可穿戴生理参数监测系统的动态心电信号处理方法研究
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摘要
随着人类健康和保健意识的增强,医疗仪器也从适用于医院的复杂、大型设备,转向一些小巧、便携并适用于家庭和个人的穿戴式装置。由吉林大学仪器学院自主研发的可穿戴生理参数仪具有无创、实时、长时间记录人体的体温、血压、血氧值、心电等参数的功能。本论文基于此项目,对动态心电信号的预处理、波形检测和辅助诊断等关键问题进行了深入的研究,针对现有算法的局限性,提出了适用于本系统的算法。
通过分析动态心电信号的特点,确定使用小波分析方法来进行心电信号的消噪与波形检测。在动态心电信号的预处理方面:提出了先使用高尺度小波分解法去除基线漂移,再使用5级平稳小波变换,Symmlet10小波基,配合Ebayes阈值法消除其他噪声的心电信号综合消噪算法,结果证明,综合算法的消噪效果优于单一算法的消噪效果,不仅有效地清除了心电信号中的噪声,还较好地保护了心电信号中的特征点。在波形检测方面:提出了一种基于小波模极大值的快速QRS波检测算法,通过有效性测试,检测精度高,运算速度快,并基于此算法,实现了P波与T波的定位、ST段改变的检测。最后,设计了可穿戴生理参数监测诊断系统的总体架构,实现了心电辅助诊断模块软件系统,并根据动态心电图的诊断标准,量化了常见心律失常病症的分类规则,实现了正常心电图与常见心律失常病症的分类诊断。
Recently, many factors affect human health, such as acceleration of life pace, increasement of work pressure, and decrease of physical activities, et al. Meanwhile, people’s awareness in health is growing, so the medical equipements should be developed in varity styles, not only the larg-scale and complex equipements in hospital, but there is a need to develop small, portable and wearable monitoring devices for families, individuals, and community, therefore, it’s significance and valuable to study on wearable medical devices with monitoring and early-warning functions.
A wearable monitoring physiological parameters system is developed by Jilin University, from a key project‘wearable physiological parameters Non-invasive continuous monitoring device (20070333)’, which is sponsored by Jilin Province Science and Technology Department. This device can do the long-time, real-time and non-invasive measurement of physiological temperature, blood pressure, oxygen values, and dynamic electrocardiogram (DCG). DCG automatic analysis is particularly important among these parameters, so it’s crutial to study on DCG analysis.
DCG de-noising, waveform detection and assistant diagnosis are studied, respectively, and due to the limitations of current processing methods in DCG automatic domain, this study proposed some applicable algorithms for our system. Firstly, determined to use wavelet analysis method for DCG de-noising and waveform detection by anlysis the characteristics of DCG signals.
Secondly, studied the wavelet thresholding theory systemtically, and summarized five key factors which influence the de-noising effect: wavelet transform(WT) techniques, wavelet basis, decomposition levels, shrinkage functions and thresholds, and applied this theory for ECG De-noising.
For WT techniques, using classical WT, wavelet packet, lifting wavelet and stationary wavelet transform (SWT), respectively, through a comprehensive comparison, determined using SWT;
For wavelet basis, through analysis the mathmetical characteristics of eight kinds of wavelet bases, determined the suitable wavelet bases for ECG de-noising theoretically, and then through the experiments, determined that the optimal basis for de-noising ECG signals is Symmlet10 by quantitative assessment;
For wavelet decomposition levels (scales), considered two aspects, one is separation of the valuable information from the noise, the other is reduction of the reconstruction error, and determined the optimal decomposition level is five, in addition, using the changing SNR ECG signals to test its stability;
For shrinkage functions, compared with Soft, Hard, Firm and Garrot functions, each function has its own advantages and disadvantages. However, Hard shrinkage function is the most suitable one for keeping the singular point of the original signal; For thresholds, compared with the commonly used thresholds, such as Minimaxi, Universal, SURE, and Hybrid, in addtion, due to the GCV estimation doesn’t need any priori knowledge of noise energy, and Ebayes estimation is good to access the minimum mean square error, that is, the threshold is close to the optimal threshold.
Through the comprehensive analysis and comparison of the above methods, proposed using SWT with 5-scale decomposition, Sym10 wavelet basis, and Hard with Ebayes thresholding method for de-noising ECG signals, and through testing, the algorithm can reduce muscle noise, power line interference and electrode motion artifacts, but can not remove the baseline wander (BW).
Thirdly, analysis of the bandwidth of BW, and comparison of two efficient methods to remove BW: using median filtering and using high-scale wavelet decomposition estimation (WTSE). The results showed that the WTSE method is better than median filtering method by comparison of SNR improvement and singular points maintaining.
If using high-scale decomposition in single thresholding method, directly, it can also achieve the purpose of BW removal through eliminating the high scale coefficients, but, on the one hand, following the increase of decomposition scales, except the BW, other noise removal will be affected, on the other hand, the execute time will increase.Therefore, proposed a combined algorithm, using WTSE method to remove BW, and then using SWT threhsolding algorithm to reduce other noise. The de-noising results proved that the combined algorithm is better than the single de-noising methods, it not only removes noise effectively, but better protects the feature points.
Fourthly, QRS complex detection is crucial in ECG automatic analysis, including the R-wave peak and the QRS onset and offset detection. There are three typical R-wave detection algorithms based on differential, digital filter and wavelet transform, respectively. No matter what kind of algorithm was used, all need the appropriate threshold, maybe due to the different selection, even though the same method, can lead to the different detection rate. Through analysis the threshold selection rules, adopted a grouped calculation strategy to select threshold, then decrease the false detection rate because of the sudden waveform variations. Using the typical data from MIT-BIH Arrhythmia database to assess the effectiveness, the anti-interference ability, and the execute time, the above algorithms have their own advantages and disadvantages. Wavelet modulus maximum (WMM) method obtained the highest detection accuracy and the best anti-interference ability, but, it is too complicated, and the execute time need to be improved. For these limitations, this study improved the traditional WMM method, proposed a fast algorithm based on WMM, which adopted two new thresholds,and eliminated the redundant steps from the traditional WMM algorithm. In addition, using MIT-BIH Arrhythmia database to assess the effectiveness and the execute time, the results proved that the proposed algorithm not only obtained the better detection accuracy, but improved the execute time. Based on this algorithm, in scale 2, implemented the QRS complex onset and offset dectection.
Fifthly, P wave and T wave have the following properties, such as smaller amplitude, lower frequency, and morphological diversity, therefore, the existing detection algorithms have great limitations, meanwhile, due to the lack of a unified, open standard evaluation database, so far, P wave and T wave detection are still difficult in the automatic analysis of DCG. Based on the targeted QRS position, according to the frequency characteristics of P wave and T wave, selected the appropriate scale, threshold and time window, detected the peak, the onset and offset of the P-wave and T-wave, respectively.
Finally, designed the structure diagram of the wearable physiological parameters monitoring system, the system includes three main parts: the wearable device for monitoring physical parameters, the user terminal, and the supporting analysis and diagnosis system. According to the DCG diagnostic criteria, listed the mathmetical presentations of the common arrhythmia classification rules, compared the calculated ECG parameters with the above rules, and realized the auto classification of normal electrocardiogram and common arrhythmia beats. This tool can assistant the clinicians to diagnose.
通过分析动态心电信号的特点,确定使用小波分析方法来进行心电信号的消噪与波形检测。在动态心电信号的预处理方面:提出了先使用高尺度小波分解法去除基线漂移,再使用5级平稳小波变换,Symmlet10小波基,配合Ebayes阈值法消除其他噪声的心电信号综合消噪算法,结果证明,综合算法的消噪效果优于单一算法的消噪效果,不仅有效地清除了心电信号中的噪声,还较好地保护了心电信号中的特征点。在波形检测方面:提出了一种基于小波模极大值的快速QRS波检测算法,通过有效性测试,检测精度高,运算速度快,并基于此算法,实现了P波与T波的定位、ST段改变的检测。最后,设计了可穿戴生理参数监测诊断系统的总体架构,实现了心电辅助诊断模块软件系统,并根据动态心电图的诊断标准,量化了常见心律失常病症的分类规则,实现了正常心电图与常见心律失常病症的分类诊断。
Recently, many factors affect human health, such as acceleration of life pace, increasement of work pressure, and decrease of physical activities, et al. Meanwhile, people’s awareness in health is growing, so the medical equipements should be developed in varity styles, not only the larg-scale and complex equipements in hospital, but there is a need to develop small, portable and wearable monitoring devices for families, individuals, and community, therefore, it’s significance and valuable to study on wearable medical devices with monitoring and early-warning functions.
