心室纤颤检测算法研究
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摘要
近年来,心血管疾病的发病率逐年提高,严重地危及人们的生命安全。心脏的猝死是心律失常中最严重的症状和表现,如果不能采取除颤等及时有效的抢救治疗,心脏猝死将意味着生命的结束。现在已知与心脏有关的猝死绝大部分是由心室纤颤(VF)或持续性心动过速(VT)所引起,其中心室纤颤是最致命的恶性心律失常,而室性心动过速在心率十分高(180bpm以上)的情况下也可能迅速转化为VF,从而导致猝死。因此,准确快速的识别出致命的心律失常具有非常重要的意义。
从目前对心电信图(ECG)号的研究来看,可将其归为非线性动力学的范畴,采用非线性动力学的方法对VF检测算法进行研究具有非常明显的优势。本文从非线性动力学的角度,使用复杂度、近似熵以及自回归(AR)模型等方法对VF检测的算法进行了研究和分析,最后结合多参数复合检测的方法提出了复杂度与多参数复合检测相结合的新算法(简称CPLX&3-Count),在准确性及速度等综合性能方面具有明显的提升,具有相当可观的实用价值,并且开拓了使用多种参数进行VF检测算法研究的新途径。
另外,对可推广应用于VF检测的一些心电相关的算法也进行了研究。在呼吸率检测算法中,对传统的FFT计算频率的方法加入了绝对差值平均的方法,使其性能有了较大改善,并提高了其精度;在使用多道心电检测QRS波的算法中,通过实验加入了对原来参数的修正系数,弥补了其只能用于特定采样频率的缺点。这些算法的研究和改进为后续心室纤颤检测算法的研究者们提供了参考。
In recent years, the incidence of cardiovascular disease increases over the years, seriously endangering the lives of the people. Sudden cardiac death is the most serious symptoms of arrhythmia, which means the end of life if no life-saving treatment such as defibrillation is applied. Nowadays, it is known that the sudden cardiac death is resulting from ventricular fibrillation (VF) or the deterioration of sustained ventricular tachycardia (VT) in most conditions. VF is the most serious and deadly malignant arrhythmia, and VT will turn into VF when heart rate is very high (over 180bpm), which always leads to sudden death. So it is of great significance to detect fatal arrhythmia rapidly and accurately.
Judging from the current research on electrocardiograph (ECG), it can be classified as nonlinear dynamic field. Thus, using the methods of nonlinear dynamics to research the VF detection algorithm has obvious advantage. We use the methods, complexity measure, approximate entropy and autoregressive (AR) modeling, to research and analysis the VF detection algorithms at nonlinear dynamic angle in the thesis. Finaly, combining with the multi-paramater composite detection algorithm, we propose a new algorithm named complexity measure combining with the multi-paramater composite detection algorithm (CPLX&3-Count for short). The overall performance such as accuracy and running time, is obviously improved. The new algorithm has considerable practical value, and starts the new way that uses variety of parameters to detecting VF.
Moreover, we also research some ECG-related algorithms which might be applied to VF detection. In the respiratory rate detection algorithm, we add the average method of absolute deviation to the traditional FFT method to caculate the frequency. The new method improves the performance and accuracy. In the research of the QRS wave detection algorithm using multi-lead ECG, the correction factor got from experiment is used to make up for the original method’s shortcomings that it can be used only in the specific sample frequency. The research and improvement of these algorithms will provide a reference to the follow-up study of ventricular fibrillation detection algorithms.
从目前对心电信图(ECG)号的研究来看,可将其归为非线性动力学的范畴,采用非线性动力学的方法对VF检测算法进行研究具有非常明显的优势。本文从非线性动力学的角度,使用复杂度、近似熵以及自回归(AR)模型等方法对VF检测的算法进行了研究和分析,最后结合多参数复合检测的方法提出了复杂度与多参数复合检测相结合的新算法(简称CPLX&3-Count),在准确性及速度等综合性能方面具有明显的提升,具有相当可观的实用价值,并且开拓了使用多种参数进行VF检测算法研究的新途径。
另外,对可推广应用于VF检测的一些心电相关的算法也进行了研究。在呼吸率检测算法中,对传统的FFT计算频率的方法加入了绝对差值平均的方法,使其性能有了较大改善,并提高了其精度;在使用多道心电检测QRS波的算法中,通过实验加入了对原来参数的修正系数,弥补了其只能用于特定采样频率的缺点。这些算法的研究和改进为后续心室纤颤检测算法的研究者们提供了参考。
In recent years, the incidence of cardiovascular disease increases over the years, seriously endangering the lives of the people. Sudden cardiac death is the most serious symptoms of arrhythmia, which means the end of life if no life-saving treatment such as defibrillation is applied. Nowadays, it is known that the sudden cardiac death is resulting from ventricular fibrillation (VF) or the deterioration of sustained ventricular tachycardia (VT) in most conditions. VF is the most serious and deadly malignant arrhythmia, and VT will turn into VF when heart rate is very high (over 180bpm), which always leads to sudden death. So it is of great significance to detect fatal arrhythmia rapidly and accurately.
