心脏电信号分析及辅助诊断系统
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摘要
心电图(electrocardiogram,简称ECG)是心脏电活动在体表的综合反映,对心电信号进行分析进而诊断心脏疾病具有重要研究价值。目前,心电信号分析与辅助诊断的研究内容主要涉及四个方面,即数据滤波、特征点识别、病情辅助诊断及信号压缩。针对这几方面国内外学者提出许多分析方法,但还存在着诸多不足,如滤波效果不甚理想、特征点的定位精度有待提高、信号压缩方法有待改进等。另外,在辅助诊断方面大多数学者更倾向于时频分析,而心脏作为一个混沌系统,从非线性动力学的角度分析可以获得更多的信息,在这一方面还有深入研究的空间。作为实现临床心电检测的心电自动分析仪,目前大多体积较大、价格高昂,很难为普通家庭所接受。因此,为推广我国心血管疾病的预防与诊疗工作,研制一套价格低廉的便携式心电检测系统并将其推向市场,是一项十分有意义的工作。基于以上理论研究的需要和现实市场的需求,论文针对心电检测系统开发、心电信号预处理及特征点识别、心电辅助诊断和心电信号压缩四个方面开展研究。
心电检测系统开发的研究:针对目前心电图机价格昂贵不易普及的问题,研制了一种便携式心电信号记录仪,与其他同类产品相比,该记录仪功能更加多样、操作更加灵活,有三种操作方式可选,可满足不同场合的要求;采样参数可调,在待机状态下电量消耗极小。此外,论文独创性地开发了基于智能手机的心电数据分析与管理系统,实现了数据的采集、处理、存储和显示,以及用户信息的数据库管理和数据远程传输,该系统简便易用、利于推广。
心电信号预处理及特征点识别的研究:根据心电分析的实际需要,设计了基于形态学滤波方法和小波滤波方法的滤波器。通过分析表明,形态学滤波器在滤除基线漂移时有其独到的好处,在处理高频信号时则会产生截断误差;而小波滤波器在处理高频干扰时效果显著,对于基线漂移的处理则会损失心电信号P、T波的能量。而后,对两种典型的R波识别方法差分阈值法和小波模极大值法进行讨论,分析了它们的不足。针对以上问题,论文提出了一种基于经验模式分解的R波识别算法,结合数学形态学、小波变换和Hilbert包络算法,实现了高精度的检测,同时在运算过程中可实现心电信号高效去噪功能,与形态学滤波器和小波滤波器相比,在不损失信号质量的前提下可获得更高的信噪比。
心电辅助诊断的研究:验证了非线性动力学方法在心电辅助诊断领域的可行性,通过四种方法重点研究了短时期不同病情的心电信号及其心律变异特性:1)通过计算六种病变信号与正常信号的最大Lyapunov指数,指出心脏运动呈现一种弱混沌状态,对诊断病情有特殊意义,为进一步研究指出方向。2)提出了短时心律变异信号的去趋势波动多重分形半谱分析方法,对正常心律、偶发性心律变异病人和室上性心律变异病人的心律变异信号进行计算,得出多重分形参数α的统计值分布随着病情的变化存在着一定的变化趋势,通过盒状图分析,分类精度可达75%以上。3)对严重室性心律失常病症,分析了其动力学变化特征,提出熵和分形的多参数分析方法,进行了室性心律的实时检测与分类,获得了较高的精度。4)首次将语音信号分析方法中的非线性归正技术引入到心电异常波的诊断当中,通过对比贴近度,对异常波的检测结果令人满意,应用到实际心电工作站中也获得了理想的结果。
心电信号压缩的研究:探讨了心电信号压缩的必要性,对有损压缩嵌入式零树编码算法提出了改进方法,与已有算法相比可达到更高的压缩比;对无损压缩,根据心电信号的弱混沌特性,提出了基于混沌预测的无损压缩方法,并通过实验证明了该方法的有效性。
Electrocardiogram (ECG for short) is the synthetic reflection of the heart electricity on body surface, analyzing the electrocardiogram signal and then diagnosing heart’s diseases has a very important researching value. At present, there are four portions in the research of the ECG signal analyzing and auxiliary diagnosing, which are data filter, character points recognition, classification and recognition of pathological signals and signal compression. According to these portions, scholars bring forward a lot of analysis methods, there are insufficiencies yet. For example, the result of filter is not so fine, the precision of character point location should be heightened, the methods of the signal compression awaiting improved. In addition, most scholars are inclined to time-frequency analyzing on the auxiliary diagnosing, however, the heart is a chaos system, it may achieve more information with by non-linear dynamics analysis, and it has space to research deeply. The automatic analyzing instruments which achieve the clinical electrocardiogram are always too big and expensive, and it is hard for the common families to be accepted. So, for the sake of extending the diagnosis and treat of early cardiopathy, it is a very significant work to develop a cheap and portable electrocardiogram detecting system and put it in the market. Based on the necessary of theory research and the factual requirement of market, this thesis carries on the study in four main aspects which are development of the electrocardiogram detecting system, the preprocession of ECG signals and the character point recognition, the ECG auxiliary diagnosis and the data compression.
Research on the development of the ECG detecting system: aiming at the high price of the electrocardiograph and its hardly popularization, the thesis develops a portable ECG signal recorder. Compared with the other products, this recorder has more functions and is very flexible. There are three operating modes to choice, and it can be used in different situations, the sampling parameter can be adapted, it consumes very low under the idle mode. Basing on the above groundwork, the thesis innovatively achieves the ECG analysis and management system on a smartphone, which realizes the data’s collection, processing, memory and display, even the database management of the users’information and remote transfers.
Research on the preprocession of ECG signal and the character point recognition: based on the realistic requirement of the ECG analyzing, the thesis designs wavelet filter and morphological filter. It indicates after much analysis that the morphological filter has its particular advantage in correcting baseline drift, but it will bring truncation error when disposing high frequency signals; the wavelet filter has distinct effect on high frequency restraining, but will lose P and T wave energy when disposing the drift. Then, the thesis discusses about the two typical R wave detecting methods: differentiator threshold method and wavelet modulus maxima method, whose shortages are discussed. Aiming at the questions above, the thesis puts forward an R wave identifying algorithm which basing on empirical mode decomposition, combining the mathematics morphology, wavelet and the Hilbert algorithm. This algorithm can achieve a higher detecting precision and a batter effect for noises wiping in the process. Compared to the wavelet filter and the morphological filter, the new algorithm can obtain much higher SNR but not lost the signal quality.
Research on the ECG auxiliary diagnosis: the thesis validates the feasibility using the non-linear dynamics in the ECG auxiliary diagnosis field. Through four methods, the thesis studies chiefly on the different ECG signals and their HRV characteristics of different illness in a short time. 1) by calculating the Lyapunov exponent of six states of illness, proves that the heart movement is a weak chaos which has the special meaning to the diagnose, but needs more reseach.2) puts forward the short-time multi-fractal detrended fluctuation half-spectrum analysis of HRV signals, through the calculating, the multi-fractal parameterαof normal arrhythmia, occurrent illness arrhythmia and supraventricular arrhythmia presents a trend, using box-plot, the classifying precision can achieve more than 75%. 3) for malignant ventricular arrhythmia, analyzes its dynamic character, puts forward the multi-parameter method of entropy and fractal to detect the onset and classify these illness, and gets a higher detecting precision. 4) firstly introduces the dynamic time warping from speech recognition to classify ECG abnormal wave, through the compare of close degree, the result of detecting is satisfying, and when used at practical workstation the effect is excellent.
Research on the ECG signals compression: the thesis discusses the necessary of the ECG signals compression, for lossy compression, brings forward an improved zero tree coding algorithm, compared with other algorithm which can achieve higher compression ratio; for lossless compression, according to the weak chaos character of ECG signal, puts forward a based chaos forecasting method, and through the experiment proves the validity of this algorithm.