A wearable monitoring physiological parameters system is developed by Jilin University, from a key project‘wearable physiological parameters Non-invasive continuous monitoring device (20070333)’, which is sponsored by Jilin Province Science and Technology Department. This device can do the long-time, real-time and non-invasive measurement of physiological temperature, blood pressure, oxygen values, and dynamic electrocardiogram (DCG). DCG automatic analysis is particularly important among these parameters, so it’s crutial to study on DCG analysis.
DCG de-noising, waveform detection and assistant diagnosis are studied, respectively, and due to the limitations of current processing methods in DCG automatic domain, this study proposed some applicable algorithms for our system. Firstly, determined to use wavelet analysis method for DCG de-noising and waveform detection by anlysis the characteristics of DCG signals.
Secondly, studied the wavelet thresholding theory systemtically, and summarized five key factors which influence the de-noising effect: wavelet transform(WT) techniques, wavelet basis, decomposition levels, shrinkage functions and thresholds, and applied this theory for ECG De-noising.
For WT techniques, using classical WT, wavelet packet, lifting wavelet and stationary wavelet transform (SWT), respectively, through a comprehensive comparison, determined using SWT;
For wavelet basis, through analysis the mathmetical characteristics of eight kinds of wavelet bases, determined the suitable wavelet bases for ECG de-noising theoretically, and then through the experiments, determined that the optimal basis for de-noising ECG signals is Symmlet10 by quantitative assessment;
For wavelet decomposition levels (scales), considered two aspects, one is separation of the valuable information from the noise, the other is reduction of the reconstruction error, and determined the optimal decomposition level is five, in addition, using the changing SNR ECG signals to test its stability;
For shrinkage functions, compared with Soft, Hard, Firm and Garrot functions, each function has its own advantages and disadvantages. However, Hard shrinkage function is the most suitable one for keeping the singular point of the original signal; For thresholds, compared with the commonly used thresholds, such as Minimaxi, Universal, SURE, and Hybrid, in addtion, due to the GCV estimation doesn’t need any priori knowledge of noise energy, and Ebayes estimation is good to access the minimum mean square error, that is, the threshold is close to the optimal threshold.
Through the comprehensive analysis and comparison of the above methods, proposed using SWT with 5-scale decomposition, Sym10 wavelet basis, and Hard with Ebayes thresholding method for de-noising ECG signals, and through testing, the algorithm can reduce muscle noise, power line interference and electrode motion artifacts, but can not remove the baseline wander (BW).
Thirdly, analysis of the bandwidth of BW, and comparison of two efficient methods to remove BW: using median filtering and using high-scale wavelet decomposition estimation (WTSE). The results showed that the WTSE method is better than median filtering method by comparison of SNR improvement and singular points maintaining.
If using high-scale decomposition in single thresholding method, directly, it can also achieve the purpose of BW removal through eliminating the high scale coefficients, but, on the one hand, following the increase of decomposition scales, except the BW, other noise removal will be affected, on the other hand, the execute time will increase.Therefore, proposed a combined algorithm, using WTSE method to remove BW, and then using SWT threhsolding algorithm to reduce other noise. The de-noising results proved that the combined algorithm is better than the single de-noising methods, it not only removes noise effectively, but better protects the feature points.
Fourthly, QRS complex detection is crucial in ECG automatic analysis, including the R-wave peak and the QRS onset and offset detection. There are three typical R-wave detection algorithms based on differential, digital filter and wavelet transform, respectively. No matter what kind of algorithm was used, all need the appropriate threshold, maybe due to the different selection, even though the same method, can lead to the different detection rate. Through analysis the threshold selection rules, adopted a grouped calculation strategy to select threshold, then decrease the false detection rate because of the sudden waveform variations. Using the typical data from MIT-BIH Arrhythmia database to assess the effectiveness, the anti-interference ability, and the execute time, the above algorithms have their own advantages and disadvantages. Wavelet modulus maximum (WMM) method obtained the highest detection accuracy and the best anti-interference ability, but, it is too complicated, and the execute time need to be improved. For these limitations, this study improved the traditional WMM method, proposed a fast algorithm based on WMM, which adopted two new thresholds,and eliminated the redundant steps from the traditional WMM algorithm. In addition, using MIT-BIH Arrhythmia database to assess the effectiveness and the execute time, the results proved that the proposed algorithm not only obtained the better detection accuracy, but improved the execute time. Based on this algorithm, in scale 2, implemented the QRS complex onset and offset dectection.
Fifthly, P wave and T wave have the following properties, such as smaller amplitude, lower frequency, and morphological diversity, therefore, the existing detection algorithms have great limitations, meanwhile, due to the lack of a unified, open standard evaluation database, so far, P wave and T wave detection are still difficult in the automatic analysis of DCG. Based on the targeted QRS position, according to the frequency characteristics of P wave and T wave, selected the appropriate scale, threshold and time window, detected the peak, the onset and offset of the P-wave and T-wave, respectively.
Finally, designed the structure diagram of the wearable physiological parameters monitoring system, the system includes three main parts: the wearable device for monitoring physical parameters, the user terminal, and the supporting analysis and diagnosis system. According to the DCG diagnostic criteria, listed the mathmetical presentations of the common arrhythmia classification rules, compared the calculated ECG parameters with the above rules, and realized the auto classification of normal electrocardiogram and common arrhythmia beats. This tool can assistant the clinicians to diagnose.
引文
[1]吴学勤.动态心电图技术与应用[M].合肥:中国科学技术大学出版社. 1998,2: 2-11, 28-35,52-57,125-177,247-253.
[2]贾春光,段会龙,严筱刚. 24小时全信息固态记录Holter系统[J].中国医疗器械杂志. 1998,22(1):1-5.
[3]沈文锦.心电诊断新技术[M].北京:中国中医药出版社,2000,8:1-28, 301-320.
[4]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. 1997,12:412-456.
[5] POPESCU, M., CRISTEA, P., BEZERIANOS, A. High resolution ECG filtering using adaptive bayesian wavelet shrinkage [C]. Computers in Cardiology. 1998: 401-404.
[6] POPESCU M, CRISTEA P, BEZERIANOS A. Multiresolutional distributed filtering: A novel technique that reduces the amount of data required in high resolution electrocardiography [J]. Future Generation Computer Systems. 1999,15(2): 195-209.
[7] AGANTE DA SILVA, P.M.G., MARQUES DE SA?, J.P. ECG noise filtering using wavelets with soft-thresholding methods. Computers in Cardiology [C]. 1999: 535-538.
[8] CHERKASSKY, V., KILTS, S. Myopotential denoising of ECG signals using wavelet thresholding methods. Neural Networks [J]. 2001,14(8): 1129-1137.
[9] CHERKASSKY V, KILTS S. Comparison of wavelet thresholding methods for denoising ECG signals [C]. Artificial Neural Networks-ICANN 2001, Proceedings. 2001: 625-629.
[10]张勇,王介生.基于多分辨率分析的心电图信号去噪算法[J].系统工程与电子技术. 2002,24(12):32-34.
[11] CéSAR SáNCHEZ, JOSéJOAQUíN RIETA, FRANCISCO CASTELLS, et al. Wavelet Domain Blind Signal Separation to Analyze Supraventricular Arrhythmias from Holter Registers [C]. Independent Component Analysis and Blind Signal Separation. 2004: 1111-1117.
[12] SANCHEZ C, RIETA JJ, VAYA C, et al. Wavelet denoising as preprocessing stage to improve ICA performance in atrial fibrillation analysis [C]. Independent Component Analysis and Blind Signal Separation, Proceedings. 2006: 486-494.
[13] POORNACHANDRA, S., KUMARAVEL, N. Hyper-trim shrinkage for denoising of ECG signal [J]. Digital Signal Processing: A Review Journal. 2005,15 (3): 317-327.
[14] SZI-WEN CHEN, HSIAO-CHEN CHEN, HSIAO-LUNG CHAN. A real-time QRS detectionmethod based on moving-averaging incorporating with wavelet denoising [J]. Computer methods and programs in biomedicine. 2006,82(3):187–195.
[15] POORNACHANDRA, S. Wavelet-based denoising using subband dependent threshold for ECG signals [J]. Digital Signal Processing: A Review Journal. 2008,18(1): 49-55.
[16] TIRTOM, H., ENGIN, M., ENGIN, E.Z. Enhancement of time-frequency properties of ECG for detecting micropotentials by wavelet transform based method [J]. Expert Systems with Applications. 2008,34(1): 746-753.
[17] ALFAOURI, M., DAQROUQ, K. ECG signal denoising by wavelet transform thresholding [J]. American Journal of Applied Sciences 2008,5(3): 276-281.