Judging from the current research on electrocardiograph (ECG), it can be classified as nonlinear dynamic field. Thus, using the methods of nonlinear dynamics to research the VF detection algorithm has obvious advantage. We use the methods, complexity measure, approximate entropy and autoregressive (AR) modeling, to research and analysis the VF detection algorithms at nonlinear dynamic angle in the thesis. Finaly, combining with the multi-paramater composite detection algorithm, we propose a new algorithm named complexity measure combining with the multi-paramater composite detection algorithm (CPLX&3-Count for short). The overall performance such as accuracy and running time, is obviously improved. The new algorithm has considerable practical value, and starts the new way that uses variety of parameters to detecting VF.
Moreover, we also research some ECG-related algorithms which might be applied to VF detection. In the respiratory rate detection algorithm, we add the average method of absolute deviation to the traditional FFT method to caculate the frequency. The new method improves the performance and accuracy. In the research of the QRS wave detection algorithm using multi-lead ECG, the correction factor got from experiment is used to make up for the original method’s shortcomings that it can be used only in the specific sample frequency. The research and improvement of these algorithms will provide a reference to the follow-up study of ventricular fibrillation detection algorithms.
引文
1. Mason P, et al. The Review of Implantable Cardioverter Defibrillator,Heart &Lung, 1992, 21(2): 141~148
2. International Liaison Committee on Resuscitation. 2005 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science with Treatment Recommendation Circulation,2005; 112:Ⅲ-1-Ⅲ-136
3. Kuo S and Dillman R. Computer Detection of Ventricular Fibrillation,Computer in Cardiology.1978, 9:347~349
4.朱贻盛,李丹,王妙发.心室纤颤信号的自适应检测,生物物理学报, 1994, 10(2): 149~153
5. A, Langer, et al. Consideration in the Development of the Automatic Implantable Defibrillation, Med. Instrum. ,1976, 10(3): 163~167
6. Nitish V. Thakor, Yi-sheng Zhu and Kong-yan Pan, Ventricular Tachycardia and Fibrillation Detection by a Sequential Hypothesis Testing Algorithm, Ibid, IEEE trans., BME-37, 1990: 837~843
7. Nygards M. E, Recognition of Ventricular Fibrillation from the Power Spectrum of the ECG, proc. Computers in Cardiology, IEEE Compute. Society Press. Long Peach, California, 1977: 393~397
8. Auber A. E. et al, Fibrillation Recognition Using Autocorrelation Analysis, proc. Computers in Cardiology, IEEE Comput. Soc. Press, Long Beach, California, 1982: 477~480
9. Kuo S. et al, Computer Detection of Ventricular Fibrillation, Proc. Computers in Cardiology, IEEE Comput. Soc. Press, 1978: 707~710
10. Valtino X. Afonso, et al, Detection Ventricular Fibrillation, IEEE Engineering in Medicine and Biology, 1995; 152~159
11.张红煊,朱贻盛.异常心电节律VT/VF与非线性动力学定性定量分析现状,北京生物医学工程, 2001, Vol.20 No.3: 229~232
12.张红煊,朱贻盛,王自明,李颖洁.基于复杂度和复杂率的心动过速和心室纤颤检测.中国生物医学工程学报, 2001, Vol.20 No.5: 423~429
13. Zhang Xusheng, Zhu Yisheng, Thakor NV. Detecting VentricularTachycardia and Fibrillation by Complexity Measure, IEEE Trans Biomed Eng, 1999, 46(5): 548~555
14. A. Lempel and J. Ziv. On the Complexity of Finite Sequences, IEEE Trans. Inform. Theory, Jan. 1976, vol. IT-22: 75~81
