可穿戴式远程医疗系统用户端心电信号的实时检测
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摘要
远程医疗系统为如今社会医疗资源紧缺的问题提供了一种有效的解决方法。然而由于系统的家庭终端部分存在着不够方便和测量过程不舒适等问题,使得远程医疗系统得不到普及。本论文研究的穿戴式远程医疗系统是一种新颖的医疗仪器,使患者无需到医院就得到日常监护。本论文重点研究了穿戴式远程医疗系统客户端的心电信号检测算法,提出了符合系统实时性要求的ECG信号R波和P波检测算法。
首先对ECG信号进行预处理,采用平滑滤波器来滤除50Hz工频干扰,然后对信号进行小波变换,通过将高尺度的逼近信号和低尺度的细节信号置零并重构信号,达到去除基线漂移和高频噪声的目的。
接着本文重点研究了心电特征信息提取方法。在分析了小波变换检测心电信号奇异点的原理之后,采用Daubechies3阶小波对预处理后的ECG信号进行分解,在22和23两个小波系数上使用自适应阈值、不应期和补偿等策略对心电信号进行R波检测。实验结果显示,算法的平均正确检测率达到了99%以上。
P波检测的方法是在R波定位完成之后,将QRS-T波对消掉,使得原始信号仅剩P波为主要成分,然后对这部分信号进行自适应阈值判断,检测出P波位置。实验结果显示本算法对某些信号的检测效率达到了90%。另外本文还研究了部分心律失常病例的自动分析技术,介绍了心动过缓、心动过速等病例的判断指标。
课题使用TMS320VC5509DSP处理器作为算法实现平台,它具有运行功率低,计算能力强的特点,非常适合应用于穿戴式设备。本文最后介绍了CCS集成开发环境测试检测算法性能的方法,通过profile工具计算执行时间可以得知算法的执行效率。实验结果表明R波检测和P波检测算法在TMS320VC5509DSP处理器上可以高效率运行。
Telemedicine system have provided an effective solution to tackle the problem of scarcity in medical resources for the society. Due to the inconvenient and uncomfortable process of measurement in system’s client, which make telemedicine system cannot be widely put into practice. In this paper, a new medical device that called wearable telemedicine system is introduced. Without going to hospital, patient can take daily care service under the help of this system. Meanwhile, the ECG signal detection algorithms on the client of wearable telemedicine systems are discussed, and then a R-wave detection and P-wave detection algorithm are proposed for real-time detecting.
First, the pre-processing procedure include: a smooth filter to filter 50 Hz frequency interference, wavelet transform to remove baseline drift and high frequency noise (mainly through set high-scale approximation coefficient and low-scale detail coefficient to reconstruct signal).
Then this paper focus on the ECG feature extraction methods. After analyzing the principle of wavelet transform in ECG signal singular point detection, db3 wavelet is used to decompose the signal, and then self-adaptive threshold, refractory period and compensation strategies are put into practice to detect R wave on 22 and 23 wavelet coefficients. The experiments’results show that average correct detection rate of this algorithm is above 99%.
Based on this algorithm, P wave can be detected by self-adaptive threshold estimation after attenuating the amplitude of QRS-T peak in order to highlight the energy of P wave. Similarly, the experiments results show that detection rate of this algorithm in some signals can reach 90%. Besides, this paper dive into automatic analysis technology of some arrhythmia cases. The indicators of bradycardia and tachycardia have been introduced.
In this project the ECG signal have been processed on the client of wearable telemedicine system, in which kernel chip is TMS320VC5509DSP processor. This processor with low power and strong calculation ability has been regarded as suitable for wearable medical device. Finally, the method which used to test algorithm in CCS IDE have been elaborated. Profile tool is able to calculate the operating time from which the operation efficiency of algorithm can be obtained. Experiment results show that R-wave and P-wave detection algorithm can be operated in TMS320VC5509DSP processor efficiently.