[18] TIKKANEN, P.E. Nonlinear wavelet and wavelet packet denoising of electrocardiogram signal [J]. Biological Cybernetics. 1999,80(4): 259-267.
[19]赵治栋,潘敏,郭希山,等.基于小波包收缩的心电信号消噪方法研究[J].计算机工程与应用. 2002,38(20):19-23.
[20] SINGH, B.N., TIWARI, A.K. Optimal selection of wavelet basis function applied to ECG signal denoising [J]. Digital Signal Processing: A Review Journal. 2006,16(3): 275-287.
[21]赵治栋,陈裕泉.广义小波收缩阈值选择及应用研究[J].传感技术学报. 2007, 20 (3): 601-605.
[22]高清维,李海鹰,庄镇泉,等.基于平稳小波变换的心电信号噪声消除方法[J].电子学报.2003,31(2):238-240.
[23] ZHANG, Z., FUJIWARA, H., REN, F. Signal processing using translation invariant RI-spline wavelet [C]. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. 2004: 3267-3272.
[24] SU, L., ZHAO, G. De-noising of ECG signal using translation- Invariant wavelet de-noising method with improved thresholding [C]. Annual International Conference of the IEEE Engineering in Medicine and Biology– Proceedings. 2005: 5946-5949.
[25] KUMARI, R.S.S., THILAGAMANIMALA, A., SADASIVAM, V. ECG signal interferences removal using wavelet based CSTD technique [C]. Proceedings - International Conference on Computational Intelligence and Multimedia Applications, ICCIMA. 2008: 530-534.
[26] KUZUME K, NIIJIMA K, TAKANO S. FPGA-based lifting wavelet processor for real-time signal detection [J]. Signal Processing. 2004,84(10): 1931-1940.
[27] SAI,R.,BEN MESSAOUD,M.,KACHOURI,et al. Analysis of theelectrocardiogram signal by the lifting scheme [C]. International Symposium on Control, Communications and Signal Processing, ISCCSP, 2004: 263-266.
[28] ERCELEBI, E. Electrocardiogram signals de-noising using lifting-based discrete wavelet transform [J]. Computers in Biology and Medicine. 2004,34(6):479-493.
[29] D. L. DONOHO. De-noising by soft-thresholding [C]. IEEE Trans. Inform. Theory. 1995:613–627.
[30] D.L. DONOHO, I.M. JOHNSTONE. Ideal spatial adaptation via wavelet shrinkage [J]. Biometrika. 1994,81(3):425–455.
[31] D.L. DONOHO, I.M. JOHNSTONE. Adapt to unknown smoothness via wavelet shrinkage [J]. J. Amer. Statist. Assoc. 1995,90: 1200–1224.
[32] NIKOLAEV, N., NIKOLOV, Z., GOTCHEV, A., et al. Wavelet domain Wiener filtering for ECG denoising using improved signal estimate [C]. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing Proceedings, 2000:3578-3581.
[33] CHMELKA, L., KOZUMPLíK, J. Wavelet-based Wiener filter for electrocardiogram signal denoising [C]. Computers in Cardiology. 2005:771-774.
[34] ZHANG, J., SHOU, G., DAI, G. Denoising of ECG signals based on wavelet transform [J]. Journal of Northwestern Polytechnical University. 2005,23(1): 11-14.
[35] ZHANG, D. Wavelet approach for ECG baseline wander correction and noise reduction [C]. Annual International Conference of the IEEE Engineering in Medicine and Biology– Proceedings. 2005:1212-1215.
[36] W. P. HOLSINGER. A QRS preprocessor based on digital differentiation [J]. IEEE Trans. Biomed. Eng. 1971,18(3):212-217.
[37] J. FRADEN AND M. R. NEUMAN. QRS wave detection [J]. Med. Biol.Eng. Comput. 1980,18(2):125-132.
[38] A. MENRAD. Dual microprocessor system for cardiovascular data acquisition, processing and recording [C]. IEEE Inr. Con5 Industrial Elect. Contr. Instrument. 1981:64-69.
[39] R. A. BALDA. The HP ECG analysis program [J]. Trends in Computer-Processed Electrocardiograms. 1977:197-205.
[40] M. L. AHLSTROM AND W. J. TOMPKINS. Automated high-speed analysis of holter tapes with microcomputers [J]. IEEE Trans. Biomed.Eng. 1983,30(10):651-657.
[41] W. A. H. ENGELSE AND C. ZEELENBERG. A single scan algorithm for QRS-detection and feature extraction [J]. IEEE Comput. Card., Long Beach: IEEE Computer Society. 1979,6:37-42.
[42] M. OKADA. A digital filter for the QRS complex detection [J]. IEEE Trans. Biomed. Eng. 1979,26(12):700-703.
[43] THAKOR NV, WEBSTER JG, TOMPKINS WJ. Estimation of QRS Complex Power Spectra for Design of a QRS Filter [J]. IEEE Trans. on Biomedical Engineering. 1984,31(11): 702-706.
[44] FRIESEN, G.M., JANNETT, T.C., AFIFY JADALLAH, M., et al. A comparison of the noise sensitivity of nine QRS detection algorithms [J]. IEEE Transactions on Biomedical Engineering.1990,37(1):85-98.
[45] VALTINO X, AFONSO , WILLIS J . TOMPKINS , et al . Filter bank based ECG beat detection [C]. Proceedings of 19th Annual Conference of IEEE/ EMBS. 1996:1037-1038.
[46] M. L. AHLSTROM AND W. J. TOMPKINS. Digital filters for real-time ECG signal processing using microprocessors [J]. IEEE Trans. Biomed.Eng. 1985,32(9):708-713.
[47] ANTTI RUHA, SAMI SALLINEN, AND SEPPO NISSILA. A Real-Time Microprocessor QRS Detector for the Measurement of Ambulatory HRV System with a 1-ms Timing Accuracy [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 1997,44(3):159-168.
[48]朱凌云,吴宝明,王正国,等.移动心电监护系统QRS波的实时检测算法研究[J].仪器仪表学报. 2005,26(6):603-607.
[49] PEDRO GOMIS, DOUGLAS L. JONES, PERE CAMINAL, EDWARD J. BERBARI, et al. Analysis of Abnormal Signals Withine the QRS Complex of the High-Resolution electrocardiogram [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 1997,44(8):681-693.
[50] IVAN DASKALOV, AND IVAN DOTSINSKY. Developments in ECG acquisition, preprocessing, parameter measurement, and recording [J]. IEEE ENGINEERING IN MEDICINE AND BIOLOY. 1998,17(2):50-58.
[51] I.K. DASKALOV, I.I. CHRISTOV. Electrocardiogram signal preprocessing for automatic detection of QRS boundaries [J]. Medical Engineering & Physics. 1999,21(1):37–44.
[52] ESTHER PUEYO, LEIF S?RNMO, AND PABLO LAGUNA. QRS Slopes forDetection and haracterization of Myocardial Ischemia [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 2008,55(2):468-477.
[53] JAMES D. WILSON, R. B. GOVINDAN, JEFF O. HATTON, et al. Integrated Approach for Fetal QRS Detection [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 2008,55(9):2190-2197.
[54] NB MCLAUGHLIN. Accuracy of automatic QT measurement techniques [J]. IEEE Trans on BME. 1993,33(1):192-200.
[55] MATTEO PAOLETTI, CARLO MARCHESI. Discovering dangerous patterns in long-term ambulatory ECG recordings using a fast QRS detection algorithm and explorative data analysis [J]. Computer methods and programs in biomedicine. 2006,8(2):20-30.
[56] SERAFIM TABAKOV, IVO ILIEV, AND VESSELA KRASTEVA. Online Digital Filter and QRS Detector Applicable in Low Resource ECG monitoring Systems [J]. Annals of Biomedical Engineering. 2008,36(11):1805–1815.
[57] NATALIA M. ARZENO, ZHI-DE DENG, AND CHI-SANG POON. Analysis of First-Derivative Based QRS Detection Algorithms [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 2008,55(2):478-474.
[58] GALEN S. WAGNER, CHARLES MAYNARD, ALAN ANDRESEN, et al. Evaluation of Advanced Electrocardiographic Diagnostic Software for Detection of Prior Myocardial Infarction [J]. The American Journal of Cardiology. 2002,89(1):75-79.
[59] BERT-UWE K?HLER, CARSTEN HENNIG,REINHOLD ORGLMEISTER. The Principles of Software QRS Detection [J]. IEEE ENGINEERING IN MEDICINE AND BIOLOGY.2002,21(1):42-57.
[60] CUIWEI LI, CHONGXUN ZHENG, AND CHANGFENG TAI. Detection of ECG characteristic point s using wavelet transforms [J]. IEEE Transon BME. 1995,42(1):21-28.