15. Caswell SA, et al. Approximate Entropy as a Measure of Morphologic Variability for Ventricular Tachycardia and Fibrillation. Computer in Cardiology, 1998, Vol.25: 265~268
16.洪波,唐庆玉,杨福生,陈天祥.近似商、互近似熵的性质、快速算法及其在脑电与认知研究中的初步应用.信号处理, 1999, Vol.15 No.2: 100~108
17. Andre E. Aubert, et al. Recognition of Ventricular Fibrillation Tachycardia from Electrogram Analysis. Computers in Cardiology 1988. Proceedings: 341~344
18. Anton Amann, Robert Tratnig, Karl Unterkofler. A New Quantitative Analysis Technique for Cardiac Arrhythmia Classification Using Bispectrum and Bicoherency.Conf Proc IEEE Eng Med Biol Soc 1: 13-6, 2004
19. Cuiwei Li, Chongxun Zheng, Changfeng Tai, Detection of ECG Characteristic Points Using Wavelet Transforms. IEEE Trans Biomed. Eng, 1995, Vol. 42, No.1: 21~29
20.黄忠朝,陈裕泉,潘敏,唐娜.独立分量分析在心房纤颤检测中的应用研究,浙江大学学报(工学版), 2006, Vol. 40 No.2: 360~364
21. Dingfei Ge, Shankar M Krihnan. Cardiac Arrhythmia Classification Using Autoregressive Modeling. BioMed. Eng. Online, 2002 1:5
22.张绪省,朱贻盛.利用多种描述信息检测心室纤颤的综合系统.中国医疗器械杂志, 1997, Vol.21 No.6: 321~326
23. R. H. Clayton, A. Murray, R. W. F. Campbell, Comparison of four Techniques for Recognition of Ventricular Fibrillation from the Surface ECG. Med.&Bio. Eng. &computing, 1993: 111~117
24. DI Robert Tratnig, Reliability of New Fibrillation Detection Algorithms for Automated External Defibrillators (AEDs), Fakult?t für Technische Mathematik and Technische Physik, PHD thesis, 2005
25. Irena Jekova and Vessela Krasteva, Real time Detection of VentricularFibrillation and Tachycardia, Physiol. Meas. 25(2004): 1167~1178
26. N. H. Packard, J. P. Crutchfield, J. D. Farmer and R. S. Shaw. Geometry from a Time Series. Phys. Rev. Lett., 1980, 45: 712~716
27. P. Grassberger, I. Procaccia. Measuring the Strangeness of Strange Attractors. Physical D, 1983, 9: 189~208
28. Steven M. Pincus. Approximate Entropy as a Measure of System Complexity. Proc. NatI. Acad. Sci.USA, 1991, 88: 2297~2301
29.陈芳,顾凡及,徐京华等.一种新的人脑信息传输复杂性的研究.生物物理学报, 1998, 14(3):508~512
30. Wolf A, Swift J, Swinney H, et al. Determining Lyapunov Exponents from a Time Series. Physical D, 1985, 16: 285~317
31. Glenny R W, Robertson H T, Yamashiro S, et al. Application of Fractal Analysis to Physiology. J. Appl. Physiol, 1991,70: 2351~2367
32. Akselrod S, Gordon D, Ubel F A, et al. Power Spectrum Analysis of Heart rate Cuctuation: a Quantitative Probe of Beat-to-Beat Cardiovascular Control. Science, 1981, 213: 220~222
33. Zhang H, Ge J G. Lyapunov Spectrum of RR Intervals Series in Dynamic Electrocardiogram. Journal of Zhejiang University, 1999, 33(6): 654~659
34.张红煊,朱贻盛,王自明.异常心电信号VT和VF的分析与检测.中国医疗器械杂志, 2001, 25(4): 187~191
35. Takens, F. Detecting Strange Attractors in Turbulence. In: Rand, D A and YOUNG, L. S. (Eds). Dynamical System and Turbulence. Lecture Notes in Mathematics Springer, Berlin, 1981: 366~387
36.谢惠民.复杂性与动力系统.上海,上海科技教育出版社, 1994: 186~211
37.杨福生,廖旺才.近似熵:一种适应于短数据的复杂性度量.中国医疗器械杂志, 1997, 21(5): 283~286
38. GE Ding-Fei1, HOU Bei-Ping1, XIANG Xin-Jian. Study of Feature Extraction Based on Autoregressive Modeling in ECG Automatic Diagnosis. ACTA AUTOMATICA SINICA, May, 2007
39.葛丁飞,李时辉.基于ARMA模型的ECG分类和压缩.浙江科技学院学报, 2004, 16(1)
40.汪源源,邵谦明,刘冀,王威琪.采用“绝对差值平均算法”进行超声多普勒胎心率的检测.中国医疗器械杂志, 1991, Vol.02: 72~75
41. F. Gritzali. Towards a Generalized Scheme for QRS Detection in ECG Waveforms. Signal Processing, 1988, vol. 15: 183~192