心电检测系统开发的研究:针对目前心电图机价格昂贵不易普及的问题,研制了一种便携式心电信号记录仪,与其他同类产品相比,该记录仪功能更加多样、操作更加灵活,有三种操作方式可选,可满足不同场合的要求;采样参数可调,在待机状态下电量消耗极小。此外,论文独创性地开发了基于智能手机的心电数据分析与管理系统,实现了数据的采集、处理、存储和显示,以及用户信息的数据库管理和数据远程传输,该系统简便易用、利于推广。
心电信号预处理及特征点识别的研究:根据心电分析的实际需要,设计了基于形态学滤波方法和小波滤波方法的滤波器。通过分析表明,形态学滤波器在滤除基线漂移时有其独到的好处,在处理高频信号时则会产生截断误差;而小波滤波器在处理高频干扰时效果显著,对于基线漂移的处理则会损失心电信号P、T波的能量。而后,对两种典型的R波识别方法差分阈值法和小波模极大值法进行讨论,分析了它们的不足。针对以上问题,论文提出了一种基于经验模式分解的R波识别算法,结合数学形态学、小波变换和Hilbert包络算法,实现了高精度的检测,同时在运算过程中可实现心电信号高效去噪功能,与形态学滤波器和小波滤波器相比,在不损失信号质量的前提下可获得更高的信噪比。
心电辅助诊断的研究:验证了非线性动力学方法在心电辅助诊断领域的可行性,通过四种方法重点研究了短时期不同病情的心电信号及其心律变异特性:1)通过计算六种病变信号与正常信号的最大Lyapunov指数,指出心脏运动呈现一种弱混沌状态,对诊断病情有特殊意义,为进一步研究指出方向。2)提出了短时心律变异信号的去趋势波动多重分形半谱分析方法,对正常心律、偶发性心律变异病人和室上性心律变异病人的心律变异信号进行计算,得出多重分形参数α的统计值分布随着病情的变化存在着一定的变化趋势,通过盒状图分析,分类精度可达75%以上。3)对严重室性心律失常病症,分析了其动力学变化特征,提出熵和分形的多参数分析方法,进行了室性心律的实时检测与分类,获得了较高的精度。4)首次将语音信号分析方法中的非线性归正技术引入到心电异常波的诊断当中,通过对比贴近度,对异常波的检测结果令人满意,应用到实际心电工作站中也获得了理想的结果。
心电信号压缩的研究:探讨了心电信号压缩的必要性,对有损压缩嵌入式零树编码算法提出了改进方法,与已有算法相比可达到更高的压缩比;对无损压缩,根据心电信号的弱混沌特性,提出了基于混沌预测的无损压缩方法,并通过实验证明了该方法的有效性。
Electrocardiogram (ECG for short) is the synthetic reflection of the heart electricity on body surface, analyzing the electrocardiogram signal and then diagnosing heart’s diseases has a very important researching value. At present, there are four portions in the research of the ECG signal analyzing and auxiliary diagnosing, which are data filter, character points recognition, classification and recognition of pathological signals and signal compression. According to these portions, scholars bring forward a lot of analysis methods, there are insufficiencies yet. For example, the result of filter is not so fine, the precision of character point location should be heightened, the methods of the signal compression awaiting improved. In addition, most scholars are inclined to time-frequency analyzing on the auxiliary diagnosing, however, the heart is a chaos system, it may achieve more information with by non-linear dynamics analysis, and it has space to research deeply. The automatic analyzing instruments which achieve the clinical electrocardiogram are always too big and expensive, and it is hard for the common families to be accepted. So, for the sake of extending the diagnosis and treat of early cardiopathy, it is a very significant work to develop a cheap and portable electrocardiogram detecting system and put it in the market. Based on the necessary of theory research and the factual requirement of market, this thesis carries on the study in four main aspects which are development of the electrocardiogram detecting system, the preprocession of ECG signals and the character point recognition, the ECG auxiliary diagnosis and the data compression.
Research on the development of the ECG detecting system: aiming at the high price of the electrocardiograph and its hardly popularization, the thesis develops a portable ECG signal recorder. Compared with the other products, this recorder has more functions and is very flexible. There are three operating modes to choice, and it can be used in different situations, the sampling parameter can be adapted, it consumes very low under the idle mode. Basing on the above groundwork, the thesis innovatively achieves the ECG analysis and management system on a smartphone, which realizes the data’s collection, processing, memory and display, even the database management of the users’information and remote transfers.
Research on the preprocession of ECG signal and the character point recognition: based on the realistic requirement of the ECG analyzing, the thesis designs wavelet filter and morphological filter. It indicates after much analysis that the morphological filter has its particular advantage in correcting baseline drift, but it will bring truncation error when disposing high frequency signals; the wavelet filter has distinct effect on high frequency restraining, but will lose P and T wave energy when disposing the drift. Then, the thesis discusses about the two typical R wave detecting methods: differentiator threshold method and wavelet modulus maxima method, whose shortages are discussed. Aiming at the questions above, the thesis puts forward an R wave identifying algorithm which basing on empirical mode decomposition, combining the mathematics morphology, wavelet and the Hilbert algorithm. This algorithm can achieve a higher detecting precision and a batter effect for noises wiping in the process. Compared to the wavelet filter and the morphological filter, the new algorithm can obtain much higher SNR but not lost the signal quality.
Research on the ECG auxiliary diagnosis: the thesis validates the feasibility using the non-linear dynamics in the ECG auxiliary diagnosis field. Through four methods, the thesis studies chiefly on the different ECG signals and their HRV characteristics of different illness in a short time. 1) by calculating the Lyapunov exponent of six states of illness, proves that the heart movement is a weak chaos which has the special meaning to the diagnose, but needs more reseach.2) puts forward the short-time multi-fractal detrended fluctuation half-spectrum analysis of HRV signals, through the calculating, the multi-fractal parameterαof normal arrhythmia, occurrent illness arrhythmia and supraventricular arrhythmia presents a trend, using box-plot, the classifying precision can achieve more than 75%. 3) for malignant ventricular arrhythmia, analyzes its dynamic character, puts forward the multi-parameter method of entropy and fractal to detect the onset and classify these illness, and gets a higher detecting precision. 4) firstly introduces the dynamic time warping from speech recognition to classify ECG abnormal wave, through the compare of close degree, the result of detecting is satisfying, and when used at practical workstation the effect is excellent.
Research on the ECG signals compression: the thesis discusses the necessary of the ECG signals compression, for lossy compression, brings forward an improved zero tree coding algorithm, compared with other algorithm which can achieve higher compression ratio; for lossless compression, according to the weak chaos character of ECG signal, puts forward a based chaos forecasting method, and through the experiment proves the validity of this algorithm.
引文
[1]中华人民共和国卫生部疾病预防控制局,中国疾病预防控制中心.中国慢性病报告[J/OL], 2006[2008-09-03]. http://www.chinacdc.net.cn/n272442/n272530/n3268871/ index.html.
[2]黄大显.现代心电图学[M].北京:人民军医出版社,1998.
[3]魏太星,魏经汉,魏毅东编著.临床心电图学及图谱第3版[M].郑州:河南,科学技术出版社,1997.
[4]黄宛.临床心电图学第5版[M].北京:人民卫生出版社,1998.
[5]李仁立,王琳.心律失常临床诊治[M].北京:科学技术文献出版社,2001.
[6] Physionet/ecglibrary. A (not so) brief history of electrocardiography[EB/OL], 2008[2008-09-03]. http://www.ecglibrary.com/ecghist.html.
[7] Titomir LI. The remote past and near future of electrocardiography: Viewpoint of a biomedical engineer[C]. Bratisl Lek Listy, 2000;101(5):272-279.