[61] M. BAHOURA, M. HASSANI, M. HUBIN. DSP implementation of wavelet transform for real time ECG wave forms detection and heart rate analysis [J]. Computer Methods and Programs in Biomedicine. 1997,52(1):35-44.
[62] SHUBHA KADAMBE,ROBIN MURRAY, AND G. Faye Boudreaux-Bartels. Wavelet Transform-Based QRS Complex Detector [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 1999,46(7):838-848.
[63]余辉,张力新,吕扬生.基于小波变换的QRS波检测[J].生物医学工程与临床. 2001, 5(2):65-70.
[64]田学隆,闫春红,俞亚青,等.基于小波变换的R波检测算法[J].生物医学工程学杂志.2006,23(2):257-261.
[65]王文,孙世双,周勇.基于小波变换的心电图QRS波群检测方法研究[J].北京生物医学工程. 2002,14(21):241-243.
[66]郑崇勋,骆京安,叶继伦.基于小波变换的运动心电信号去噪方法研究.第四军医大学学报. 2000,21(5):34-37.
[67]李向军,陈裕泉. QRS波群时频检测方法的新进展.国外医学生物医学工程分册. 2005, 28(5):281-286.
[68]余辉,张凯,吕扬生,等.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):334-338.
[69] S. C. SAXENA A, V. KUMAR A, S. T. HAMDE. Feature extraction from ECG signals using wavelet transforms for disease Diagnostics [J]. International Journal of Systems Science. 2002,92(2):1073-1085.
[70]陈培军,陈士贵,俞尧荣,等.二项式小波检测心电特征点[J].航天医学与医学工程. 2004,17(1):59-63.
[71] JUAN PABLO MARTíNEZ, RUTE ALMEIDA, SALVADOR OLMOS,et al. A Wavelet-Based ECG Delineator: Evaluation on Standard Databases [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 2004,51(4):570-581.
[72]胡鹏,张永红,张菊鹏,等.基于小波变换的心律失常判别算法[J].北京生物医学工程. 2003, 22(11):23-26.
[73]杨毓英,史习智,陈光冶,等.小波变换用于ECG信号的R波实时探测[J].上海交通大学学报. 1997,31(9):18-21.
[74]万相奎,秦树人,梁小容,等.小波变换在心电信号特征提取中的应用[J].北京生物医学工程. 2005,24(16):411-413.
[75]尹登峰.动态ECG分析中QRS波检测算法的研究[D].浙江大学硕士学位论文. 2003.
[76]陈永利.动态心电自动分析中QRS复合波检测算法研究[D].浙江大学博士学位论文.2006.
[77]翟爱梅.心电图自动分析技术的研究[D].哈尔滨工业大学硕士学位论文. 2003.
[78]苏丽.远程心电监护诊断系统心电信号处理研究[D].哈尔滨工程大学博士学位论文. 2006.
[79] SENHADJI L, WANG F. Wavelet extrema representation for QRS-T cancellation and P wave detection [J]. Computers in Cardiology. 2002,29:37-40.
[80] M. I VAI AND LI-GAO ZHOU. Beat-to-Beat ECG Ventricular Late Potentials Variance Detection by Filter Bank and Wavelet Transform as Beat-Sequence Filter [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 2004, 51(8):1407-1413.
[81] A. RAKOTOMAMONJY, B. MIGEON P. MARCHE. Automated neural network detection of wavelet preprocessed electrocardiogram late potentials [J]. Medical & Biological Engineering & Computing. 1998,36(3):346-350.
[82]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.
[83]王宏山,杨军,俞梦孙,等.模糊ART神经网络在心律失常分析中的应用[J].中国生物医学工程学报. 2004,23(5):410-413.
[84] DE AZEVEDO BOTTER E,NASCIMENTO CR,YONEYAMA T.A neural network with asymmetric basis functions for feature extraction of ECG P waves [J]. IEEE Trans. Neural Network. 2001,12(5):1252-1255.
[85] D. BENITEZA, P.A. GAYDECKIA, A. ZAIDIB, A.P. Fitzpatrick. The use of the Hilbert transform in ECG signal analysis [J]. Computers in Biology and Medicine. 2001,31(5):399–406.
[86]陈文菊,潘敏,赵治栋,等.基于小波分析和Hilbert变换的R波检测算法[J].传感技术学报. 2006,19(1):248-252.
[87] P.E.TRAHANIAS. An approach to QRS complex detection using mathematical morphology [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 1993,40 (2):201-205.
[88]马焕清,张更生.心电计算机辅助诊断中的波形检测和识别方法的研究[J].中国科学技术大学学报. 1991,2(13):47-56.
[89] SO HH, CHAN KL. Development of QRS detection method for real-time ambulatorycardiac monitor [J]. IEEE-EMBS. 1997,1(30):289-292.
[90] TANKF, CHANKL, CHOI K. Detection of the QRS complex, P wave and T wave in electrocardiogram [C]. Advances in Medical Signal and Information Processing. IEE Conf. 2000:41-47.
[91] STRUMILLO P. Nested median filtering for detecting T-wave offset in ECGs [J]. Electronics Letters. 2002,38(14):682-683.
[92] NITISH V.THAKOR,YI-SHENG ZHU. Application of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection [J].IEEE Transactions on Biomedical Engineering. 1991,38(8):785-793.
[93] EMILIO SORIA-OLIVAS.Application of adaptive signals processing for determining the limits of P and T waves in an ECG [J]. IEEE Transactions on Biomedical Engineering. 1998,45(8):1077-1080.
[94]杨振野,李玲华,林家瑞.一种基于函数逼近原理的心电图P波识别的研究[J].生物医学工程杂志. 1998,15(2):120-122.
[95]骆建华,李高平,庄大戈.演化算法在T波终点检测中的应用[J].中国生物医学工程学报. 2002,21(3):211-214.
[96] STEPHANE MALLAT著,杨力华,等译.信号处理的小波导引[M].北京:机械工业出版社.2003: 9,22-23,58-66,94-116,112-255,331-376.
[97]李建平.小波分析与信号处理——理论、应用及软件实现[M].重庆:重庆出版社. 1997:3-94.
[98]杨福生.小波变换的工程分析与应用[M].北京:科学出版社. 2000: 1-106.
[99]彭玉华.小波变换与工程应用[M].北京:科学出版社. 1999:13-54,88-99.
[100] MARTIN VETTERLI. Wavelets and subband coding [M]. Upper Saddle River, N.J.:Prentice Hall PTR. 1995:209-289.
[101] Y.MEYER.著,尤众译.小波与算子[M].北京,世界图书出版公司. 1992: 32-42.
[102]飞思科技产品研发中心. Matlab6.5辅助小波分析与应用[M].北京:电子工业出版社. 2003:6-49, 109-114.
[103]胡昌华,张军波,夏军等.基于Matlab的系统分析与设计——小波分析[M].西安:西安电子科技大学出版社. 1999:1-23.
[104] INGRID DAUBECHIES著,李建平等译.小波十讲[M].国防工业出版社.2004, 5: 239 -243.
[105] D.L. DONOHO, I.M. JOHNSTONE, G. KERKYACHARIAN, D. PICARD. Wavelet Shrinkage: Asymptopia [J]. J. Roy. Statist. Soc. Ser. B. 1995, 57: 301–369.
[106] I.M. JOHNSTONE, B.W. SILVERMAN. Wavelet threshold estimator for the data with correlated noise [J]. J. Roy. Statist. Soc. Ser. B. 1997, 59: 319–351.
[107] MATHWORKS. Http://www.mathworks.com/access/helpdesk/help/pdf_doc/ wavelet/wavelet_ug.pdf[EB/OL].
[108] GAO,H-Y., AND BRUCE, A.G. WaveShrink with Firm Shrinkage [J]. Statistica Sinica. 1997,7: 855-874.
[109] HONG-YE GAO. Wavelet Shrinkage Denoising Using the Non-Negative Garrote [J]. Journal of Computational and Graphical Statistics. 1998,7(4): 469- 488.
[110] M. JANSEN, M. MALFAIT, A. BUTHEEL. Generalized cross validation for wavelet thresholding [J]. Signal Processing. 1997,45(1):33-44.
[111] WEYRICH, N., WARHOLA, G.T. Wavelet shrinkage and generalized cross validation for image denoising [J].Image Processing. IEEE Transactions. 1998,7 (1):82-90.
[112] JANSEN M, BULTHEEL A. Multiple wavelet threshold estimation by generalized cross validation for images with correlated noise [J]. IEEETRANSACTIONS ON IMAGE PROCESSING. 1999,8(7):947-953.
