应用于远程监护系统的ECG信号处理算法在DSP器件上的实现
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摘要
对老龄人口和慢性病患者的监护日益成为全球医疗卫生工作的一个重要问题。远程监护,相对于传统的住院观察,给予患者更大的活动自由,不影响老龄人口的正常生活,价格低廉,是一个很好的解决方案。
本课题提出了一个不同于传统概念的新型穿戴式远程生理参数监护系统,它主要由监护中心和用户的穿戴式监护终端构成,二者使用GPRS网络进行通讯;监护终端由ARM控制器控制多个数据采集模块进行多路生理信号采集(心电、血压、体温等),同时由DSP处理器完成实时的数据处理。这个方案的主要特点为:首先,使用了GPRS无线通信网络作为穿戴式终端与中心的数据交换通道,克服了传统的有线传输或医院内部射频传输的弊端,使远程监护有较广的覆盖范围和可靠的数据传输通道;其次,使用了智能手持式设备的设计理念进行穿戴式终端的设计,引入ARM与DSP的双处理器构架进行本地生理信号处理,使监护终端的功能由传统的采集和传输数据扩展到处理和分析数据,给用户及时的信息反馈和危险预警。
本论文详细介绍了在穿戴式终端的DSP处理器上运行的ECG信号处理软件的设计和编程实现过程。本课题选用的DSP器件为TI公司的TMS320VC5509,它运行功率低,计算能力强,非常适合于穿戴式设备的应用。信号处理软件的具体功能模块包括一个基于小波分析和阈值检测的ECG信号R波的识别和定位算法、一个基于Lz77数据压缩算法的ECG数据无损压缩算法,以及一个基于AES的ECG数据加密算法。另外,本文还讨论了DSP与ARM两个处理器之间使用USB接口进行通讯的实现方式,使用DSP/BIOS操作系统进行程序任务管理和调度等问题。
试验结果表明,本文所设计的ECG信号处理算法能在TMS320VC5509上高效地运行,快速而准确地获得处理结果。本文为使用通用DSP器件进行医学信号处理提供了一个成功范例。
The monitoring of health status for old people and patients chronic disease has increasingly become an important healthcare issue around globe. Telemonitoring, compared with traditional in-hospital care, offers patients advantages such as low cost, more freedom and less interference with normal life, thus is a promising solution to above mentioned monitoring problem.
This project introduces an innovative wearable remote physiological monitoring system. It is composed of two parts: a healthcare center and wearable terminals. Those two parts communicate with each other via GPRS network. In the terminal, an ARM controller is in charge of multi-channel physiological signal sampling (ECG, blood pressure, body temperature, etc), and a DSP processor is employed to conduct real-time data processing. The notable feature of this design include: firstly, GPRS wireless communication network is utilized as data exchange media, which overcomes the shortcomings of traditional wired and short distance Radio Frequency methods, providing wide signal coverage and stable communication channels; Secondly, the design is base on the concept of intelligent handle-hold device, which utilizes ARM and DSP dual processors to conduct local physiological signal processing. This strategy extends the function of the terminal from the traditional data sampling and transmission, to data processing and analysis, which is able to provide the user with real-time feedbacks and warnings.
In this paper, the design and implementation of the ECG signal processing algorithm is discussed in detail. In this design, the DSP chip is the TMS320VC5509, manufactured by Texas Instrument Cooperation. This processor is featured as low power consumption and high computation capacity, which is very suitable in embedded application. The signal processing algorithm has the 3 main function modules: an R-wave detection algorithm based on wavelet transformation and threshold detection, a lossless data compression algorithm derived from Lz77 classical algorithm, and a data encryption algorithm based on AES method. Besides, discussion also includes data exchange strategy between ARM and DSP processors, the use of DSP/BIOS operation system to manage multi-task scheduling.
The experimental results demonstrate that the designed algorithm is very efficient on the DSP. Processing results can be obtained within desired time and accuracy. This paper offers a successful example for customizing general DSP device to medical signal processing applications.
