complex, P wave and T wave are studied by means of the multi-resolution analysis of wavelet transform. Good simulation result has been attained.
At last, the multithreading technique is employed to realize high speed and real-time communication between RS232 serial ports. At the same time, the multifunctional software about central monitoring system is developed, which has great practical value in clinical diagnose.
在多参数监护仪的研制方面，重点设计了心电模块，详细阐述了各个环节硬件电路的组成结构与性能，针对系统的电气安全要求和心电信号的特点，选择了适宜的芯片，使心电监护系统具有输入信号灵敏度高、抗干扰性能强、电气安全特性好和低功耗的特征。
在心电信号处理方面，利用小波变换多尺度多分辨的特点对心电信号进行了预处理，并进一步对 QRS、P、T波进行检测识别，取得了良好的仿真效果。
在中央网络监护方面，利用多线程方法实现了RS232串口间的高速实时通讯，并编写出功能强大的中央监护系统软件，具有很强的实用价值，临床使用效果良好。
The aim of this paper is to research a central monitoring network system on the basis of the development of the physiological multi-parameter monitor. The central monitoring system can be used to complete the real-time communication between a central machine and several bedside monitors and construct a useful hospital monitoring network so that many different physiological signals of patients can be monitored continuously and transmitted for a long distance. Meanwhile, with the research in moving noise of ECG signals and detecting them, the wavelet transform technique is selected to dispose the ECG signal.
In the aspect of the development of the multi-parameter monitor, the ECG module is designed chiefly. The structure and performance of hardware circuits are explained in detail. And in consideration of the electric safe specification and characteristics of ECG signals, suitable chips are selected so that the ECG monitoring system has many characteristics such as higher sensitivity, preferable anti-interference, good electric safety and low power dissipation.
Then, in the aspect of ECG signals disposing, the pre-processing of ECG signals and the recognition of the typical waveform such as QRS complex, P wave and T wave are studied by means of the multi-resolution analysis of wavelet transform. Good simulation result has been attained.
At last, the multithreading technique is employed to realize high speed and real-time communication between RS232 serial ports. At the same time, the multifunctional software about central monitoring system is developed, which has great practical value in clinical diagnose.
引文
1. Daubechies I. The wavelet transform, time-frequency localization and signal analysis. IEEE Trans Inform Theory, 1990,36:961-1005
2. Mallat S.A. Theory of multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Machine Intell, 1989,11:674-693
3. Mallat S and Hwang W L. Singularity detection and processing with wavelets. IEEE Trans Inform Theory, 1992,38:617-642
4. Cohen A, Daubechies I and Feauveau J C. Biorthogonal bases of compactly supported wavelets. CommPure and Appl Math, 1992,42:485-560
5. M. Shensa. The discrete wavelet transform: wedding the trous and Mallat algorithms. IEEE Trans. On Signal Processing, 1992(10): 2464-2482.
6. Gramatikov, B Brinker, J Yi-chun,S Thakor, NV. wavelet analysis and time-frequency distributions of the body surface ECG before and after angioplasty. Computer Methods and Programs in Biomedicine, 2000, Vol.62(2): 87-98
7. Sivannarayana,N Reddy, DC. Biorthogonal wavelet transforms for ECG parameters estimation. Medical engineering & physics, 1999, Vol.21 (3): 167-174
8. Unser, M. Aldroubi, A. A review of wavelets in biomedical applications. Proceedings of the IEEE, 1996,Vol. 84Issue: 4:626-638