[113] IAIN M. JOHNSTONE, BERNARD W. SILVERMAN. EbayesThresh: R Programs for Empirical Bayes Thresholding [J]. Journal of Statistical Software. 2005, 12(8):1-38.
[114] IAIN M. JOHNSTONE AND BERNARD W. SILVERMAN. Needles and Straw in Haystacks: Empirical Bayes Estimates of Possibly Sparse Sequences [J]. The Annals of Statistics.2004,32(4):1594-1649.
[115] ANTONIADIS A, JANSEN M, JOHNSTONE IM, SILVERMAN BW. EbayesThresh: MATLAB Software for Empirical Bayes Thresholding. 2004, URL http://www-lmc.imag.fr/lmc-sms/Anestis.Antoniadis/EBayesThresh/.
[116] Wavelab. http://www-stat.stanford.edu/~wavelab [EB/OL].
[117] C. STEIN. Estimation of the mean of a multivariate normal distribution [J]. Ann. Statist. 1981,9(6):1135–1151.
[118] S.R. PETERSON. Some statistical properties of alpha-trimmed mean and standard type M filters [C]. IEEE Trans. ASSP. 1998,36 (5):707-713.
[119] DAUBECHIES I, SWELDENS W. Factoring wavelet transforms into lifting steps [J]. Journal of Fourier analysis and applications. 1998,4(3): 247-269.
[120] SWELDENS W. The lifting scheme: A construction of second generation wavelets [J]. Siam Journal on Mathematical analysis. 1998,29(2): 511-546.
[121] SWELDENS W. The lifting scheme: A custom-design construction of biorthogonal wavelets [J]. Applied and Computational Harmonic Analysis. 1996,3 (2):186-200.
[122]孙红星,王蓉,赵楠楠,等.基于小波提升和形态学的图像边缘检测方法[J].系统仿真学报. 2006, S2:869-871.
[123]李洪刚,吴乐南.基于任意小波的提升格式的设计[J].东南大学学报(自然科学版). 2001,31(14):22-26.
[124]李刚,吴渝,王国胤,等.几种三层小波提升方案及其在图像压缩中的应用[J].计算机科学. 2002,29(11):87-89.
[125] NASON, G.P., B.W. SILVERMAN. The stationary wavelet transform and some statistical applications [C]. Lecture Notes in Statistics. 1995:281-299.
[126]郭锐,彭玉华,万洪林.基于平稳小波变换的掌纹特征提取与识别[J].计算机工程与应用. 2006,42(17):61-65.
[127]罗强,田化梅,罗萍,等.基于平稳小波变换的心电信号去噪研究[J].计算机与数字工程. 2006,34(6):67-70.
[128] GOLDBERGER AL, AMARAL LAN, GLASS L, HAUSDORFF JM, et al.PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220. http://circ.ahajournals.org/cgi/content/full/101/23/e215],2000.
[129]王林泓,杨浩.心电信号处理中滤波器设计的研究[J].北京生物医学工程. 2002, 21(13): 218-221.
[130]朱伟芳,齐春.一种实用的去基线漂移滤波算法[J].苏州大学学报(工科版). 2006, 26(1): 62-64.
[131]陈环,丁永生,吴怡之.面向智能服装健康监护系统的心电信号基线漂移处理[J].计算机应用研究. 2008,25(6):1707-1709.
[132]汪家旺,吴玲燕,杨涛,等.几种去心电基线漂移算法的实现和比较[J].中国医疗器械信息. 2008,14(6):30-33.
[133] P.DE CHAZAL. Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoes [J]. IEEE Trans. Biomed. Eng. 2003,50(6):686-696.
[134]叶文宇.心电自动诊断技术的研究][D].天津大学博士学位论文. 2003.
[135]师黎,杨岑玉,费敏锐.基于小波变换的心电信号R波及ST段的提取[J].仪器仪表学报. 2008,29(4): 804-809.
[136] J. S. SAHAMBI,S.N. TANDON,R.K.P. BHATT. Wavelet based ST-segment analysis [J].Med. Biol. Eng. Comput. 1998,36(5):568-572.
[137] RAMI LEHTINENA, HARRI SIEVANEN, VAINO TURJANMAA,et al. Effect of ST segment measurement point on performance of exercise ECG analysis [J]. International Journal of Cardiology. 1997,61(3):239-245.
[138] MAGLAVERAS N, STAMKOPOULOS T, PAPPAS C,et al. An adaptive backpropagation neural network for real-time ischemia episodes detection: development and performance analysis using the European ST-T database [J]. IEEE Trans. Biomed. Eng. 1998,45(7):805-813.
[139] PAPALOUKAS C, FOTIADIS D I, LIKAS A,et al. An ischemia detection method based on artificial neural networks [J]. Artificial Intelligence in Medicine. 2002,24(2):167–78.
[140]宋喜国,邓亲恺.基于小波变换的ST-T段自动检测[J].中国医学物理学杂志. 2005, 22(4):601-603.
[141]芦继来,胡广书.基于小波变换的运动心电ST段检测方法[J].北京生物医学工程. 2005,24(5):329-333.
[142]陈海燕,黄敏,姜云霞,等.利用小波变换检测心电图ST段[J].电机与控制学报.2006, 10(5):531-533.
[143] JOSéGARCíA, LEIF S?RNMO, SALVADOR OLMOS,et al. Automatic Detection of ST-T Complex Changes on the ECG Using Filtered RMS Difference Series: Application to Ambulatory Ischemia Monitoring [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 2000,47(9):1195-1211.
[144] FAYYAZ A AFSAR, M ARIF, J YANG. Detection of ST segment deviation episodes in ECG using KLT with an ensemble neural classifier [J]. Physiol. Meas. 2008, 29:747–760.
[145]朱伟芳,王文斌,齐春,等.一种适用于Holter系统的ST段分析方案[J].生物医学工程学杂志.2004,21(6):943-946.
[146]滕晓菲,张元亭.移动医疗:穿戴式医疗仪器的发展趋势[J].中国医疗器械杂志. 2006,30(5):330-340.
[147]卜茉.人体生理参数监测系统原理样机的研究[D].吉林大学硕士论文. 2007.
[148]朱建新,高蕾娜,张新访.初探普适时代的健康穿戴系统[J].计算机系统应用.2008, 17(10):2-6.
[149]卢喜烈,主编.心电图诊断与鉴别诊断[M].北京:科学技术文献出版社. 1995,5: 2-11,19-27,170-176.
[150]刘广芝.心电图学概论[M].贵州:贵州科技出版社.2003:157-162, 224-232, 282-397.
[151]卢志刚,薛保延,周立坤.一种心电图自动检测算法[J].山西技术.2005,6:118-123.
[152]田蕴青,郝冬梅.心电图自动诊断系统的研制[J].中国医学物理学杂志. 2000,17 (4):215-217.
[153]朱凌云.移动心电监护系统ECG信号的实时检测算法研究[D].重庆大学博士学位论文. 2003:60-63.
[154] M. G. TSIPOURAS, D. I. FOTIADIS, D. SIDERIS. Arrhythmia classification using the RR-interval duration signal [C]. Computers in Cardiology. 2002: 485-488.
[155] MG TSIPOURAS, Y GOLETSIS1, DI FOTIADIS.A Method for Arrhythmic Episode Classification in ECGs Using Fuzzy Logic and Markov Models [C]. Computers in Cardiology. 2004:361-364.
[156] REN-GUEY LEE,CHI CHOU,CHIEN-CHIN LAI,et al. A Novel QRS Detection Algorithm Applied to the Analysis for Heart Rate Variability of Patients with Sleep Apnea [J]. Biomedical Engineering Applications, Basis & Communications. 2005,17(5): 258-262.
[157] VESSELA T. KRASTEVA, IRENA I. JEKOVA, IVAYLO I. CHRISTOV. Automatic detection of premature atrial contractions in the electrocardiogram [J]. Electrotechniques & Electronics. 2006,9(10): 49-56.
[158] PHILIP DE CHAZAL, MARIA O’DWYER, RICHARD B. Reilly. AutomaticClassification of Heartbeats Using ECG Morphology and Heartbeat Interval Features [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 2004,51(7): 1196-1206.
[159] DINGFEI GE, NARAYANAN SRINIVASAN, SHANKAR M KRISHNAN. Cardiac arrhythmia classification using autoregressive modeling [J/OL]. BioMedical Engineering OnLine.2002:1-5. http://www.biomedical-engineering-online.com/content/1/1/5.
[160] NICHOLAS ANDRISEVIC, KHALED EJAZ, FERNANDO RIOS-GUTIERREZ, et al. Detection of Heart Murmurs Using Wavelet Analysis and Artificial Neural Networks. Journal of Biomechanical Engineering [J]. 2005,127: 899-995.