本课题提出了一个不同于传统概念的新型穿戴式远程生理参数监护系统,它主要由监护中心和用户的穿戴式监护终端构成,二者使用GPRS网络进行通讯;监护终端由ARM控制器控制多个数据采集模块进行多路生理信号采集(心电、血压、体温等),同时由DSP处理器完成实时的数据处理。这个方案的主要特点为:首先,使用了GPRS无线通信网络作为穿戴式终端与中心的数据交换通道,克服了传统的有线传输或医院内部射频传输的弊端,使远程监护有较广的覆盖范围和可靠的数据传输通道;其次,使用了智能手持式设备的设计理念进行穿戴式终端的设计,引入ARM与DSP的双处理器构架进行本地生理信号处理,使监护终端的功能由传统的采集和传输数据扩展到处理和分析数据,给用户及时的信息反馈和危险预警。
本论文详细介绍了在穿戴式终端的DSP处理器上运行的ECG信号处理软件的设计和编程实现过程。本课题选用的DSP器件为TI公司的TMS320VC5509,它运行功率低,计算能力强,非常适合于穿戴式设备的应用。信号处理软件的具体功能模块包括一个基于小波分析和阈值检测的ECG信号R波的识别和定位算法、一个基于Lz77数据压缩算法的ECG数据无损压缩算法,以及一个基于AES的ECG数据加密算法。另外,本文还讨论了DSP与ARM两个处理器之间使用USB接口进行通讯的实现方式,使用DSP/BIOS操作系统进行程序任务管理和调度等问题。
试验结果表明,本文所设计的ECG信号处理算法能在TMS320VC5509上高效地运行,快速而准确地获得处理结果。本文为使用通用DSP器件进行医学信号处理提供了一个成功范例。
The monitoring of health status for old people and patients chronic disease has increasingly become an important healthcare issue around globe. Telemonitoring, compared with traditional in-hospital care, offers patients advantages such as low cost, more freedom and less interference with normal life, thus is a promising solution to above mentioned monitoring problem.
This project introduces an innovative wearable remote physiological monitoring system. It is composed of two parts: a healthcare center and wearable terminals. Those two parts communicate with each other via GPRS network. In the terminal, an ARM controller is in charge of multi-channel physiological signal sampling (ECG, blood pressure, body temperature, etc), and a DSP processor is employed to conduct real-time data processing. The notable feature of this design include: firstly, GPRS wireless communication network is utilized as data exchange media, which overcomes the shortcomings of traditional wired and short distance Radio Frequency methods, providing wide signal coverage and stable communication channels; Secondly, the design is base on the concept of intelligent handle-hold device, which utilizes ARM and DSP dual processors to conduct local physiological signal processing. This strategy extends the function of the terminal from the traditional data sampling and transmission, to data processing and analysis, which is able to provide the user with real-time feedbacks and warnings.
In this paper, the design and implementation of the ECG signal processing algorithm is discussed in detail. In this design, the DSP chip is the TMS320VC5509, manufactured by Texas Instrument Cooperation. This processor is featured as low power consumption and high computation capacity, which is very suitable in embedded application. The signal processing algorithm has the 3 main function modules: an R-wave detection algorithm based on wavelet transformation and threshold detection, a lossless data compression algorithm derived from Lz77 classical algorithm, and a data encryption algorithm based on AES method. Besides, discussion also includes data exchange strategy between ARM and DSP processors, the use of DSP/BIOS operation system to manage multi-task scheduling.
The experimental results demonstrate that the designed algorithm is very efficient on the DSP. Processing results can be obtained within desired time and accuracy. This paper offers a successful example for customizing general DSP device to medical signal processing applications.
引文
[1] 李明禄等.远程医学系统一般模型及其设计与实现问题.计算机工程.1997,23(特刊):172-175
[2] Kazmi, A.Arslan, B.R.Tulgar, et al. Advances in wireless telemedicine: improvements and challenges facing wireless ultrasound . Information Technology Applications in Biomedicine, 2000. Proceedings. 2000 IEEE EMBS International Conference on 9-10 Nov. 2000 :11 - 16.