[8]张笑微,冯焕清.低功耗超大存储容量的动态心电记录仪的设计[J].电子技术应用,2004,6:28-29.
[9]李萍,刘国忠.基于USB的12导联同步心电采集系统[J].仪器仪表学报, 2004,25(4):344-346.
[10]董薇,陈霏,宁新宝.心电数据床边采集系统的开发研究[J].电子技术应用,2001,1:19-21.
[11] Haiying Zhou,Kun Mean Hou,Jean Ponsonnaille,Laurent Gineste,Christophe De Vaulx. A Real-Time Continuous Cardiac Arrhythmias Detection System: RECAD[C],Proceedings of the 2005 IEEE,Engineering in Medicine and Biology 27th Annual Conference,2005
[12] DONG Jun, XU Miao, ZHU Hong-hai, LU Wei-feng. Wearable ECG Recognition and Monitor. Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems, 2005.
[13] Escalona QJ, Mitchell RH, Balderson D E, et al. Fast and reliable QRS alignment technique for high frequency analysis of signal averaged ECG[J]. Med. Biol. Eng. Comput, 1993, 31(7):137-142
[14] Jane R, Rix H, Caminal P, et al. Alignment methods for averaging of high resolution cardiac signals: A Comparative Study of Performance[J]. IEEE Trans.Biomed.Eng., 1991,38(6):571-577.
[15]皇甫堪,陈建文,楼生强.现代数字信号处理[M].北京:电子工业出版社,2003.
[16]张贤达.现代信号处理第2版[M].北京:清华大学出版社,2002.
[17]张贤达,保铮.非平稳信号分析与处理[M].北京:国防工业出版社,1998.
[18]杨福生,吕扬生.生物医学信号的处理和识别[M].天津:天津科技翻译出版公司,1997.
[19] Lynn PA. Online digital filters for biological signal: Some fast designs for a small computer[J]. Med.Biol.Eng.,1977,15(4):534-540.
[20] Kunt M, Rey H. Preprocessing of electrocardiograms by digital technique[J]. Signal Processing, 1982,4(1):215-222.
[21] Li Gang, et al. A new adaptive coherent model algorithm for removal of power-line interference[J]. J. Clin. Engin., 1995,20(5):147-150.
[22] Joseph Suresh Paul, M. Ramasubba Reddy, Varadarajan Jagadeesh Kumar. A Transform Domain SVD Filter for Suppression of Muscle Noise Artefacts in Exercise ECG's[J],IEEE Trans.Biomed.Eng., 2000, 47(5):654-663.
[23] J.Lee, K.J.Lee, S.K.Yoo. Development of a new Signal Processing Algorithm based on Independent Component Analysis for Single Channel ECG Data[C]. Proceedings of the 26th Annual International Conference of the IEEE EMBS, San Francisco, 2004.
[24]谭小刚.多抽样率频率抽样FIR数字滤波器设计[J].电子科技大学学报, 2002, 31(5):460-464.
[25]戴虹、曹柏荣、周政新.心电信号中工频干扰的最小二乘估计和消除[J].微计算机信息,2005,21(27):14-15.
[26] Donoho D.L., Johnstone I.M.. Threshold selection for wavelet shrinkage of noisy data, Engineering in Medicine and Biology Society[C]. Proceedings of the 16th Annual International Conference of the IEEE; 1994:A24 - A25.
[27]汤宝平,杨昌棋,谭善文,秦树人.基于平移不变的小波去噪方法及应用[J].重庆大学学报(自然科学版),2002,25(3):1-5.
[28]高清维,李海鹰,庄镇泉,王涛.基于平稳小波变换的心电信号噪声消除方法[J].电子学报,2003, 2:238-240.
[29] Donghui Zhang. Wavelet Approach for ECG Baseline Wander Correction and Noise Reduction[C]. Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 2005.
[30] Li Su, Guoliang Zhao. De-Noising of ECG Signal Using Translation Invariant Wavelet De-Noising Method with Improved Thresholding[C]. Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 2005.
[31] Chu CH,Delp EJ. Impulsive noise suppression and baekground normalization of electrocardiogram signals using morphological operators[J]. IEEE Trans.Biomed.Eng.,1989, 36(2):262-273.
[32] Sun P,Wu QH,Weindling AM, etal. An improved morphological approach to background normalization of ECG signals[J]. IEEE Trans.Biomed.Eng.,2003,50(l):117-121.
[33] Hamllton PS. A comparison of adaptive and nonadaptive filters for reduction of power line interference in the ECG[J]. IEEE Trans.Biomed.Eng.,1996,43(l):105-109.
[34]席涛,杨国胜,汤池.基于自适应滤波的心电图中呼吸信号的提取方法[J].第四军医大学学报,2005,26(9):552-554.
[35]郑小林,王志刚,吴楠,肖岚.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.
[36] Jiapu Pan, et al. A real-time QRS detection algorithm[J]. IEEE.Trans.Biomed.Eng., 1985,32(6):230-237.
[37] Friesen GM, et al. A comparison of the noise sensitivity of nine QRS detection algorithms[J]. IEEE Trans.Biomed.Eng.,1990,37(5):85-91.
[38] Thakor NV, et al. Design and evaluation of QRS and noise detectors of ambulatory ECG monitors[J]. Med.Biol.Eng.Comput., 1982,20(4):709-717.
[39] Fatmi MBE, Wang PP. Fuzzy detector and fuzzy estimator in communication[J]. Fuzzy Set and Possibility Theory, 1981,14(7):35-46.
[40] Mori R D. Computer models of speech using fuzzy algorithms[C]. New York: Plenum Press, 1983,67(10):1081-1092.
[41] Lund K,Christianen E H,Lund B,et al. Recovery of beat-to-beat variation of QRS[J]. Med.Biol. Eng.Comput, 1998,36(9):438-447.
[42] Pan J, Tompkins WJ. A real time QRS detection algorithm[J]. IEEE Trans. Biomed. Eng., 1985, 32(2): 6-30.
[43] Senhadji L, Bellanger JJ, Carrault G, et al. Wavelet analysis of ECG signals.Engineering in Medicine and Biology Society[C]. Proceedings of the 12th Annual International Conference of the IEEE,1990:811-812.
[44]丁哨卫,张作生.基于自适应小波变换的QRS波检测算法[J].中国科学技术大学学报, 1998,28(5):580-586.
[45] Li C, Zheng C, Tai C. Detection of ECG characteristic points using wavelet transforms[J]. IEEE Trans.Biomed.Eng., 1995,42(1):21-28.
[46] Martinez JP, Almeida R, Olmos S, et al. A wavelet-based ECG delineator:evaluation on standard databases[J]. IEEE Trans.Biomed.Eng., 2004,51(4):570-581.
[47] Sahambi JS, Tandon SN, Bhatt RK. Using wavelet transforms for ECG characterization: an on-line digital signal processing system[J]. IEEE Engineering in Medicine and BiologyMagazine, 1997,16 (1):77-83.
[48] Sahambi JS, Tandon SN, Bhatt RK. Quantitative analysis of errors due to power-line interference and base-line drift in detection of onsets and offsets in ECG using wavelets[J]. Medical and Biological Engineering and Computing,1997,35(6):747-751.
[49]余辉,张凯,吕扬生.二次微分小波在心电图QRS波检测中的应用[J].中国医疗器械杂志,2001,25(6): 334-337.
[50]罗小刚,彭承琳,郑小林. ECG信号小波变换与峰谷检测算法的研究[J].北京生物医学工程,2003,22(3):168-171.
[51] D.Cabello, S.Barro, R.Ruiz, E.L.Zapata. Fuzzy clustering: application to the diagnosis of ventricular arrhythmias[C]. Engineering in Medicine and Biology Society, Proceedings of the Annual International Conference of the IEEE,1988, l:5-6.
[52] G.Bortolan, R.Degani, J.L.Willems. ECG classification with neural networks and cluster analysis[C]. Computers in Cardiology 1991,Proceedings,1991:177-180.