[161] E.A.FERNANDEZ. Detection of abnormality in the electrocardiogram without prior knowledge by using the quantization error of a self-organising map, tested on the European ischaemia database [J]. Med. Biol. Eng. Comput. 2001, 39:330-337.
[162] M.G. TSIPOURAS, D.I. FOTIADIS, D. SIDERIS. An arrhythmia classification system based on the RR-interval signal [J]. Artificial Intelligence in Medicine. 2005,33:237-250.
[2]贾春光,段会龙,严筱刚. 24小时全信息固态记录Holter系统[J].中国医疗器械杂志. 1998,22(1):1-5.
[3]沈文锦.心电诊断新技术[M].北京:中国中医药出版社,2000,8:1-28, 301-320.
[4]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. 1997,12:412-456.
[5] POPESCU, M., CRISTEA, P., BEZERIANOS, A. High resolution ECG filtering using adaptive bayesian wavelet shrinkage [C]. Computers in Cardiology. 1998: 401-404.
[6] POPESCU M, CRISTEA P, BEZERIANOS A. Multiresolutional distributed filtering: A novel technique that reduces the amount of data required in high resolution electrocardiography [J]. Future Generation Computer Systems. 1999,15(2): 195-209.
[7] AGANTE DA SILVA, P.M.G., MARQUES DE SA?, J.P. ECG noise filtering using wavelets with soft-thresholding methods. Computers in Cardiology [C]. 1999: 535-538.
[8] CHERKASSKY, V., KILTS, S. Myopotential denoising of ECG signals using wavelet thresholding methods. Neural Networks [J]. 2001,14(8): 1129-1137.
[9] CHERKASSKY V, KILTS S. Comparison of wavelet thresholding methods for denoising ECG signals [C]. Artificial Neural Networks-ICANN 2001, Proceedings. 2001: 625-629.
[10]张勇,王介生.基于多分辨率分析的心电图信号去噪算法[J].系统工程与电子技术. 2002,24(12):32-34.
[11] CéSAR SáNCHEZ, JOSéJOAQUíN RIETA, FRANCISCO CASTELLS, et al. Wavelet Domain Blind Signal Separation to Analyze Supraventricular Arrhythmias from Holter Registers [C]. Independent Component Analysis and Blind Signal Separation. 2004: 1111-1117.
[12] SANCHEZ C, RIETA JJ, VAYA C, et al. Wavelet denoising as preprocessing stage to improve ICA performance in atrial fibrillation analysis [C]. Independent Component Analysis and Blind Signal Separation, Proceedings. 2006: 486-494.
[13] POORNACHANDRA, S., KUMARAVEL, N. Hyper-trim shrinkage for denoising of ECG signal [J]. Digital Signal Processing: A Review Journal. 2005,15 (3): 317-327.
[14] SZI-WEN CHEN, HSIAO-CHEN CHEN, HSIAO-LUNG CHAN. A real-time QRS detectionmethod based on moving-averaging incorporating with wavelet denoising [J]. Computer methods and programs in biomedicine. 2006,82(3):187–195.
[15] POORNACHANDRA, S. Wavelet-based denoising using subband dependent threshold for ECG signals [J]. Digital Signal Processing: A Review Journal. 2008,18(1): 49-55.
[16] TIRTOM, H., ENGIN, M., ENGIN, E.Z. Enhancement of time-frequency properties of ECG for detecting micropotentials by wavelet transform based method [J]. Expert Systems with Applications. 2008,34(1): 746-753.
[17] ALFAOURI, M., DAQROUQ, K. ECG signal denoising by wavelet transform thresholding [J]. American Journal of Applied Sciences 2008,5(3): 276-281.
[18] TIKKANEN, P.E. Nonlinear wavelet and wavelet packet denoising of electrocardiogram signal [J]. Biological Cybernetics. 1999,80(4): 259-267.
[19]赵治栋,潘敏,郭希山,等.基于小波包收缩的心电信号消噪方法研究[J].计算机工程与应用. 2002,38(20):19-23.
[20] SINGH, B.N., TIWARI, A.K. Optimal selection of wavelet basis function applied to ECG signal denoising [J]. Digital Signal Processing: A Review Journal. 2006,16(3): 275-287.
[21]赵治栋,陈裕泉.广义小波收缩阈值选择及应用研究[J].传感技术学报. 2007, 20 (3): 601-605.
[22]高清维,李海鹰,庄镇泉,等.基于平稳小波变换的心电信号噪声消除方法[J].电子学报.2003,31(2):238-240.
[23] ZHANG, Z., FUJIWARA, H., REN, F. Signal processing using translation invariant RI-spline wavelet [C]. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. 2004: 3267-3272.
[24] SU, L., ZHAO, G. De-noising of ECG signal using translation- Invariant wavelet de-noising method with improved thresholding [C]. Annual International Conference of the IEEE Engineering in Medicine and Biology– Proceedings. 2005: 5946-5949.
[25] KUMARI, R.S.S., THILAGAMANIMALA, A., SADASIVAM, V. ECG signal interferences removal using wavelet based CSTD technique [C]. Proceedings - International Conference on Computational Intelligence and Multimedia Applications, ICCIMA. 2008: 530-534.
[26] KUZUME K, NIIJIMA K, TAKANO S. FPGA-based lifting wavelet processor for real-time signal detection [J]. Signal Processing. 2004,84(10): 1931-1940.
[27] SAI,R.,BEN MESSAOUD,M.,KACHOURI,et al. Analysis of theelectrocardiogram signal by the lifting scheme [C]. International Symposium on Control, Communications and Signal Processing, ISCCSP, 2004: 263-266.
[28] ERCELEBI, E. Electrocardiogram signals de-noising using lifting-based discrete wavelet transform [J]. Computers in Biology and Medicine. 2004,34(6):479-493.
[29] D. L. DONOHO. De-noising by soft-thresholding [C]. IEEE Trans. Inform. Theory. 1995:613–627.
[30] D.L. DONOHO, I.M. JOHNSTONE. Ideal spatial adaptation via wavelet shrinkage [J]. Biometrika. 1994,81(3):425–455.
[31] D.L. DONOHO, I.M. JOHNSTONE. Adapt to unknown smoothness via wavelet shrinkage [J]. J. Amer. Statist. Assoc. 1995,90: 1200–1224.
[32] NIKOLAEV, N., NIKOLOV, Z., GOTCHEV, A., et al. Wavelet domain Wiener filtering for ECG denoising using improved signal estimate [C]. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing Proceedings, 2000:3578-3581.
[33] CHMELKA, L., KOZUMPLíK, J. Wavelet-based Wiener filter for electrocardiogram signal denoising [C]. Computers in Cardiology. 2005:771-774.
[34] ZHANG, J., SHOU, G., DAI, G. Denoising of ECG signals based on wavelet transform [J]. Journal of Northwestern Polytechnical University. 2005,23(1): 11-14.
[35] ZHANG, D. Wavelet approach for ECG baseline wander correction and noise reduction [C]. Annual International Conference of the IEEE Engineering in Medicine and Biology– Proceedings. 2005:1212-1215.
[36] W. P. HOLSINGER. A QRS preprocessor based on digital differentiation [J]. IEEE Trans. Biomed. Eng. 1971,18(3):212-217.
[37] J. FRADEN AND M. R. NEUMAN. QRS wave detection [J]. Med. Biol.Eng. Comput. 1980,18(2):125-132.
[38] A. MENRAD. Dual microprocessor system for cardiovascular data acquisition, processing and recording [C]. IEEE Inr. Con5 Industrial Elect. Contr. Instrument. 1981:64-69.
[39] R. A. BALDA. The HP ECG analysis program [J]. Trends in Computer-Processed Electrocardiograms. 1977:197-205.
[40] M. L. AHLSTROM AND W. J. TOMPKINS. Automated high-speed analysis of holter tapes with microcomputers [J]. IEEE Trans. Biomed.Eng. 1983,30(10):651-657.
[41] W. A. H. ENGELSE AND C. ZEELENBERG. A single scan algorithm for QRS-detection and feature extraction [J]. IEEE Comput. Card., Long Beach: IEEE Computer Society. 1979,6:37-42.
[42] M. OKADA. A digital filter for the QRS complex detection [J]. IEEE Trans. Biomed. Eng. 1979,26(12):700-703.
[43] THAKOR NV, WEBSTER JG, TOMPKINS WJ. Estimation of QRS Complex Power Spectra for Design of a QRS Filter [J]. IEEE Trans. on Biomedical Engineering. 1984,31(11): 702-706.