[3] 李亚军.远程医疗在国内的应用和发展.医学信息.2006,19(1):39-40
[4] 胡新平.基于 Internet 远程医疗诊断系统.中国交通医学杂志.2004,18(6):777-779
[5] 简晓瑜.基于 P2P 的远程医疗会诊系统的研究与实现.计算机应用.2006,26(7):1747-1750
[6] 朱凌云,王正国,吴宝明等.GPRS移动式心电监护系统的QRS波实时检测算法.第三军医大学学报.2005,27(14):1467-1470
[7] 徐庐生,唐慧明.从信息技术看我国远程医疗的发展.中国医疗器械信息.2006,12(1):33-37
[8] 王明刚,姬光荣,毛英军.远程医疗技术的发展和应用.使用医药杂志.2006,23(3):359-362
[9] 方未艾.3G 技术将给中国远程医疗带来契机.中国医疗器械杂志.2006,30(1):31-32
[10] 韩 俊 淑 , 高 振 海 , 王 海 涛 . 远 程 医 疗 系 统 车 载 化 应 用 及 发 展 趋 势 . 中 国 医 学 装备.2006,3(1):34-36
[11] 郭在华,何建新,刘福祥等.家庭远程医疗保健系统设计与实现.成都信息工程学院学报.2006,21(1):7-11
[12] 程佩青.数字信号处理教程.第二版.北京.清华大学出版社.2001.8:1-7
[13] 汪春梅,孙洪波,任治刚等.TMS320C5000 系列 DSP 系统设计与开发实例.第一版.北京.电子工业出版社.2004,7:1-7
[14] 李翠微, 郑崇勋, 袁超伟. ECG 信号的小波变换检测方法. 中国生物医学工程学报, 1995,14(01):60-63
[15] Rasiah, A.I., Togneri, R., Attikiouzel, Y.. QRS detection using morphological and rhythm information., Proceedings., IEEE International Conference on Neural Networks. Nov.27 - Dec.1 1995. Vol. 5:2287-2292
[16] 刘少颖, 卢继来,郝丽,等.基于数学形态学和小波分解的 QRS 波群检测算法. 清华大学学报(自然科学版), 2004,44(06):852-855
[17] 边肇祺,张学工. 模式识别.第二版.北京.清华大学出版社.2000,1:16-25
[18] 阎平凡,张长水. 人工神经网络与模拟进化计算.第一版.北京.清华大学出版社.2000,11:1-12
[19] 王磊, 郑崇勋, 叶继伦,等.一种高效的 QRS 波实时检测方法. 北京生物医学工程, 1998,17(04):217-220
[20] 林薇, 吴效明, 朱维宗等. 应用小波变换模极大值检测 ECG 特征点. 医疗卫生装备.2004,10:13-14
[21] W Zong, GB Moody, D Jiang. A Robust Open-source Algorithm to Detect Onset and Duration of QRS Complexes. Computers in Cardiology 2003, 30:737-740
[22] HAN Dinh, DK Kumar, ND Pah, et al. Wavelets for QRS Detection. 2001 Proceedings of the 23rd Annual EMBS International Conference, October 25-28, Istanbul, Turkey: 1883-1887
[23] D. A. Huffman, A method for the construction of minimum-redundancy codes, Proc. IRE, vol.40 pp. 1098-1101 1952
[24] Jacob Ziv, Abraham Lempel. A Universal Algorithm for Sequential Data Compression. IEEE Transctions on Information Theory. Vol.IT-23, No.3, pp 337-343. MAY 1977
[25] Jacob Ziv, Abraham Lempel. Compression of Individual Sequences via Variable-Rate Coding. IEEE Transaction on information theory, vol.IT-24, No.5, pp 530-535, September 1978
[26] Welch T A.A Technique for High-performance Data Compression.IEEE Comput., 1984,17(6):8-19
[27] 管庶安.抗 50Hz 干扰心电数据无损压缩算法.中国医疗器械杂志.2005,29(2):112-114
[28] 练秋生,卢辉斌,王成儒.基于最优小波包的心电图压缩算法.仪器仪表学报.2001,22(2)增刊:336-338
[29] 蒋学程.心电信号小波变换自适应编码压缩方法.闽江学院学报.2006,27(2):45-48
[30] 成巍,方滨,沈毅等.基于 DCT 系数的二级矢量量化压缩 ECG 数据的方法.中国生物医学工程学报.2005,24(6):690-695
[31] 寇 鹏 , 方 滨 , 沈 毅 . 一 种 可 控 重 构 质 量 的 心 电 信 号 压 缩 方 法 . 北 京 生 物 医 学 工程.2004,23(2):109-112
[32] 王 兴 元 , 孟 娟 . 基 于 小 波 变 换 的 混 合 二 维 ECG 数 据 压 缩 方 法 . 生 物 物 理 学报.2006,22(3):133-136
[33] 张淅亮,吕维雪.心电数据压缩技术的评价指标.中国医疗器械杂志.1997,21(2):56-60
[34] Yaniv Zigel,Arnon Cohen,and Amos Katz. ECG Signal Compression Using Analysis by Synthesis Coding. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING.VOL. 47, NO. 10, OCTOBER 2000