9. Cuiwei Li Chongxun Zheng. Detection of ECG characteristic points using wavelet transforms. Biomedical Chromatography, 1995,Vol. 42(1): 21-28
10. Shubha Kadambe,Robin Murray. Wavelet Transform-Based QRS Complex Detector. IEEE Transactions on Biomedical Engineering, 1999,Vol.46(7): 838-848
11. Challis R.E. and R.I.Kitney. Digital Filters for Biomedical signal Processing. Journal of Biomedical Engineering, 1982,Vol.4(10): 273-277
12. Kostas I. Panoulas Leontios J. Hadjileontiadis Stavros M. Panas. Enhancement of R-wave Detection in ECG data analysis using higher-orDErstatistics. 23rd Annual International Conference of the IEEE Engineering in Medicine andBiology Society, vol. 1, Istanbul, Turkey, October 25-28, 2001, 344-347
13. Leontios J. Hadjileontiadis Kostas I. Panoulas Thomas Penzel Stavros M. Panas. Performance of three QRS Detection algorithms during sleep: a comparative study. 23rd Annual International Conferernce of the IEEE Engineering in Medicine andBiology Society, vol.2, Istanbul, Turkey, October 25-28, 2001, 1954-1957
14. KocCK, Chen G R and Chui C K. Complexity analysis of wavelet signal decomposition and reconstruction. IEEE Trans on Aerospace and Electronic Systems, 1994,30:910-918
15. Chia CW, et all. A Comparison of Human Experts and computer algorithms in detecting and classifying beats in noise-corrupted electrocardiograms. Computer in Cardiology, 1991,18:465
16．田艳军，廖春林。中央监护系统的发展趋势与网络开发。中国医疗机械杂志，2001，Vol．25(5)：284-287
17．田艳军，廖春林。适合中国国情的多参数中央监护系统的前景预测与研究开发。医疗卫生装备，2000．5：3-6
18．周荷琴，蔡方辉等。多参数监护仪的设计。中国医疗器械杂志，1999，Vol．23(4)：191-193
19．刘铁兵，汤黎明等。基于可编程逻辑器件的便携式心电监护仪的设计与实现。医疗卫生装备，2001．4：9-10
20．杨玉星，萨伊德等。LCD便携式急救心电监护仪。中国医疗器械杂志，1995，Vol．19(2)：67-72
21．陈敏莲，何平，吴雄文等。基于Windows平台的多生理参数网络监护系统。中国医疗器械杂志，2000，Vol．24(2)：73-77
22．文莉，刘正士，葛运建，小波去噪的几种方法。合肥工业大学学报(自然科学版)，2002，Vol．25(2)：167-172
23．王博亮，刘希顺，黄晓玲。心电信号中QRS波群的实时检测算法。航天医学与医学工程，1995，Vol．8(1)：23-26
24．姬军，董秀珍。心电信号QRS波的识别算法及程序设计。北京生物医学工程，Vol．20(2)，2001：119-122。
25．陆英北，蔡坤宝。基于小波变换心电信号去噪方法。广西工学院学报，1999，Vol．10(2)：68-71
26．朱康丽，王笑梅。基于小波变换的ECG信号伪差消除法。合肥工业大学学报(自然科学版)，1999，Vol．22(4)：104-106
27．李智，黄智。小波变换在心电图QRS波检测中的应用。北京生物医学工程，1996，Vol．15(1)：10—14
28．张晶晶。串口通讯在卡普中央监护系统中的应用。微型机与应用，2001．1：45-46
29．陈金详，董恩源，邹积言。Windows95／NT下用VC++进行多线程串行通讯的编程方法。计算机应用，2001．5：97-98
30．张志明，李蓉艳，王磊。基于多线程技术和自定义消息编程实现Windows 9x异步串行通讯。计算机应用研究，2000．5：64-66
31．刘海涛，陆延哲。医用多参数监护仪软件的设计和实现。中国民航学院学报，2002，Vol．20(4)：44-48
32．许俊，许茹。Windows 3.1下实时心电监护系统软件的设计。电子技术应用，1998．7：10-12
33．吴杰。心电图自动分析技术的标准化问题。世界医疗器械，1997，Vol．3(10)：24-28
34．常昌远，魏同立。无创血氧饱和度传感系统的研究。应用科学学报，1999．12：433～438
35．Frank van Gilluwe著。PC技术内幕：I／O、CPU和固定内存区程序员指南。精英科技译，北京：中国电力出版社，2001
36．[美]崔锦泰。小波分析导论。白居先译。西安：西安交通大学出版社，1998
37．秦前清，杨宗凯。实用小波分析。西安：西安电子科技大学出版社，1992
38．宋建社。小波分析及其应用例选。北京：现代出版社，1998
39．胡昌华，张军波等。基于MATLAB的系统分析与设计——小波分析。西安：西安电子科技大学出版社，1999
40．杨福生，吕扬生主编。生物医学信号的处理和识别。天津，天津科技翻译出版公司。1997
41．David J. Kruglinski, Scot Wingo and George Shepherd. Visual C++6. 0技术内幕。北京：希望电子出版社，1999
42．清汉计算机工作室。Visual C++6.0数据库与网络开发实例。北京：机械工业出
版社，2000
43．刘刀桂，孟繁晶。Visual C++实践与提高数据库篇。北京：中国铁道出版社，2001
2. Mallat S.A. Theory of multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Machine Intell, 1989,11:674-693
3. Mallat S and Hwang W L. Singularity detection and processing with wavelets. IEEE Trans Inform Theory, 1992,38:617-642
4. Cohen A, Daubechies I and Feauveau J C. Biorthogonal bases of compactly supported wavelets. CommPure and Appl Math, 1992,42:485-560
5. M. Shensa. The discrete wavelet transform: wedding the trous and Mallat algorithms. IEEE Trans. On Signal Processing, 1992(10): 2464-2482.