首先对ECG信号进行预处理,采用平滑滤波器来滤除50Hz工频干扰,然后对信号进行小波变换,通过将高尺度的逼近信号和低尺度的细节信号置零并重构信号,达到去除基线漂移和高频噪声的目的。
接着本文重点研究了心电特征信息提取方法。在分析了小波变换检测心电信号奇异点的原理之后,采用Daubechies3阶小波对预处理后的ECG信号进行分解,在22和23两个小波系数上使用自适应阈值、不应期和补偿等策略对心电信号进行R波检测。实验结果显示,算法的平均正确检测率达到了99%以上。
P波检测的方法是在R波定位完成之后,将QRS-T波对消掉,使得原始信号仅剩P波为主要成分,然后对这部分信号进行自适应阈值判断,检测出P波位置。实验结果显示本算法对某些信号的检测效率达到了90%。另外本文还研究了部分心律失常病例的自动分析技术,介绍了心动过缓、心动过速等病例的判断指标。
课题使用TMS320VC5509DSP处理器作为算法实现平台,它具有运行功率低,计算能力强的特点,非常适合应用于穿戴式设备。本文最后介绍了CCS集成开发环境测试检测算法性能的方法,通过profile工具计算执行时间可以得知算法的执行效率。实验结果表明R波检测和P波检测算法在TMS320VC5509DSP处理器上可以高效率运行。
Telemedicine system have provided an effective solution to tackle the problem of scarcity in medical resources for the society. Due to the inconvenient and uncomfortable process of measurement in system’s client, which make telemedicine system cannot be widely put into practice. In this paper, a new medical device that called wearable telemedicine system is introduced. Without going to hospital, patient can take daily care service under the help of this system. Meanwhile, the ECG signal detection algorithms on the client of wearable telemedicine systems are discussed, and then a R-wave detection and P-wave detection algorithm are proposed for real-time detecting.
First, the pre-processing procedure include: a smooth filter to filter 50 Hz frequency interference, wavelet transform to remove baseline drift and high frequency noise (mainly through set high-scale approximation coefficient and low-scale detail coefficient to reconstruct signal).
Then this paper focus on the ECG feature extraction methods. After analyzing the principle of wavelet transform in ECG signal singular point detection, db3 wavelet is used to decompose the signal, and then self-adaptive threshold, refractory period and compensation strategies are put into practice to detect R wave on 22 and 23 wavelet coefficients. The experiments’results show that average correct detection rate of this algorithm is above 99%.
Based on this algorithm, P wave can be detected by self-adaptive threshold estimation after attenuating the amplitude of QRS-T peak in order to highlight the energy of P wave. Similarly, the experiments results show that detection rate of this algorithm in some signals can reach 90%. Besides, this paper dive into automatic analysis technology of some arrhythmia cases. The indicators of bradycardia and tachycardia have been introduced.
In this project the ECG signal have been processed on the client of wearable telemedicine system, in which kernel chip is TMS320VC5509DSP processor. This processor with low power and strong calculation ability has been regarded as suitable for wearable medical device. Finally, the method which used to test algorithm in CCS IDE have been elaborated. Profile tool is able to calculate the operating time from which the operation efficiency of algorithm can be obtained. Experiment results show that R-wave and P-wave detection algorithm can be operated in TMS320VC5509DSP processor efficiently.
引文
[1]孟兆辉,白净等.穿戴式技术在远程医疗领域的进展及应用[J].世界医疗器械,2006,12(1):49-51.
[2] Jiang He M.D, Ph.D., Dongfeng Gu, M.D etc. Major Causes of Death among Men and Women in China[J]. The New England journal of medicine 2005, 9:1127-1133.
[3]胡大一.第14届世界心脏病学学术大会报道[C].首届国际颈动脉冠脉介入治疗演示及心力衰竭现代治疗模式培训课程,2002,73-89.
[4]李亚军.远程医疗在国内的应用和发展[J].医学信息,2006年1月第19卷第1期.
[5] 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.
[6]胡新平.基于Internet远程医疗诊断系统[J].中国交通医学杂志.2004,18(6):777-779.
[7]简晓瑜.基于P2P的远程医疗会诊系统的研究与实现[J].计算机应用,2006 ,26 (7):1747-1750.
[8]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.
[9]徐庐生,唐慧明.从信息技术看我国远程医疗的发展[J].中国医疗器械信息.2006,12(1):33-37.