[35] Furht. B., Perez. A. An adaptive real-time ECG compression algorithm with variable threshold. IEEE Transactions on Biomedical Engineering, Volume 35, Issue 6, June 1988 Page (s):489–494
[36] Barlas. G.D, Skordalakis. E.S.A novel family of compression algorithms for ECG and other semiperiodical, one-dimensional, biomedical signals. Biomedical Engineering, IEEE Transactions on, Vol. 43, Issue 8, Aug. 1996:820 - 828
[37] Smith, N.C., Platt, J.S. An ECG compression algorithm for full disclosure in a solid-statereal-time Holter monitor.Computers in Cardiology 1988. Proceedings. 25-28 Sept. 1988:569–572
[38] 曾海坚,胡振山,林家瑞,具有压缩比自动控制的 ECG 混合压缩算法,华中理工大学学报,1998,26(5):
[39] Tai, Shen-Chuan; Sun, Chia-Chun; Yan, Wen-Chien. A 2-D ECG compression method based on wavelet transform and modified SPIHT. IEEE Transactions on Biomedical Engineering, vol 52, No. 6, June, 2005:999-1008
[40] Blanco-Velasco, M.; Cruz-Roldan, F.; Godino-Llorente, J.I.; Barner, K.E.ECG compression with retrieved quality guaranteed. Electronics Letters, vol. 40, No. 23, Nov 11, 2004:1466-1467
[41] 谷大武,徐胜波.高级加密标准(AES)算法.第 1 版.北京.电子工业出版社.2003.3:65-72
[2] Kazmi, A.Arslan, B.R.Tulgar, et al. Advances in wireless telemedicine: improvements and challenges facing wireless ultrasound . Information Technology Applications in Biomedicine, 2000. Proceedings. 2000 IEEE EMBS International Conference on 9-10 Nov. 2000 :11 - 16.
[3] 李亚军.远程医疗在国内的应用和发展.医学信息.2006,19(1):39-40
[4] 胡新平.基于 Internet 远程医疗诊断系统.中国交通医学杂志.2004,18(6):777-779
[5] 简晓瑜.基于 P2P 的远程医疗会诊系统的研究与实现.计算机应用.2006,26(7):1747-1750
[6] 朱凌云,王正国,吴宝明等.GPRS移动式心电监护系统的QRS波实时检测算法.第三军医大学学报.2005,27(14):1467-1470
[7] 徐庐生,唐慧明.从信息技术看我国远程医疗的发展.中国医疗器械信息.2006,12(1):33-37
[8] 王明刚,姬光荣,毛英军.远程医疗技术的发展和应用.使用医药杂志.2006,23(3):359-362
[9] 方未艾.3G 技术将给中国远程医疗带来契机.中国医疗器械杂志.2006,30(1):31-32
[10] 韩 俊 淑 , 高 振 海 , 王 海 涛 . 远 程 医 疗 系 统 车 载 化 应 用 及 发 展 趋 势 . 中 国 医 学 装备.2006,3(1):34-36
[11] 郭在华,何建新,刘福祥等.家庭远程医疗保健系统设计与实现.成都信息工程学院学报.2006,21(1):7-11
[12] 程佩青.数字信号处理教程.第二版.北京.清华大学出版社.2001.8:1-7
[13] 汪春梅,孙洪波,任治刚等.TMS320C5000 系列 DSP 系统设计与开发实例.第一版.北京.电子工业出版社.2004,7:1-7
[14] 李翠微, 郑崇勋, 袁超伟. ECG 信号的小波变换检测方法. 中国生物医学工程学报, 1995,14(01):60-63
[15] Rasiah, A.I., Togneri, R., Attikiouzel, Y.. QRS detection using morphological and rhythm information., Proceedings., IEEE International Conference on Neural Networks. Nov.27 - Dec.1 1995. Vol. 5:2287-2292
[16] 刘少颖, 卢继来,郝丽,等.基于数学形态学和小波分解的 QRS 波群检测算法. 清华大学学报(自然科学版), 2004,44(06):852-855
[17] 边肇祺,张学工. 模式识别.第二版.北京.清华大学出版社.2000,1:16-25
[18] 阎平凡,张长水. 人工神经网络与模拟进化计算.第一版.北京.清华大学出版社.2000,11:1-12
[19] 王磊, 郑崇勋, 叶继伦,等.一种高效的 QRS 波实时检测方法. 北京生物医学工程, 1998,17(04):217-220