[44] FRIESEN, G.M., JANNETT, T.C., AFIFY JADALLAH, M., et al. A comparison of the noise sensitivity of nine QRS detection algorithms [J]. IEEE Transactions on Biomedical Engineering.1990,37(1):85-98.
[45] VALTINO X, AFONSO , WILLIS J . TOMPKINS , et al . Filter bank based ECG beat detection [C]. Proceedings of 19th Annual Conference of IEEE/ EMBS. 1996:1037-1038.
[46] M. L. AHLSTROM AND W. J. TOMPKINS. Digital filters for real-time ECG signal processing using microprocessors [J]. IEEE Trans. Biomed.Eng. 1985,32(9):708-713.
[47] ANTTI RUHA, SAMI SALLINEN, AND SEPPO NISSILA. A Real-Time Microprocessor QRS Detector for the Measurement of Ambulatory HRV System with a 1-ms Timing Accuracy [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 1997,44(3):159-168.
[48]朱凌云,吴宝明,王正国,等.移动心电监护系统QRS波的实时检测算法研究[J].仪器仪表学报. 2005,26(6):603-607.
[49] PEDRO GOMIS, DOUGLAS L. JONES, PERE CAMINAL, EDWARD J. BERBARI, et al. Analysis of Abnormal Signals Withine the QRS Complex of the High-Resolution electrocardiogram [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 1997,44(8):681-693.
[50] IVAN DASKALOV, AND IVAN DOTSINSKY. Developments in ECG acquisition, preprocessing, parameter measurement, and recording [J]. IEEE ENGINEERING IN MEDICINE AND BIOLOY. 1998,17(2):50-58.
[51] I.K. DASKALOV, I.I. CHRISTOV. Electrocardiogram signal preprocessing for automatic detection of QRS boundaries [J]. Medical Engineering & Physics. 1999,21(1):37–44.
[52] ESTHER PUEYO, LEIF S?RNMO, AND PABLO LAGUNA. QRS Slopes forDetection and haracterization of Myocardial Ischemia [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 2008,55(2):468-477.
[53] JAMES D. WILSON, R. B. GOVINDAN, JEFF O. HATTON, et al. Integrated Approach for Fetal QRS Detection [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 2008,55(9):2190-2197.
[54] NB MCLAUGHLIN. Accuracy of automatic QT measurement techniques [J]. IEEE Trans on BME. 1993,33(1):192-200.
[55] MATTEO PAOLETTI, CARLO MARCHESI. Discovering dangerous patterns in long-term ambulatory ECG recordings using a fast QRS detection algorithm and explorative data analysis [J]. Computer methods and programs in biomedicine. 2006,8(2):20-30.
[56] SERAFIM TABAKOV, IVO ILIEV, AND VESSELA KRASTEVA. Online Digital Filter and QRS Detector Applicable in Low Resource ECG monitoring Systems [J]. Annals of Biomedical Engineering. 2008,36(11):1805–1815.
[57] NATALIA M. ARZENO, ZHI-DE DENG, AND CHI-SANG POON. Analysis of First-Derivative Based QRS Detection Algorithms [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 2008,55(2):478-474.
[58] GALEN S. WAGNER, CHARLES MAYNARD, ALAN ANDRESEN, et al. Evaluation of Advanced Electrocardiographic Diagnostic Software for Detection of Prior Myocardial Infarction [J]. The American Journal of Cardiology. 2002,89(1):75-79.
[59] BERT-UWE K?HLER, CARSTEN HENNIG,REINHOLD ORGLMEISTER. The Principles of Software QRS Detection [J]. IEEE ENGINEERING IN MEDICINE AND BIOLOGY.2002,21(1):42-57.
[60] CUIWEI LI, CHONGXUN ZHENG, AND CHANGFENG TAI. Detection of ECG characteristic point s using wavelet transforms [J]. IEEE Transon BME. 1995,42(1):21-28.
[61] M. BAHOURA, M. HASSANI, M. HUBIN. DSP implementation of wavelet transform for real time ECG wave forms detection and heart rate analysis [J]. Computer Methods and Programs in Biomedicine. 1997,52(1):35-44.
[62] SHUBHA KADAMBE,ROBIN MURRAY, AND G. Faye Boudreaux-Bartels. Wavelet Transform-Based QRS Complex Detector [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 1999,46(7):838-848.
[63]余辉,张力新,吕扬生.基于小波变换的QRS波检测[J].生物医学工程与临床. 2001, 5(2):65-70.
[64]田学隆,闫春红,俞亚青,等.基于小波变换的R波检测算法[J].生物医学工程学杂志.2006,23(2):257-261.
[65]王文,孙世双,周勇.基于小波变换的心电图QRS波群检测方法研究[J].北京生物医学工程. 2002,14(21):241-243.
[66]郑崇勋,骆京安,叶继伦.基于小波变换的运动心电信号去噪方法研究.第四军医大学学报. 2000,21(5):34-37.
[67]李向军,陈裕泉. QRS波群时频检测方法的新进展.国外医学生物医学工程分册. 2005, 28(5):281-286.
[68]余辉,张凯,吕扬生,等.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):334-338.
[69] S. C. SAXENA A, V. KUMAR A, S. T. HAMDE. Feature extraction from ECG signals using wavelet transforms for disease Diagnostics [J]. International Journal of Systems Science. 2002,92(2):1073-1085.
[70]陈培军,陈士贵,俞尧荣,等.二项式小波检测心电特征点[J].航天医学与医学工程. 2004,17(1):59-63.
[71] JUAN PABLO MARTíNEZ, RUTE ALMEIDA, SALVADOR OLMOS,et al. A Wavelet-Based ECG Delineator: Evaluation on Standard Databases [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 2004,51(4):570-581.
[72]胡鹏,张永红,张菊鹏,等.基于小波变换的心律失常判别算法[J].北京生物医学工程. 2003, 22(11):23-26.
[73]杨毓英,史习智,陈光冶,等.小波变换用于ECG信号的R波实时探测[J].上海交通大学学报. 1997,31(9):18-21.
[74]万相奎,秦树人,梁小容,等.小波变换在心电信号特征提取中的应用[J].北京生物医学工程. 2005,24(16):411-413.
[75]尹登峰.动态ECG分析中QRS波检测算法的研究[D].浙江大学硕士学位论文. 2003.
[76]陈永利.动态心电自动分析中QRS复合波检测算法研究[D].浙江大学博士学位论文.2006.
[77]翟爱梅.心电图自动分析技术的研究[D].哈尔滨工业大学硕士学位论文. 2003.
[78]苏丽.远程心电监护诊断系统心电信号处理研究[D].哈尔滨工程大学博士学位论文. 2006.
[79] SENHADJI L, WANG F. Wavelet extrema representation for QRS-T cancellation and P wave detection [J]. Computers in Cardiology. 2002,29:37-40.
[80] M. I VAI AND LI-GAO ZHOU. Beat-to-Beat ECG Ventricular Late Potentials Variance Detection by Filter Bank and Wavelet Transform as Beat-Sequence Filter [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 2004, 51(8):1407-1413.
[81] A. RAKOTOMAMONJY, B. MIGEON P. MARCHE. Automated neural network detection of wavelet preprocessed electrocardiogram late potentials [J]. Medical & Biological Engineering & Computing. 1998,36(3):346-350.
[82]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.
[83]王宏山,杨军,俞梦孙,等.模糊ART神经网络在心律失常分析中的应用[J].中国生物医学工程学报. 2004,23(5):410-413.
[84] DE AZEVEDO BOTTER E,NASCIMENTO CR,YONEYAMA T.A neural network with asymmetric basis functions for feature extraction of ECG P waves [J]. IEEE Trans. Neural Network. 2001,12(5):1252-1255.
[85] D. BENITEZA, P.A. GAYDECKIA, A. ZAIDIB, A.P. Fitzpatrick. The use of the Hilbert transform in ECG signal analysis [J]. Computers in Biology and Medicine. 2001,31(5):399–406.
[86]陈文菊,潘敏,赵治栋,等.基于小波分析和Hilbert变换的R波检测算法[J].传感技术学报. 2006,19(1):248-252.
[87] P.E.TRAHANIAS. An approach to QRS complex detection using mathematical morphology [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 1993,40 (2):201-205.
[88]马焕清,张更生.心电计算机辅助诊断中的波形检测和识别方法的研究[J].中国科学技术大学学报. 1991,2(13):47-56.
[89] SO HH, CHAN KL. Development of QRS detection method for real-time ambulatorycardiac monitor [J]. IEEE-EMBS. 1997,1(30):289-292.