[53] T.Stamkopoulos, K.Diamantaras, N.maglaveras. ECG Analysis Using Nonlinear PCA Neural Networks for Ischemia Detection[J]. IEEE Trans.Signal Processing,1998,46(11):3058-3067.
[54] Haijian Zeng, Hai Wu, Jiarui Lin. QRS classification using adaptive Hermite decomposition and radial basis function network[C]. Engineering in Medicine and Biology Society. Proceedings of the 20th Annual International Conference of the IEEE,1998,.1:147-150.
[55] Osowski S, Hoai LT, Markiewicz T. Support vector machine-based expert system for reliable heartbeat recognition. IEEE.Trans.Biomed. Eng., 2004,51(4):582-589.
[56] Small M,Yu D,Simonotto J,et al. Uncovering non-linear struc-ture in human ECG recordings[J]. Chaos Soliton Fract,2002,13: 1755-1762.
[57] Wang ZZ, Ning X B, Zhang Y, et al. Nonlinear dynamical charac-teristics analysis of synchronous 12-lead ECG signals[J]. IEEE EngMed Biol,2000,19:110-115.
[58]王振洲,宁新宝,张宇.同步十二导联心电图信号的关联维分布[J].科学通报, 2000,45(8):873-877.
[59]王振洲,李政,魏义祥.同步12导联ECG信号的Lyapunov指数谱[J].科学通报, 2002,47(19):1469-1472.
[60] Ivanov P Ch,Amaral L A N,Goldberger A L,et al. Multifractality in human heartbeat dynamics[J]. Nature,1999,399:461-465.
[61] Goldberger A L, Amaral L A N, Hausdorff J M, et al. Fractal dynamics in physiology: Alterations with disease and aging[C]. ProcNatl Acad Sci USA, 2002,99:2466-2472.
[62] Wang J, Ning X B, Chen Y. Modulation of heart disease informa-tion to the 12-lead ECG multifractal distribution[J]. Physica A,2003,325(3-4):485-491.
[63] Amaral L A N, Ivanov P Ch, Aoyagi N, et al. Behavioral-inde-pendent features of complex heartbeat dynamics[J]. Phys Rev Lett, 2001,86:6026-6029.
[64] Wang J, Ning X B, Chen Y. Multifractal analysis of electronic cardiogram taken from healthy and unhealthy adult subjects[J]. Physica A,2003,323:561-568.
[65] Wang J, Ning X B, Ma Q L, et al. Multiscale multifractality analysis of a 12-lead electrocardiogram[J]. Phys Rev E,2005,71: 062902.
[66]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.
[67] Priestley M B. Spectral analysis and time series [C] . New York : Academic Press , 1981.
[68] Burg J P. Maximum entropy spectral analysis[C]. The 37th Meeting of Society Exploration Geophysicists . Oklahoma City ,1967.
[69]胡广书.数字信号处理、理论、算法与实现[M] .北京:清华大学出版社, 2003.
[70]廖旺才,胡广书,杨福生.心率变异性的非线性动力学分析及其应用.中国生物医学工程学报,1996,15(3):193-201.
[71] Sergio Cerutti, Ary L. Goldberger, Yoshiharu Yamamoto. Recent Advances in Heart Rate Variability Signal Processing and Interpretation[J]. IEEE Trans.Biomed.Eng., 2006,53(1):1-3.
[72] Schepers HE, Von Beek J HGM, Bassingthwaighte JB. Four methods to estimate the fractal dimension from selfaffine signals[J]. IEEE Eng Med Biol ,1992,11(6):57-64.
[73]刘建平,贺太纲. EEG复杂性测度用于大脑负荷状态的研究[J].生物医学工程学杂志,1997 ,14 (1):33-37.
[74]孟欣,欧阳楷.脑电信号的几个非线性动力学分析方法[J].北京生物医学工程学报,1997,16 (3):135-140.
[75] Pincus SM. Approximate entropy as a measure of system complexity measure[J]. Chaos,1995,5:110-117.
[76]张辉,杨明静.心电图中的非线形动力学特征[J].中国生物医学工程学报,1998 ,17 (3):214-221.
[77]古华光,任维.近似熵及其在心率变异分析中的应用[J].航天医学与医学工程,2000,13 (6):417-421.
[78]张浙亮,吕维雪.心电信号数据压缩技术的发展[J].国外医学生物医学工程分册,1997,20(2):73-79.
[79]赵安,吴宝明.心电数据压缩技术的新进展[J].国外医学生物医学工程分册, 2005,28(2):89-93.
[80]张唯真.生物医学电子学[M].北京:清华大学出版社,1990.
[81] Analog Devices Inc.. AD620 Datasheet[DB/OL]. 2005[2008-09-03]. http://www.analog.com/en/other/militaryaerospace/ad620/products/product.html.
[82] Burr-Brown Corporation. ADS1210 Datasheet[DB/OL]. 2005[2008-09-03]. http://focus.ti.com/docs/prod/folders/print/ads1210.html.
[83] Ramtron Corporation. FM1808 Datasheet[DB/OL]. 2005[2008-09-03]. http://www.ramtron.com.cn/search/PartNumberSearch/fm1808.
[84] Escalona QJ,Mitchell RH,Balderson D E,et al. Fast and reliable QRS alignment technique for high frequency analysis of signal averaged ECG[J]. Med&Biol Eng.Comput, 1993,31(7):137-142.
[85] Jane R,Rix H,Caminal P,et al. Alignment methods for averaging of high resolution cardiac signals:A Comparative Study of Performance[J]. IEEE Trans.Biomed.Eng.,1991, 38(6):571-577.
[86] Alste JAV. Removal of baseline wander and power line interference the ECG by an efficient FIR filter with a reduced number of taps[J]. IEEE Trans.Biomed.Eng., 1985,32(12):1053-1059
[87]李兰玉.一种性能优良的QRS检测电路[J].中国医疗器械杂志,1992,6(16):341-545.
[88]吴水才.一种心电和心室晚电位综合采集电路的设计[J].中国医疗器械杂志. 2000,24(1):20-22.
[89] MIT-BIH database. PhysioBank. 2006[2008-09-03]. http://www.physionet.org/ physiobank/ database.
[90]刘少颖,卢继来等.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(6):852-855.
[91] Jerome Bobin, Jean-Luc Starck. Morphological Component Analysis: An Adaptive Thresholding Strategy[J]. IEEE Transactions on Image Processing, 2007,16(17):2675-2681.
[92]唐常青,吕宏伯,黄铮.数学形态学方法及应用[M].北京,科学出版社, 1990:10-27.
[93]杨福生.小波变换的工程分析与应用[M].北京,科学出版社,1999.
[94]彭玉华.小波变换与工程应用[M].北京,科学出版社,1999.
[95] S. Mallat著,杨力华等译.信号处理的小波导论[M].机械工业出版社,2002.
[96] N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, and H. H. Liu. The empirical mode decomposition and hilbert spectrum for nonlinear and nonstationary time series analysis[J]. Proc. R. Soc. Lond., 1998, 454:903–995.
[97] Babloyantz A, Destexhe A. Is the normal heart a periodic oscillator[C]. Biol Cybern, 1988,58:203-211.
[98] Goldberger A L. Nonlinear dynamics, fractals and chaos: Applications to cardiac electrophysiology. Ann Biomed Eng,1990,18(2):195-203.
[99] Goldberger A L,Rigney D R,Mietus J,et al. Nonlinear dynamics in sudden cardiac death syndrome: Heartrate oscillations and bifurcations. Experientia,1988,44(11-12):983-987.
[100] Pool R. Is it healthy to be chaotic[J]. Science,1989,243:604-607.
[101]刘秉正,彭建华.非线性动力学[M].高等教育出版社,2004.