42. Donlad P. Gilden, at al. Vectan II: A Computer Program for the Spatial Analysis of the Vectorcardiogram, J. Electrocardiology 1975, 8(3):217~225
43. M.L.Simoons, et al. On-line processing of orthogonal exercise electrocardiograms. Computer and biomedical research 1975 Apr, 8(2):105~107
44. J. H. van Bemmel, et al. Template Waveform Recognition Applied to ECG/VCG Analysis. Computer and biomedical research 1973, 6:430~441
45. Zhao Jie, Han Yue-qiu. Processing of 8-Channel Synchronous Electro-Cardiograph, Journal of Beijing Institute of Technology, 2002, Vol.11, No.1: 66~70
46. Bakardjian H: Ventricular Bear Classifier Using Fractal Number Clustering. Med. &Bio. Eng. &computing, 1992, 30: 495~502
47. Ivaylo I Christov. Real Time Electrocardiogram QRS Detection Using Combined Adaptive Threshold. Biomed Eng Online. 2004, 3: 28~36
2. International Liaison Committee on Resuscitation. 2005 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science with Treatment Recommendation Circulation,2005; 112:Ⅲ-1-Ⅲ-136
3. Kuo S and Dillman R. Computer Detection of Ventricular Fibrillation,Computer in Cardiology.1978, 9:347~349
4.朱贻盛,李丹,王妙发.心室纤颤信号的自适应检测,生物物理学报, 1994, 10(2): 149~153
5. A, Langer, et al. Consideration in the Development of the Automatic Implantable Defibrillation, Med. Instrum. ,1976, 10(3): 163~167
6. Nitish V. Thakor, Yi-sheng Zhu and Kong-yan Pan, Ventricular Tachycardia and Fibrillation Detection by a Sequential Hypothesis Testing Algorithm, Ibid, IEEE trans., BME-37, 1990: 837~843
7. Nygards M. E, Recognition of Ventricular Fibrillation from the Power Spectrum of the ECG, proc. Computers in Cardiology, IEEE Compute. Society Press. Long Peach, California, 1977: 393~397
8. Auber A. E. et al, Fibrillation Recognition Using Autocorrelation Analysis, proc. Computers in Cardiology, IEEE Comput. Soc. Press, Long Beach, California, 1982: 477~480
9. Kuo S. et al, Computer Detection of Ventricular Fibrillation, Proc. Computers in Cardiology, IEEE Comput. Soc. Press, 1978: 707~710
10. Valtino X. Afonso, et al, Detection Ventricular Fibrillation, IEEE Engineering in Medicine and Biology, 1995; 152~159
11.张红煊,朱贻盛.异常心电节律VT/VF与非线性动力学定性定量分析现状,北京生物医学工程, 2001, Vol.20 No.3: 229~232
12.张红煊,朱贻盛,王自明,李颖洁.基于复杂度和复杂率的心动过速和心室纤颤检测.中国生物医学工程学报, 2001, Vol.20 No.5: 423~429
13. Zhang Xusheng, Zhu Yisheng, Thakor NV. Detecting VentricularTachycardia and Fibrillation by Complexity Measure, IEEE Trans Biomed Eng, 1999, 46(5): 548~555
14. A. Lempel and J. Ziv. On the Complexity of Finite Sequences, IEEE Trans. Inform. Theory, Jan. 1976, vol. IT-22: 75~81
15. Caswell SA, et al. Approximate Entropy as a Measure of Morphologic Variability for Ventricular Tachycardia and Fibrillation. Computer in Cardiology, 1998, Vol.25: 265~268
16.洪波,唐庆玉,杨福生,陈天祥.近似商、互近似熵的性质、快速算法及其在脑电与认知研究中的初步应用.信号处理, 1999, Vol.15 No.2: 100~108
17. Andre E. Aubert, et al. Recognition of Ventricular Fibrillation Tachycardia from Electrogram Analysis. Computers in Cardiology 1988. Proceedings: 341~344
18. Anton Amann, Robert Tratnig, Karl Unterkofler. A New Quantitative Analysis Technique for Cardiac Arrhythmia Classification Using Bispectrum and Bicoherency.Conf Proc IEEE Eng Med Biol Soc 1: 13-6, 2004
19. Cuiwei Li, Chongxun Zheng, Changfeng Tai, Detection of ECG Characteristic Points Using Wavelet Transforms. IEEE Trans Biomed. Eng, 1995, Vol. 42, No.1: 21~29
20.黄忠朝,陈裕泉,潘敏,唐娜.独立分量分析在心房纤颤检测中的应用研究,浙江大学学报(工学版), 2006, Vol. 40 No.2: 360~364