[20] 林薇, 吴效明, 朱维宗等. 应用小波变换模极大值检测 ECG 特征点. 医疗卫生装备.2004,10:13-14
[21] W Zong, GB Moody, D Jiang. A Robust Open-source Algorithm to Detect Onset and Duration of QRS Complexes. Computers in Cardiology 2003, 30:737-740
[22] HAN Dinh, DK Kumar, ND Pah, et al. Wavelets for QRS Detection. 2001 Proceedings of the 23rd Annual EMBS International Conference, October 25-28, Istanbul, Turkey: 1883-1887
[23] D. A. Huffman, A method for the construction of minimum-redundancy codes, Proc. IRE, vol.40 pp. 1098-1101 1952
[24] Jacob Ziv, Abraham Lempel. A Universal Algorithm for Sequential Data Compression. IEEE Transctions on Information Theory. Vol.IT-23, No.3, pp 337-343. MAY 1977
[25] Jacob Ziv, Abraham Lempel. Compression of Individual Sequences via Variable-Rate Coding. IEEE Transaction on information theory, vol.IT-24, No.5, pp 530-535, September 1978
[26] Welch T A.A Technique for High-performance Data Compression.IEEE Comput., 1984,17(6):8-19
[27] 管庶安.抗 50Hz 干扰心电数据无损压缩算法.中国医疗器械杂志.2005,29(2):112-114
[28] 练秋生,卢辉斌,王成儒.基于最优小波包的心电图压缩算法.仪器仪表学报.2001,22(2)增刊:336-338
[29] 蒋学程.心电信号小波变换自适应编码压缩方法.闽江学院学报.2006,27(2):45-48
[30] 成巍,方滨,沈毅等.基于 DCT 系数的二级矢量量化压缩 ECG 数据的方法.中国生物医学工程学报.2005,24(6):690-695
[31] 寇 鹏 , 方 滨 , 沈 毅 . 一 种 可 控 重 构 质 量 的 心 电 信 号 压 缩 方 法 . 北 京 生 物 医 学 工程.2004,23(2):109-112
[32] 王 兴 元 , 孟 娟 . 基 于 小 波 变 换 的 混 合 二 维 ECG 数 据 压 缩 方 法 . 生 物 物 理 学报.2006,22(3):133-136
[33] 张淅亮,吕维雪.心电数据压缩技术的评价指标.中国医疗器械杂志.1997,21(2):56-60
[34] Yaniv Zigel,Arnon Cohen,and Amos Katz. ECG Signal Compression Using Analysis by Synthesis Coding. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING.VOL. 47, NO. 10, OCTOBER 2000
[35] Furht. B., Perez. A. An adaptive real-time ECG compression algorithm with variable threshold. IEEE Transactions on Biomedical Engineering, Volume 35, Issue 6, June 1988 Page (s):489–494
[36] Barlas. G.D, Skordalakis. E.S.A novel family of compression algorithms for ECG and other semiperiodical, one-dimensional, biomedical signals. Biomedical Engineering, IEEE Transactions on, Vol. 43, Issue 8, Aug. 1996:820 - 828
[37] Smith, N.C., Platt, J.S. An ECG compression algorithm for full disclosure in a solid-statereal-time Holter monitor.Computers in Cardiology 1988. Proceedings. 25-28 Sept. 1988:569–572
[38] 曾海坚,胡振山,林家瑞,具有压缩比自动控制的 ECG 混合压缩算法,华中理工大学学报,1998,26(5):
[39] Tai, Shen-Chuan; Sun, Chia-Chun; Yan, Wen-Chien. A 2-D ECG compression method based on wavelet transform and modified SPIHT. IEEE Transactions on Biomedical Engineering, vol 52, No. 6, June, 2005:999-1008
[40] Blanco-Velasco, M.; Cruz-Roldan, F.; Godino-Llorente, J.I.; Barner, K.E.ECG compression with retrieved quality guaranteed. Electronics Letters, vol. 40, No. 23, Nov 11, 2004:1466-1467
[41] 谷大武,徐胜波.高级加密标准(AES)算法.第 1 版.北京.电子工业出版社.2003.3:65-72