[102]吕金虎,陆君安,陈士华.混沌时间序列分析及其应用[M],武汉大学出版社,2002
[103] Rosenstein Michael T., Collins James J.. De Luca Carlo. A practical method for calculating largest Lyapunov exponents from small data sets[J]. Physica D: Nonlinear Phenomena. 1993,65(1-2):117-134.
[104] Eckmann J.-P.and, Kamphomt S.O. Lyapunov exponents from time seties[J]. Physical Review A, 1986,34:4971-4979
[105] Chhabra A B, Meneveau C, Jensen R V, et al. Direct determination of the f(alpha) singularity spectrum and its application to fully developed turbulence[J]. Physical Review A, 1989,40(9):5284-5294.
[106] Chhabra A, Jensen R V. Direct determination of the f(alpha) singularity spectrum[J]. Physical Review Letters, 1989,62(12):1327-1330.
[107]刘心东,蒋江民,蒋大宗.利用Lyapunov指数提取心电动态生理及病理信息的研究[J].生物医学工程学杂志,1995,12(3):249-253.
[108] Mohamed I. Owis, Ahmed H. Abou-Zied, Abou-Bakr M. Youssef and Yasser M. Kadah. Study of Features Based on Nonlinear Dynamical Modeling in ECG Arrhythmia Detection and Classification[J]. IEEE Trans.Biomed.Eng., 2002,49(7):733-736.
[109] Kantelhardt J W, Zschiegner S A, Koscielny-bunde E, et al. Multifractal detrended fluctuation analysis of nonstationary time series[J]. Physica A: Statistical Mechanics and its Applications. 2002,316(1-4):87-114.
[110] O?wicimka, Pawe?; Kwapień, Jaros?aw; Dro?d?, Stanis?aw. Wavelet versus detrended fluctuation analysis of multifractal structures[J]. Physical Review E, 2006, 74(1), 016103
[111] Peng CK, Havlin S, Stanley HE, et al. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series[J]. Chaos, 1995, 5 (1): 82-87
[112] Hurst H E.The long-term storage capacity of reservoirs[J]. Transactions of the American Society of Civil Engineer, 1951:116.
[113] C. Bandt, B. Pompe,. Permutation Entropy: A Natural Complexity Measure for TimeSeries[J]. Physical Review Letters, 2002, 88.174102.
[114] W. Hou, G. Feng, W. Dong, and J. Li. A technique for distinguishing dynamical species in the temperature time series of north China[J]. ACTA PHYSICA SINICA,2006, 55(5). 2663-2668.
[115] Y. Cao, W. Tung, J. B. Gao, V. A. Protopopescu, and L. M. Hively. Detecting dynamical changes in time series using the permutation entropy[J]. Physical Review E, 2004, 70.046217.
[116] R. Francois, L. V. Jacques. A Regularization Approach to Fractional Dimension Estimation[C].Malta: Proceeding of Fractals 98, Malta, 1998.
[117] D. Kontos, V. Megalooikonomou, A. Javadi, P. Bakic, and A. Maidment. Classification of Galactograms Using Fractal Properties of the Breast Ductal Network[C]. Biomedical Imaging: Macro to Nano, the 3rd IEEE International Symposium, 2006, pp. 1324-1327.
[118] H. Xie, H. Huang, Z. Wang and J. Yuan. Multifractal Analysis of Ventricular Fibrillation and Ventricular Tachycardia[J]. Journal of Data Acquisition and Processing, 2006, 21(1):69-73.
[119] Nitish V. Thakor, Yi-Sheng Zhu, Kong-Yan Pan. Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm[J]. IEEE Trans. Biomed.Eng., 1990,37(9):837-843
[120] Millet, J., Rieta, J. J., Vilanova, E., Mocholi, A., and Chorro, F. J. Time-Frequency Analysis of a single ECG to discriminate between Ventricular Tachycardia and Ventricular Fibrillation[J]. IEEE Computers in Cardiology, 1999,(26):711-714.
[121] S Ruiz de Gauna, A Lazkano, J Ruiz, E Arinendi. Discrimination between ventricular tachycardia and ventricular fibrillation using the continuous wavelet transform[J]. IEEE Computers in Cardiology, 2004,(31):21-24.
[122] Szi-Wen Chen. A two-stage discrimination of cardiac arrhythmias using a total least squares-based prony modeling algorithm[J]. IEEE Trans. Biomed.Eng., 2000,47(10):1317-1327.
[123] Labib Khadra, Amjed S. Al-Fahoum and Saed Binajjaj. A quantitative analysis approach for cardiac arrhythmia classification using higher order spectral techniques[J]. IEEE Trans. Biomed.Eng., 2005,52(11):1840-1845
[124]陈永彬,王仁华.语文信号处理[M].合肥:中国科学技术人学出版社.1990
[125] Wim Sweldens. The Lifting Scheme:A Construction of Second Generation Wavelets[J]. Technical Report of University of South Carolina, 1995, (6):1-5.
[126]胡广书.现代信号处理教程[M].北京:清华大学出版社,2004.
[127] A. S. Lewis and G. Knowles. Image compression using the 2-D wavelet transform[J]. IEEE Trans. Image Processing, 1992, 1:244-250.
[128] Jerome M. Shapiro. Embedded Image Coding Using Zerotrees of Wavelet Coefficients[J]. IEEE Trans. Sign. Proc,1993,41(12): 3445-3462.
[129] Amir Said, William A. Pearlman. A New, Fast, and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees[C]. IEEE Transactions on Circuits and Systems for Video Technology,1996,6(3) :243-250.
[130] David Taubman. High Performance Scalable Image Compression with EBCOT[J]. IEEE Transactions on Image Processing, 2000,9(7):1158-1170.
[131] David Salomon著,吴乐南等译. Data compression the complete reference(Second Edition)[M].电子工业出版社,2003.
[132] M.Blanco-velasco, F.Cruz-Roldan. ECG compression with retrieved quality guaratnteed. Electron.lett[C]. 2004,40(23):1466-1467.
[133] M.Benzid, F.Marir, A.Boussaad. Fixed percentage of wavelet coefficients to be zeroed for ECG compression[C]. Electron.lett., 2003,39(11):830-831.
[134] Z. Lu, D. Y. Kim, and W. A. Pearlman. Wavelet compression of ECG signals by the set partitioning in hierarchical trees (SPIHT) algorithm[J]. IEEE Trans. Biomed. Eng., 2000,47:849-856.
[135] M. L. Hilton. Wavelet and wavelet packet compression of electrocardiograms[J]. IEEE Trans. Biomed. Eng., 1997,44:394-402.
[136] Z. Lu, D. Y. Kim. Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm[J]. IEEE Trans. Biomed. Eng., 2000,47:849-856.
[137]徐蓓,吴林根.高频心电图临床意义探讨[J].心脏杂志, 2000, 12(2):150
[138]沈慧芳,盛正祥.高频心电图和心室晚电位与心电图异常的临床研究[J].中国现代医学杂志, 2002,12(5):29-34
[139]吕金虎,占勇,陆君安.电力系统短期负荷预测的非线性混沌改进模型[J].中国电机工程学报, 2000, 20(12): 80-83
[140]温权,张勇传,程时杰.负荷预报的混沌时间序列分析方法[J].电网技术, 2001,25(10): 13-16.
[141] Fraser A M. Information and entropy in strange attractors[J]. IEEE Trans. Inform Theory, 1989, 35(2):245-262.
[2]黄大显.现代心电图学[M].北京:人民军医出版社,1998.
[3]魏太星,魏经汉,魏毅东编著.临床心电图学及图谱第3版[M].郑州:河南,科学技术出版社,1997.
[4]黄宛.临床心电图学第5版[M].北京:人民卫生出版社,1998.
[5]李仁立,王琳.心律失常临床诊治[M].北京:科学技术文献出版社,2001.