[10]王明刚,姬光荣,毛英军.远程医疗技术的发展和应用[J].使用医药杂志,2006,23(3):359-362.
[11]方未艾. 3G技术将给中国远程医疗带来契机[J].中国医疗器械杂志.2006,30(1):31-32.
[12]程佩青.数字信号处理教程.第二版[M].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[M].北京:电子工业出版社, 2004,7:1-7.
[14]骆合德,冯金忠主编.心电图简明教程[M].北京:军事医学科学出版社,2007.
[15]田玉荣.比较两种去除基线漂移的滤波算法[J].中国医学物理学杂志,第15卷,第3期1998 :181 -182.
[16] Rasiah, A.I, Togneri, R., Attikiouzel, Y. QRS detection using morphological and rhythm information., Proceedings[C], IEEE International Conference on Neural Networks. Nov.27 - Dec.1 1995. Vol. 5:2287-2292.
[17]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[J].清华大学学报(自然科学版), 2004,44(06):852-855.
[18]季虎,毛玲,孙即祥.基于小波变换与形态学运算的R波检测算法[J].计算机应用第26卷第5期2006年5月.
[19]边肇祺,张学工.模式识别.第二版[M].北京:清华大学出版社,2000,1:16-25.
[20]阎平凡,张长水.人工神经网络与模拟进化计算.第一版[M].北京:清华大学出版社, 2000,11:1-12.
[21]于学鸿,许小汉.基于神经网络的波型检测方法[J].生物医学工程学杂志,2000.17(1):59-62.
[22]王守岩,王兴邦,程九华,张立藩.基于小波变换和相关分析的心电信号检测[J].第四军医大学学报, 2000,21(3):320-323.
[23]秦前清,杨宗凯.实用小波分析[M],陕西:西安电子科技出版社,1995,22-23.
[24]胡广书.现代信号处理教程[M].北京:清华大学出版社,2004.
[25]成礼智,王红霞,罗永等.小波的理论与应用[M].北京:科学出版社, 2004.
[26]李翠微,郑崇勋,袁超伟. ECG信号的小波变换检测方法[J].中国生物医学工程学报, 1995,14(01):60-63.
[27]林薇,吴效明,朱维宗,周静.应用小波变换模极大值检测ECG特征点[J].医疗卫生装备.2004年第10期:13-14.
[28]郑小林等.抑制心阻抗信号呼吸基线漂移的小波变换法[J].重庆大学学报,1997 20(5) 58-62.
[29] W Zong, GB Moody, D Jiang. A Robust Open-source Algorithm to Detect Onset and Duration of QRS Complexes[J]. Computers in Cardiology 2003, 30:737-740.
[30] HAN Dinh, DK Kumar, ND Pah, et al. Wavelets for QRS Detection[C]. 2001 Proceedings of the 23rd Annual EMBS International Conference, October 25-28, Istanbul, Turkey: 1883-1887.
[31] Marco Di Rienzo,et al. 2h sequential spectral analysis of arterial blood pressure and pulse interval in free-moving subjects[J]. IEEE Trans on BME.1989, 36(11): 1066-1075.
[32]郑达安等.心理应激性时青年人R-R间期变异性的谱分析[J].生物医学工程学杂志,1997,14(1): 38-45.
[33]杨振野.评价心率变异性的一种新方法[J].中国生物医学工程学报,1998, 1(1): 24-30.
[34]黄国言.一种实用的心律失常分析系统的解决方案[J].计算机工程与设计.2003, 24(8): 87-90.
[35] Fredric M Ham, Soowhan Han. Classification of cardiac arrhythmia using fuzzy. ARTMAP.IEEE Transactions on biomedical engineering. 1996, 43(4): 425-429.
[2] Jiang He M.D, Ph.D., Dongfeng Gu, M.D etc. Major Causes of Death among Men and Women in China[J]. The New England journal of medicine 2005, 9:1127-1133.
[3]胡大一.第14届世界心脏病学学术大会报道[C].首届国际颈动脉冠脉介入治疗演示及心力衰竭现代治疗模式培训课程,2002,73-89.
[4]李亚军.远程医疗在国内的应用和发展[J].医学信息,2006年1月第19卷第1期.