6. Gramatikov, B Brinker, J Yi-chun,S Thakor, NV. wavelet analysis and time-frequency distributions of the body surface ECG before and after angioplasty. Computer Methods and Programs in Biomedicine, 2000, Vol.62(2): 87-98
7. Sivannarayana,N Reddy, DC. Biorthogonal wavelet transforms for ECG parameters estimation. Medical engineering & physics, 1999, Vol.21 (3): 167-174
8. Unser, M. Aldroubi, A. A review of wavelets in biomedical applications. Proceedings of the IEEE, 1996,Vol. 84Issue: 4:626-638
9. Cuiwei Li Chongxun Zheng. Detection of ECG characteristic points using wavelet transforms. Biomedical Chromatography, 1995,Vol. 42(1): 21-28
10. Shubha Kadambe,Robin Murray. Wavelet Transform-Based QRS Complex Detector. IEEE Transactions on Biomedical Engineering, 1999,Vol.46(7): 838-848
11. Challis R.E. and R.I.Kitney. Digital Filters for Biomedical signal Processing. Journal of Biomedical Engineering, 1982,Vol.4(10): 273-277
12. Kostas I. Panoulas Leontios J. Hadjileontiadis Stavros M. Panas. Enhancement of R-wave Detection in ECG data analysis using higher-orDErstatistics. 23rd Annual International Conference of the IEEE Engineering in Medicine andBiology Society, vol. 1, Istanbul, Turkey, October 25-28, 2001, 344-347
13. Leontios J. Hadjileontiadis Kostas I. Panoulas Thomas Penzel Stavros M. Panas. Performance of three QRS Detection algorithms during sleep: a comparative study. 23rd Annual International Conferernce of the IEEE Engineering in Medicine andBiology Society, vol.2, Istanbul, Turkey, October 25-28, 2001, 1954-1957
14. KocCK, Chen G R and Chui C K. Complexity analysis of wavelet signal decomposition and reconstruction. IEEE Trans on Aerospace and Electronic Systems, 1994,30:910-918
15. Chia CW, et all. A Comparison of Human Experts and computer algorithms in detecting and classifying beats in noise-corrupted electrocardiograms. Computer in Cardiology, 1991,18:465
16．田艳军，廖春林。中央监护系统的发展趋势与网络开发。中国医疗机械杂志，2001，Vol．25(5)：284-287
17．田艳军，廖春林。适合中国国情的多参数中央监护系统的前景预测与研究开发。医疗卫生装备，2000．5：3-6
18．周荷琴，蔡方辉等。多参数监护仪的设计。中国医疗器械杂志，1999，Vol．23(4)：191-193
19．刘铁兵，汤黎明等。基于可编程逻辑器件的便携式心电监护仪的设计与实现。医疗卫生装备，2001．4：9-10
20．杨玉星，萨伊德等。LCD便携式急救心电监护仪。中国医疗器械杂志，1995，Vol．19(2)：67-72
21．陈敏莲，何平，吴雄文等。基于Windows平台的多生理参数网络监护系统。中国医疗器械杂志，2000，Vol．24(2)：73-77
22．文莉，刘正士，葛运建，小波去噪的几种方法。合肥工业大学学报(自然科学版)，2002，Vol．25(2)：167-172
23．王博亮，刘希顺，黄晓玲。心电信号中QRS波群的实时检测算法。航天医学与医学工程，1995，Vol．8(1)：23-26
24．姬军，董秀珍。心电信号QRS波的识别算法及程序设计。北京生物医学工程，Vol．20(2)，2001：119-122。
25．陆英北，蔡坤宝。基于小波变换心电信号去噪方法。广西工学院学报，1999，Vol．10(2)：68-71
26．朱康丽，王笑梅。基于小波变换的ECG信号伪差消除法。合肥工业大学学报(自然科学版)，1999，Vol．22(4)：104-106
27．李智，黄智。小波变换在心电图QRS波检测中的应用。北京生物医学工程，1996，Vol．15(1)：10—14
28．张晶晶。串口通讯在卡普中央监护系统中的应用。微型机与应用，2001．1：45-46
29．陈金详，董恩源，邹积言。Windows95／NT下用VC++进行多线程串行通讯的编程方法。计算机应用，2001．5：97-98
30．张志明，李蓉艳，王磊。基于多线程技术和自定义消息编程实现Windows 9x异步串行通讯。计算机应用研究，2000．5：64-66
31．刘海涛，陆延哲。医用多参数监护仪软件的设计和实现。中国民航学院学报，2002，Vol．20(4)：44-48
32．许俊，许茹。Windows 3.1下实时心电监护系统软件的设计。电子技术应用，1998．7：10-12
33．吴杰。心电图自动分析技术的标准化问题。世界医疗器械，1997，Vol．3(10)：24-28
34．常昌远，魏同立。无创血氧饱和度传感系统的研究。应用科学学报，1999．12：433～438
35．Frank van Gilluwe著。PC技术内幕：I／O、CPU和固定内存区程序员指南。精英科技译，北京：中国电力出版社，2001
36．[美]崔锦泰。小波分析导论。白居先译。西安：西安交通大学出版社，1998
37．秦前清，杨宗凯。实用小波分析。西安：西安电子科技大学出版社，1992
38．宋建社。小波分析及其应用例选。北京：现代出版社，1998
39．胡昌华，张军波等。基于MATLAB的系统分析与设计——小波分析。西安：西安电子科技大学出版社，1999
40．杨福生，吕扬生主编。生物医学信号的处理和识别。天津，天津科技翻译出版公司。1997
41．David J. Kruglinski, Scot Wingo and George Shepherd. Visual C++6. 0技术内幕。北京：希望电子出版社，1999
42．清汉计算机工作室。Visual C++6.0数据库与网络开发实例。北京：机械工业出
版社，2000
43．刘刀桂，孟繁晶。Visual C++实践与提高数据库篇。北京：中国铁道出版社，2001