[6] Physionet/ecglibrary. A (not so) brief history of electrocardiography[EB/OL], 2008[2008-09-03]. http://www.ecglibrary.com/ecghist.html.
[7] Titomir LI. The remote past and near future of electrocardiography: Viewpoint of a biomedical engineer[C]. Bratisl Lek Listy, 2000;101(5):272-279.
[8]张笑微,冯焕清.低功耗超大存储容量的动态心电记录仪的设计[J].电子技术应用,2004,6:28-29.
[9]李萍,刘国忠.基于USB的12导联同步心电采集系统[J].仪器仪表学报, 2004,25(4):344-346.
[10]董薇,陈霏,宁新宝.心电数据床边采集系统的开发研究[J].电子技术应用,2001,1:19-21.
[11] Haiying Zhou,Kun Mean Hou,Jean Ponsonnaille,Laurent Gineste,Christophe De Vaulx. A Real-Time Continuous Cardiac Arrhythmias Detection System: RECAD[C],Proceedings of the 2005 IEEE,Engineering in Medicine and Biology 27th Annual Conference,2005
[12] DONG Jun, XU Miao, ZHU Hong-hai, LU Wei-feng. Wearable ECG Recognition and Monitor. Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems, 2005.
[13] Escalona QJ, Mitchell RH, Balderson D E, et al. Fast and reliable QRS alignment technique for high frequency analysis of signal averaged ECG[J]. Med. Biol. Eng. Comput, 1993, 31(7):137-142
[14] Jane R, Rix H, Caminal P, et al. Alignment methods for averaging of high resolution cardiac signals: A Comparative Study of Performance[J]. IEEE Trans.Biomed.Eng., 1991,38(6):571-577.
[15]皇甫堪,陈建文,楼生强.现代数字信号处理[M].北京:电子工业出版社,2003.
[16]张贤达.现代信号处理第2版[M].北京:清华大学出版社,2002.
[17]张贤达,保铮.非平稳信号分析与处理[M].北京:国防工业出版社,1998.
[18]杨福生,吕扬生.生物医学信号的处理和识别[M].天津:天津科技翻译出版公司,1997.
[19] Lynn PA. Online digital filters for biological signal: Some fast designs for a small computer[J]. Med.Biol.Eng.,1977,15(4):534-540.
[20] Kunt M, Rey H. Preprocessing of electrocardiograms by digital technique[J]. Signal Processing, 1982,4(1):215-222.
[21] Li Gang, et al. A new adaptive coherent model algorithm for removal of power-line interference[J]. J. Clin. Engin., 1995,20(5):147-150.
[22] Joseph Suresh Paul, M. Ramasubba Reddy, Varadarajan Jagadeesh Kumar. A Transform Domain SVD Filter for Suppression of Muscle Noise Artefacts in Exercise ECG's[J],IEEE Trans.Biomed.Eng., 2000, 47(5):654-663.
[23] J.Lee, K.J.Lee, S.K.Yoo. Development of a new Signal Processing Algorithm based on Independent Component Analysis for Single Channel ECG Data[C]. Proceedings of the 26th Annual International Conference of the IEEE EMBS, San Francisco, 2004.
[24]谭小刚.多抽样率频率抽样FIR数字滤波器设计[J].电子科技大学学报, 2002, 31(5):460-464.
[25]戴虹、曹柏荣、周政新.心电信号中工频干扰的最小二乘估计和消除[J].微计算机信息,2005,21(27):14-15.
[26] Donoho D.L., Johnstone I.M.. Threshold selection for wavelet shrinkage of noisy data, Engineering in Medicine and Biology Society[C]. Proceedings of the 16th Annual International Conference of the IEEE; 1994:A24 - A25.
[27]汤宝平,杨昌棋,谭善文,秦树人.基于平移不变的小波去噪方法及应用[J].重庆大学学报(自然科学版),2002,25(3):1-5.
[28]高清维,李海鹰,庄镇泉,王涛.基于平稳小波变换的心电信号噪声消除方法[J].电子学报,2003, 2:238-240.
[29] Donghui Zhang. Wavelet Approach for ECG Baseline Wander Correction and Noise Reduction[C]. Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 2005.
[30] Li Su, Guoliang Zhao. De-Noising of ECG Signal Using Translation Invariant Wavelet De-Noising Method with Improved Thresholding[C]. Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 2005.
[31] Chu CH,Delp EJ. Impulsive noise suppression and baekground normalization of electrocardiogram signals using morphological operators[J]. IEEE Trans.Biomed.Eng.,1989, 36(2):262-273.
[32] Sun P,Wu QH,Weindling AM, etal. An improved morphological approach to background normalization of ECG signals[J]. IEEE Trans.Biomed.Eng.,2003,50(l):117-121.
[33] Hamllton PS. A comparison of adaptive and nonadaptive filters for reduction of power line interference in the ECG[J]. IEEE Trans.Biomed.Eng.,1996,43(l):105-109.
[34]席涛,杨国胜,汤池.基于自适应滤波的心电图中呼吸信号的提取方法[J].第四军医大学学报,2005,26(9):552-554.
[35]郑小林,王志刚,吴楠,肖岚.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.
[36] Jiapu Pan, et al. A real-time QRS detection algorithm[J]. IEEE.Trans.Biomed.Eng., 1985,32(6):230-237.
[37] Friesen GM, et al. A comparison of the noise sensitivity of nine QRS detection algorithms[J]. IEEE Trans.Biomed.Eng.,1990,37(5):85-91.
[38] Thakor NV, et al. Design and evaluation of QRS and noise detectors of ambulatory ECG monitors[J]. Med.Biol.Eng.Comput., 1982,20(4):709-717.
[39] Fatmi MBE, Wang PP. Fuzzy detector and fuzzy estimator in communication[J]. Fuzzy Set and Possibility Theory, 1981,14(7):35-46.
[40] Mori R D. Computer models of speech using fuzzy algorithms[C]. New York: Plenum Press, 1983,67(10):1081-1092.
[41] Lund K,Christianen E H,Lund B,et al. Recovery of beat-to-beat variation of QRS[J]. Med.Biol. Eng.Comput, 1998,36(9):438-447.
[42] Pan J, Tompkins WJ. A real time QRS detection algorithm[J]. IEEE Trans. Biomed. Eng., 1985, 32(2): 6-30.
[43] Senhadji L, Bellanger JJ, Carrault G, et al. Wavelet analysis of ECG signals.Engineering in Medicine and Biology Society[C]. Proceedings of the 12th Annual International Conference of the IEEE,1990:811-812.
[44]丁哨卫,张作生.基于自适应小波变换的QRS波检测算法[J].中国科学技术大学学报, 1998,28(5):580-586.
[45] Li C, Zheng C, Tai C. Detection of ECG characteristic points using wavelet transforms[J]. IEEE Trans.Biomed.Eng., 1995,42(1):21-28.
[46] Martinez JP, Almeida R, Olmos S, et al. A wavelet-based ECG delineator:evaluation on standard databases[J]. IEEE Trans.Biomed.Eng., 2004,51(4):570-581.
[47] Sahambi JS, Tandon SN, Bhatt RK. Using wavelet transforms for ECG characterization: an on-line digital signal processing system[J]. IEEE Engineering in Medicine and BiologyMagazine, 1997,16 (1):77-83.
[48] Sahambi JS, Tandon SN, Bhatt RK. Quantitative analysis of errors due to power-line interference and base-line drift in detection of onsets and offsets in ECG using wavelets[J]. Medical and Biological Engineering and Computing,1997,35(6):747-751.
[49]余辉,张凯,吕扬生.二次微分小波在心电图QRS波检测中的应用[J].中国医疗器械杂志,2001,25(6): 334-337.
[50]罗小刚,彭承琳,郑小林. ECG信号小波变换与峰谷检测算法的研究[J].北京生物医学工程,2003,22(3):168-171.