[5] 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.
[6]胡新平.基于Internet远程医疗诊断系统[J].中国交通医学杂志.2004,18(6):777-779.
[7]简晓瑜.基于P2P的远程医疗会诊系统的研究与实现[J].计算机应用,2006 ,26 (7):1747-1750.
[8]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.
[9]徐庐生,唐慧明.从信息技术看我国远程医疗的发展[J].中国医疗器械信息.2006,12(1):33-37.
[10]王明刚,姬光荣,毛英军.远程医疗技术的发展和应用[J].使用医药杂志,2006,23(3):359-362.
[11]方未艾. 3G技术将给中国远程医疗带来契机[J].中国医疗器械杂志.2006,30(1):31-32.
[12]程佩青.数字信号处理教程.第二版[M].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[M].北京:电子工业出版社, 2004,7:1-7.
[14]骆合德,冯金忠主编.心电图简明教程[M].北京:军事医学科学出版社,2007.
[15]田玉荣.比较两种去除基线漂移的滤波算法[J].中国医学物理学杂志,第15卷,第3期1998 :181 -182.
[16] Rasiah, A.I, Togneri, R., Attikiouzel, Y. QRS detection using morphological and rhythm information., Proceedings[C], IEEE International Conference on Neural Networks. Nov.27 - Dec.1 1995. Vol. 5:2287-2292.
[17]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[J].清华大学学报(自然科学版), 2004,44(06):852-855.
[18]季虎,毛玲,孙即祥.基于小波变换与形态学运算的R波检测算法[J].计算机应用第26卷第5期2006年5月.
[19]边肇祺,张学工.模式识别.第二版[M].北京:清华大学出版社,2000,1:16-25.
[20]阎平凡,张长水.人工神经网络与模拟进化计算.第一版[M].北京:清华大学出版社, 2000,11:1-12.
[21]于学鸿,许小汉.基于神经网络的波型检测方法[J].生物医学工程学杂志,2000.17(1):59-62.
[22]王守岩,王兴邦,程九华,张立藩.基于小波变换和相关分析的心电信号检测[J].第四军医大学学报, 2000,21(3):320-323.
[23]秦前清,杨宗凯.实用小波分析[M],陕西:西安电子科技出版社,1995,22-23.
[24]胡广书.现代信号处理教程[M].北京:清华大学出版社,2004.
[25]成礼智,王红霞,罗永等.小波的理论与应用[M].北京:科学出版社, 2004.
[26]李翠微,郑崇勋,袁超伟. ECG信号的小波变换检测方法[J].中国生物医学工程学报, 1995,14(01):60-63.
[27]林薇,吴效明,朱维宗,周静.应用小波变换模极大值检测ECG特征点[J].医疗卫生装备.2004年第10期:13-14.
[28]郑小林等.抑制心阻抗信号呼吸基线漂移的小波变换法[J].重庆大学学报,1997 20(5) 58-62.
[29] W Zong, GB Moody, D Jiang. A Robust Open-source Algorithm to Detect Onset and Duration of QRS Complexes[J]. Computers in Cardiology 2003, 30:737-740.
[30] HAN Dinh, DK Kumar, ND Pah, et al. Wavelets for QRS Detection[C]. 2001 Proceedings of the 23rd Annual EMBS International Conference, October 25-28, Istanbul, Turkey: 1883-1887.
[31] Marco Di Rienzo,et al. 2h sequential spectral analysis of arterial blood pressure and pulse interval in free-moving subjects[J]. IEEE Trans on BME.1989, 36(11): 1066-1075.
[32]郑达安等.心理应激性时青年人R-R间期变异性的谱分析[J].生物医学工程学杂志,1997,14(1): 38-45.
[33]杨振野.评价心率变异性的一种新方法[J].中国生物医学工程学报,1998, 1(1): 24-30.
[34]黄国言.一种实用的心律失常分析系统的解决方案[J].计算机工程与设计.2003, 24(8): 87-90.
[35] Fredric M Ham, Soowhan Han. Classification of cardiac arrhythmia using fuzzy. ARTMAP.IEEE Transactions on biomedical engineering. 1996, 43(4): 425-429.