21. Dingfei Ge, Shankar M Krihnan. Cardiac Arrhythmia Classification Using Autoregressive Modeling. BioMed. Eng. Online, 2002 1:5
22.张绪省,朱贻盛.利用多种描述信息检测心室纤颤的综合系统.中国医疗器械杂志, 1997, Vol.21 No.6: 321~326
23. R. H. Clayton, A. Murray, R. W. F. Campbell, Comparison of four Techniques for Recognition of Ventricular Fibrillation from the Surface ECG. Med.&Bio. Eng. &computing, 1993: 111~117
24. DI Robert Tratnig, Reliability of New Fibrillation Detection Algorithms for Automated External Defibrillators (AEDs), Fakult?t für Technische Mathematik and Technische Physik, PHD thesis, 2005
25. Irena Jekova and Vessela Krasteva, Real time Detection of VentricularFibrillation and Tachycardia, Physiol. Meas. 25(2004): 1167~1178
26. N. H. Packard, J. P. Crutchfield, J. D. Farmer and R. S. Shaw. Geometry from a Time Series. Phys. Rev. Lett., 1980, 45: 712~716
27. P. Grassberger, I. Procaccia. Measuring the Strangeness of Strange Attractors. Physical D, 1983, 9: 189~208
28. Steven M. Pincus. Approximate Entropy as a Measure of System Complexity. Proc. NatI. Acad. Sci.USA, 1991, 88: 2297~2301
29.陈芳,顾凡及,徐京华等.一种新的人脑信息传输复杂性的研究.生物物理学报, 1998, 14(3):508~512
30. Wolf A, Swift J, Swinney H, et al. Determining Lyapunov Exponents from a Time Series. Physical D, 1985, 16: 285~317
31. Glenny R W, Robertson H T, Yamashiro S, et al. Application of Fractal Analysis to Physiology. J. Appl. Physiol, 1991,70: 2351~2367
32. Akselrod S, Gordon D, Ubel F A, et al. Power Spectrum Analysis of Heart rate Cuctuation: a Quantitative Probe of Beat-to-Beat Cardiovascular Control. Science, 1981, 213: 220~222
33. Zhang H, Ge J G. Lyapunov Spectrum of RR Intervals Series in Dynamic Electrocardiogram. Journal of Zhejiang University, 1999, 33(6): 654~659
34.张红煊,朱贻盛,王自明.异常心电信号VT和VF的分析与检测.中国医疗器械杂志, 2001, 25(4): 187~191
35. Takens, F. Detecting Strange Attractors in Turbulence. In: Rand, D A and YOUNG, L. S. (Eds). Dynamical System and Turbulence. Lecture Notes in Mathematics Springer, Berlin, 1981: 366~387
36.谢惠民.复杂性与动力系统.上海,上海科技教育出版社, 1994: 186~211
37.杨福生,廖旺才.近似熵:一种适应于短数据的复杂性度量.中国医疗器械杂志, 1997, 21(5): 283~286
38. GE Ding-Fei1, HOU Bei-Ping1, XIANG Xin-Jian. Study of Feature Extraction Based on Autoregressive Modeling in ECG Automatic Diagnosis. ACTA AUTOMATICA SINICA, May, 2007
39.葛丁飞,李时辉.基于ARMA模型的ECG分类和压缩.浙江科技学院学报, 2004, 16(1)
40.汪源源,邵谦明,刘冀,王威琪.采用“绝对差值平均算法”进行超声多普勒胎心率的检测.中国医疗器械杂志, 1991, Vol.02: 72~75
41. F. Gritzali. Towards a Generalized Scheme for QRS Detection in ECG Waveforms. Signal Processing, 1988, vol. 15: 183~192
42. Donlad P. Gilden, at al. Vectan II: A Computer Program for the Spatial Analysis of the Vectorcardiogram, J. Electrocardiology 1975, 8(3):217~225
43. M.L.Simoons, et al. On-line processing of orthogonal exercise electrocardiograms. Computer and biomedical research 1975 Apr, 8(2):105~107
44. J. H. van Bemmel, et al. Template Waveform Recognition Applied to ECG/VCG Analysis. Computer and biomedical research 1973, 6:430~441
45. Zhao Jie, Han Yue-qiu. Processing of 8-Channel Synchronous Electro-Cardiograph, Journal of Beijing Institute of Technology, 2002, Vol.11, No.1: 66~70
46. Bakardjian H: Ventricular Bear Classifier Using Fractal Number Clustering. Med. &Bio. Eng. &computing, 1992, 30: 495~502
47. Ivaylo I Christov. Real Time Electrocardiogram QRS Detection Using Combined Adaptive Threshold. Biomed Eng Online. 2004, 3: 28~36