[51] D.Cabello, S.Barro, R.Ruiz, E.L.Zapata. Fuzzy clustering: application to the diagnosis of ventricular arrhythmias[C]. Engineering in Medicine and Biology Society, Proceedings of the Annual International Conference of the IEEE,1988, l:5-6.
[52] G.Bortolan, R.Degani, J.L.Willems. ECG classification with neural networks and cluster analysis[C]. Computers in Cardiology 1991,Proceedings,1991:177-180.
[53] T.Stamkopoulos, K.Diamantaras, N.maglaveras. ECG Analysis Using Nonlinear PCA Neural Networks for Ischemia Detection[J]. IEEE Trans.Signal Processing,1998,46(11):3058-3067.
[54] Haijian Zeng, Hai Wu, Jiarui Lin. QRS classification using adaptive Hermite decomposition and radial basis function network[C]. Engineering in Medicine and Biology Society. Proceedings of the 20th Annual International Conference of the IEEE,1998,.1:147-150.
[55] Osowski S, Hoai LT, Markiewicz T. Support vector machine-based expert system for reliable heartbeat recognition. IEEE.Trans.Biomed. Eng., 2004,51(4):582-589.
[56] Small M,Yu D,Simonotto J,et al. Uncovering non-linear struc-ture in human ECG recordings[J]. Chaos Soliton Fract,2002,13: 1755-1762.
[57] Wang ZZ, Ning X B, Zhang Y, et al. Nonlinear dynamical charac-teristics analysis of synchronous 12-lead ECG signals[J]. IEEE EngMed Biol,2000,19:110-115.
[58]王振洲,宁新宝,张宇.同步十二导联心电图信号的关联维分布[J].科学通报, 2000,45(8):873-877.
[59]王振洲,李政,魏义祥.同步12导联ECG信号的Lyapunov指数谱[J].科学通报, 2002,47(19):1469-1472.
[60] Ivanov P Ch,Amaral L A N,Goldberger A L,et al. Multifractality in human heartbeat dynamics[J]. Nature,1999,399:461-465.
[61] Goldberger A L, Amaral L A N, Hausdorff J M, et al. Fractal dynamics in physiology: Alterations with disease and aging[C]. ProcNatl Acad Sci USA, 2002,99:2466-2472.
[62] Wang J, Ning X B, Chen Y. Modulation of heart disease informa-tion to the 12-lead ECG multifractal distribution[J]. Physica A,2003,325(3-4):485-491.
[63] Amaral L A N, Ivanov P Ch, Aoyagi N, et al. Behavioral-inde-pendent features of complex heartbeat dynamics[J]. Phys Rev Lett, 2001,86:6026-6029.
[64] Wang J, Ning X B, Chen Y. Multifractal analysis of electronic cardiogram taken from healthy and unhealthy adult subjects[J]. Physica A,2003,323:561-568.
[65] Wang J, Ning X B, Ma Q L, et al. Multiscale multifractality analysis of a 12-lead electrocardiogram[J]. Phys Rev E,2005,71: 062902.
[66]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.
[67] Priestley M B. Spectral analysis and time series [C] . New York : Academic Press , 1981.
[68] Burg J P. Maximum entropy spectral analysis[C]. The 37th Meeting of Society Exploration Geophysicists . Oklahoma City ,1967.
[69]胡广书.数字信号处理、理论、算法与实现[M] .北京:清华大学出版社, 2003.
[70]廖旺才,胡广书,杨福生.心率变异性的非线性动力学分析及其应用.中国生物医学工程学报,1996,15(3):193-201.
[71] Sergio Cerutti, Ary L. Goldberger, Yoshiharu Yamamoto. Recent Advances in Heart Rate Variability Signal Processing and Interpretation[J]. IEEE Trans.Biomed.Eng., 2006,53(1):1-3.
[72] Schepers HE, Von Beek J HGM, Bassingthwaighte JB. Four methods to estimate the fractal dimension from selfaffine signals[J]. IEEE Eng Med Biol ,1992,11(6):57-64.
[73]刘建平,贺太纲. EEG复杂性测度用于大脑负荷状态的研究[J].生物医学工程学杂志,1997 ,14 (1):33-37.
[74]孟欣,欧阳楷.脑电信号的几个非线性动力学分析方法[J].北京生物医学工程学报,1997,16 (3):135-140.
[75] Pincus SM. Approximate entropy as a measure of system complexity measure[J]. Chaos,1995,5:110-117.
[76]张辉,杨明静.心电图中的非线形动力学特征[J].中国生物医学工程学报,1998 ,17 (3):214-221.
[77]古华光,任维.近似熵及其在心率变异分析中的应用[J].航天医学与医学工程,2000,13 (6):417-421.
[78]张浙亮,吕维雪.心电信号数据压缩技术的发展[J].国外医学生物医学工程分册,1997,20(2):73-79.
[79]赵安,吴宝明.心电数据压缩技术的新进展[J].国外医学生物医学工程分册, 2005,28(2):89-93.
[80]张唯真.生物医学电子学[M].北京:清华大学出版社,1990.
[81] Analog Devices Inc.. AD620 Datasheet[DB/OL]. 2005[2008-09-03]. http://www.analog.com/en/other/militaryaerospace/ad620/products/product.html.
[82] Burr-Brown Corporation. ADS1210 Datasheet[DB/OL]. 2005[2008-09-03]. http://focus.ti.com/docs/prod/folders/print/ads1210.html.
[83] Ramtron Corporation. FM1808 Datasheet[DB/OL]. 2005[2008-09-03]. http://www.ramtron.com.cn/search/PartNumberSearch/fm1808.
[84] Escalona QJ,Mitchell RH,Balderson D E,et al. Fast and reliable QRS alignment technique for high frequency analysis of signal averaged ECG[J]. Med&Biol Eng.Comput, 1993,31(7):137-142.
[85] Jane R,Rix H,Caminal P,et al. Alignment methods for averaging of high resolution cardiac signals:A Comparative Study of Performance[J]. IEEE Trans.Biomed.Eng.,1991, 38(6):571-577.
[86] Alste JAV. Removal of baseline wander and power line interference the ECG by an efficient FIR filter with a reduced number of taps[J]. IEEE Trans.Biomed.Eng., 1985,32(12):1053-1059
[87]李兰玉.一种性能优良的QRS检测电路[J].中国医疗器械杂志,1992,6(16):341-545.
[88]吴水才.一种心电和心室晚电位综合采集电路的设计[J].中国医疗器械杂志. 2000,24(1):20-22.
[89] MIT-BIH database. PhysioBank. 2006[2008-09-03]. http://www.physionet.org/ physiobank/ database.
[90]刘少颖,卢继来等.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(6):852-855.
[91] Jerome Bobin, Jean-Luc Starck. Morphological Component Analysis: An Adaptive Thresholding Strategy[J]. IEEE Transactions on Image Processing, 2007,16(17):2675-2681.
[92]唐常青,吕宏伯,黄铮.数学形态学方法及应用[M].北京,科学出版社, 1990:10-27.
[93]杨福生.小波变换的工程分析与应用[M].北京,科学出版社,1999.
[94]彭玉华.小波变换与工程应用[M].北京,科学出版社,1999.
[95] S. Mallat著,杨力华等译.信号处理的小波导论[M].机械工业出版社,2002.
[96] N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, and H. H. Liu. The empirical mode decomposition and hilbert spectrum for nonlinear and nonstationary time series analysis[J]. Proc. R. Soc. Lond., 1998, 454:903–995.
[97] Babloyantz A, Destexhe A. Is the normal heart a periodic oscillator[C]. Biol Cybern, 1988,58:203-211.
[98] Goldberger A L. Nonlinear dynamics, fractals and chaos: Applications to cardiac electrophysiology. Ann Biomed Eng,1990,18(2):195-203.
[99] Goldberger A L,Rigney D R,Mietus J,et al. Nonlinear dynamics in sudden cardiac death syndrome: Heartrate oscillations and bifurcations. Experientia,1988,44(11-12):983-987.
[100] Pool R. Is it healthy to be chaotic[J]. Science,1989,243:604-607.
[101]刘秉正,彭建华.非线性动力学[M].高等教育出版社,2004.
[102]吕金虎,陆君安,陈士华.混沌时间序列分析及其应用[M],武汉大学出版社,2002
[103] Rosenstein Michael T., Collins James J.. De Luca Carlo. A practical method for calculating largest Lyapunov exponents from small data sets[J]. Physica D: Nonlinear Phenomena. 1993,65(1-2):117-134.
[104] Eckmann J.-P.and, Kamphomt S.O. Lyapunov exponents from time seties[J]. Physical Review A, 1986,34:4971-4979
[105] Chhabra A B, Meneveau C, Jensen R V, et al. Direct determination of the f(alpha) singularity spectrum and its application to fully developed turbulence[J]. Physical Review A, 1989,40(9):5284-5294.
[106] Chhabra A, Jensen R V. Direct determination of the f(alpha) singularity spectrum[J]. Physical Review Letters, 1989,62(12):1327-1330.
[107]刘心东,蒋江民,蒋大宗.利用Lyapunov指数提取心电动态生理及病理信息的研究[J].生物医学工程学杂志,1995,12(3):249-253.
[108] Mohamed I. Owis, Ahmed H. Abou-Zied, Abou-Bakr M. Youssef and Yasser M. Kadah. Study of Features Based on Nonlinear Dynamical Modeling in ECG Arrhythmia Detection and Classification[J]. IEEE Trans.Biomed.Eng., 2002,49(7):733-736.
[109] Kantelhardt J W, Zschiegner S A, Koscielny-bunde E, et al. Multifractal detrended fluctuation analysis of nonstationary time series[J]. Physica A: Statistical Mechanics and its Applications. 2002,316(1-4):87-114.
[110] O?wicimka, Pawe?; Kwapień, Jaros?aw; Dro?d?, Stanis?aw. Wavelet versus detrended fluctuation analysis of multifractal structures[J]. Physical Review E, 2006, 74(1), 016103
[111] Peng CK, Havlin S, Stanley HE, et al. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series[J]. Chaos, 1995, 5 (1): 82-87
[112] Hurst H E.The long-term storage capacity of reservoirs[J]. Transactions of the American Society of Civil Engineer, 1951:116.
[113] C. Bandt, B. Pompe,. Permutation Entropy: A Natural Complexity Measure for TimeSeries[J]. Physical Review Letters, 2002, 88.174102.
[114] W. Hou, G. Feng, W. Dong, and J. Li. A technique for distinguishing dynamical species in the temperature time series of north China[J]. ACTA PHYSICA SINICA,2006, 55(5). 2663-2668.
[115] Y. Cao, W. Tung, J. B. Gao, V. A. Protopopescu, and L. M. Hively. Detecting dynamical changes in time series using the permutation entropy[J]. Physical Review E, 2004, 70.046217.
[116] R. Francois, L. V. Jacques. A Regularization Approach to Fractional Dimension Estimation[C].Malta: Proceeding of Fractals 98, Malta, 1998.
[117] D. Kontos, V. Megalooikonomou, A. Javadi, P. Bakic, and A. Maidment. Classification of Galactograms Using Fractal Properties of the Breast Ductal Network[C]. Biomedical Imaging: Macro to Nano, the 3rd IEEE International Symposium, 2006, pp. 1324-1327.
[118] H. Xie, H. Huang, Z. Wang and J. Yuan. Multifractal Analysis of Ventricular Fibrillation and Ventricular Tachycardia[J]. Journal of Data Acquisition and Processing, 2006, 21(1):69-73.
[119] Nitish V. Thakor, Yi-Sheng Zhu, Kong-Yan Pan. Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm[J]. IEEE Trans. Biomed.Eng., 1990,37(9):837-843
[120] Millet, J., Rieta, J. J., Vilanova, E., Mocholi, A., and Chorro, F. J. Time-Frequency Analysis of a single ECG to discriminate between Ventricular Tachycardia and Ventricular Fibrillation[J]. IEEE Computers in Cardiology, 1999,(26):711-714.
[121] S Ruiz de Gauna, A Lazkano, J Ruiz, E Arinendi. Discrimination between ventricular tachycardia and ventricular fibrillation using the continuous wavelet transform[J]. IEEE Computers in Cardiology, 2004,(31):21-24.
[122] Szi-Wen Chen. A two-stage discrimination of cardiac arrhythmias using a total least squares-based prony modeling algorithm[J]. IEEE Trans. Biomed.Eng., 2000,47(10):1317-1327.
[123] Labib Khadra, Amjed S. Al-Fahoum and Saed Binajjaj. A quantitative analysis approach for cardiac arrhythmia classification using higher order spectral techniques[J]. IEEE Trans. Biomed.Eng., 2005,52(11):1840-1845
[124]陈永彬,王仁华.语文信号处理[M].合肥:中国科学技术人学出版社.1990
[125] Wim Sweldens. The Lifting Scheme:A Construction of Second Generation Wavelets[J]. Technical Report of University of South Carolina, 1995, (6):1-5.
[126]胡广书.现代信号处理教程[M].北京:清华大学出版社,2004.
[127] A. S. Lewis and G. Knowles. Image compression using the 2-D wavelet transform[J]. IEEE Trans. Image Processing, 1992, 1:244-250.
[128] Jerome M. Shapiro. Embedded Image Coding Using Zerotrees of Wavelet Coefficients[J]. IEEE Trans. Sign. Proc,1993,41(12): 3445-3462.
[129] Amir Said, William A. Pearlman. A New, Fast, and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees[C]. IEEE Transactions on Circuits and Systems for Video Technology,1996,6(3) :243-250.
[130] David Taubman. High Performance Scalable Image Compression with EBCOT[J]. IEEE Transactions on Image Processing, 2000,9(7):1158-1170.
[131] David Salomon著,吴乐南等译. Data compression the complete reference(Second Edition)[M].电子工业出版社,2003.
[132] M.Blanco-velasco, F.Cruz-Roldan. ECG compression with retrieved quality guaratnteed. Electron.lett[C]. 2004,40(23):1466-1467.
[133] M.Benzid, F.Marir, A.Boussaad. Fixed percentage of wavelet coefficients to be zeroed for ECG compression[C]. Electron.lett., 2003,39(11):830-831.
[134] Z. Lu, D. Y. Kim, and W. A. Pearlman. Wavelet compression of ECG signals by the set partitioning in hierarchical trees (SPIHT) algorithm[J]. IEEE Trans. Biomed. Eng., 2000,47:849-856.
[135] M. L. Hilton. Wavelet and wavelet packet compression of electrocardiograms[J]. IEEE Trans. Biomed. Eng., 1997,44:394-402.
[136] Z. Lu, D. Y. Kim. Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm[J]. IEEE Trans. Biomed. Eng., 2000,47:849-856.
[137]徐蓓,吴林根.高频心电图临床意义探讨[J].心脏杂志, 2000, 12(2):150
[138]沈慧芳,盛正祥.高频心电图和心室晚电位与心电图异常的临床研究[J].中国现代医学杂志, 2002,12(5):29-34
[139]吕金虎,占勇,陆君安.电力系统短期负荷预测的非线性混沌改进模型[J].中国电机工程学报, 2000, 20(12): 80-83
[140]温权,张勇传,程时杰.负荷预报的混沌时间序列分析方法[J].电网技术, 2001,25(10): 13-16.
[141] Fraser A M. Information and entropy in strange attractors[J]. IEEE Trans. Inform Theory, 1989, 35(2):245-262.