[90] TANKF, CHANKL, CHOI K. Detection of the QRS complex, P wave and T wave in electrocardiogram [C]. Advances in Medical Signal and Information Processing. IEE Conf. 2000:41-47.
[91] STRUMILLO P. Nested median filtering for detecting T-wave offset in ECGs [J]. Electronics Letters. 2002,38(14):682-683.
[92] NITISH V.THAKOR,YI-SHENG ZHU. Application of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection [J].IEEE Transactions on Biomedical Engineering. 1991,38(8):785-793.
[93] EMILIO SORIA-OLIVAS.Application of adaptive signals processing for determining the limits of P and T waves in an ECG [J]. IEEE Transactions on Biomedical Engineering. 1998,45(8):1077-1080.
[94]杨振野,李玲华,林家瑞.一种基于函数逼近原理的心电图P波识别的研究[J].生物医学工程杂志. 1998,15(2):120-122.
[95]骆建华,李高平,庄大戈.演化算法在T波终点检测中的应用[J].中国生物医学工程学报. 2002,21(3):211-214.
[96] STEPHANE MALLAT著,杨力华,等译.信号处理的小波导引[M].北京:机械工业出版社.2003: 9,22-23,58-66,94-116,112-255,331-376.
[97]李建平.小波分析与信号处理——理论、应用及软件实现[M].重庆:重庆出版社. 1997:3-94.
[98]杨福生.小波变换的工程分析与应用[M].北京:科学出版社. 2000: 1-106.
[99]彭玉华.小波变换与工程应用[M].北京:科学出版社. 1999:13-54,88-99.
[100] MARTIN VETTERLI. Wavelets and subband coding [M]. Upper Saddle River, N.J.:Prentice Hall PTR. 1995:209-289.
[101] Y.MEYER.著,尤众译.小波与算子[M].北京,世界图书出版公司. 1992: 32-42.
[102]飞思科技产品研发中心. Matlab6.5辅助小波分析与应用[M].北京:电子工业出版社. 2003:6-49, 109-114.
[103]胡昌华,张军波,夏军等.基于Matlab的系统分析与设计——小波分析[M].西安:西安电子科技大学出版社. 1999:1-23.
[104] INGRID DAUBECHIES著,李建平等译.小波十讲[M].国防工业出版社.2004, 5: 239 -243.
[105] D.L. DONOHO, I.M. JOHNSTONE, G. KERKYACHARIAN, D. PICARD. Wavelet Shrinkage: Asymptopia [J]. J. Roy. Statist. Soc. Ser. B. 1995, 57: 301–369.
[106] I.M. JOHNSTONE, B.W. SILVERMAN. Wavelet threshold estimator for the data with correlated noise [J]. J. Roy. Statist. Soc. Ser. B. 1997, 59: 319–351.
[107] MATHWORKS. Http://www.mathworks.com/access/helpdesk/help/pdf_doc/ wavelet/wavelet_ug.pdf[EB/OL].
[108] GAO,H-Y., AND BRUCE, A.G. WaveShrink with Firm Shrinkage [J]. Statistica Sinica. 1997,7: 855-874.
[109] HONG-YE GAO. Wavelet Shrinkage Denoising Using the Non-Negative Garrote [J]. Journal of Computational and Graphical Statistics. 1998,7(4): 469- 488.
[110] M. JANSEN, M. MALFAIT, A. BUTHEEL. Generalized cross validation for wavelet thresholding [J]. Signal Processing. 1997,45(1):33-44.
[111] WEYRICH, N., WARHOLA, G.T. Wavelet shrinkage and generalized cross validation for image denoising [J].Image Processing. IEEE Transactions. 1998,7 (1):82-90.
[112] JANSEN M, BULTHEEL A. Multiple wavelet threshold estimation by generalized cross validation for images with correlated noise [J]. IEEETRANSACTIONS ON IMAGE PROCESSING. 1999,8(7):947-953.
[113] IAIN M. JOHNSTONE, BERNARD W. SILVERMAN. EbayesThresh: R Programs for Empirical Bayes Thresholding [J]. Journal of Statistical Software. 2005, 12(8):1-38.
[114] IAIN M. JOHNSTONE AND BERNARD W. SILVERMAN. Needles and Straw in Haystacks: Empirical Bayes Estimates of Possibly Sparse Sequences [J]. The Annals of Statistics.2004,32(4):1594-1649.
[115] ANTONIADIS A, JANSEN M, JOHNSTONE IM, SILVERMAN BW. EbayesThresh: MATLAB Software for Empirical Bayes Thresholding. 2004, URL http://www-lmc.imag.fr/lmc-sms/Anestis.Antoniadis/EBayesThresh/.
[116] Wavelab. http://www-stat.stanford.edu/~wavelab [EB/OL].
[117] C. STEIN. Estimation of the mean of a multivariate normal distribution [J]. Ann. Statist. 1981,9(6):1135–1151.
[118] S.R. PETERSON. Some statistical properties of alpha-trimmed mean and standard type M filters [C]. IEEE Trans. ASSP. 1998,36 (5):707-713.
[119] DAUBECHIES I, SWELDENS W. Factoring wavelet transforms into lifting steps [J]. Journal of Fourier analysis and applications. 1998,4(3): 247-269.
[120] SWELDENS W. The lifting scheme: A construction of second generation wavelets [J]. Siam Journal on Mathematical analysis. 1998,29(2): 511-546.
[121] SWELDENS W. The lifting scheme: A custom-design construction of biorthogonal wavelets [J]. Applied and Computational Harmonic Analysis. 1996,3 (2):186-200.
[122]孙红星,王蓉,赵楠楠,等.基于小波提升和形态学的图像边缘检测方法[J].系统仿真学报. 2006, S2:869-871.
[123]李洪刚,吴乐南.基于任意小波的提升格式的设计[J].东南大学学报(自然科学版). 2001,31(14):22-26.
[124]李刚,吴渝,王国胤,等.几种三层小波提升方案及其在图像压缩中的应用[J].计算机科学. 2002,29(11):87-89.
[125] NASON, G.P., B.W. SILVERMAN. The stationary wavelet transform and some statistical applications [C]. Lecture Notes in Statistics. 1995:281-299.
[126]郭锐,彭玉华,万洪林.基于平稳小波变换的掌纹特征提取与识别[J].计算机工程与应用. 2006,42(17):61-65.
[127]罗强,田化梅,罗萍,等.基于平稳小波变换的心电信号去噪研究[J].计算机与数字工程. 2006,34(6):67-70.
[128] GOLDBERGER AL, AMARAL LAN, GLASS L, HAUSDORFF JM, et al.PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220. http://circ.ahajournals.org/cgi/content/full/101/23/e215],2000.
[129]王林泓,杨浩.心电信号处理中滤波器设计的研究[J].北京生物医学工程. 2002, 21(13): 218-221.
[130]朱伟芳,齐春.一种实用的去基线漂移滤波算法[J].苏州大学学报(工科版). 2006, 26(1): 62-64.
[131]陈环,丁永生,吴怡之.面向智能服装健康监护系统的心电信号基线漂移处理[J].计算机应用研究. 2008,25(6):1707-1709.
[132]汪家旺,吴玲燕,杨涛,等.几种去心电基线漂移算法的实现和比较[J].中国医疗器械信息. 2008,14(6):30-33.
[133] P.DE CHAZAL. Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoes [J]. IEEE Trans. Biomed. Eng. 2003,50(6):686-696.
[134]叶文宇.心电自动诊断技术的研究][D].天津大学博士学位论文. 2003.
[135]师黎,杨岑玉,费敏锐.基于小波变换的心电信号R波及ST段的提取[J].仪器仪表学报. 2008,29(4): 804-809.
[136] J. S. SAHAMBI,S.N. TANDON,R.K.P. BHATT. Wavelet based ST-segment analysis [J].Med. Biol. Eng. Comput. 1998,36(5):568-572.
[137] RAMI LEHTINENA, HARRI SIEVANEN, VAINO TURJANMAA,et al. Effect of ST segment measurement point on performance of exercise ECG analysis [J]. International Journal of Cardiology. 1997,61(3):239-245.
[138] MAGLAVERAS N, STAMKOPOULOS T, PAPPAS C,et al. An adaptive backpropagation neural network for real-time ischemia episodes detection: development and performance analysis using the European ST-T database [J]. IEEE Trans. Biomed. Eng. 1998,45(7):805-813.
[139] PAPALOUKAS C, FOTIADIS D I, LIKAS A,et al. An ischemia detection method based on artificial neural networks [J]. Artificial Intelligence in Medicine. 2002,24(2):167–78.
[140]宋喜国,邓亲恺.基于小波变换的ST-T段自动检测[J].中国医学物理学杂志. 2005, 22(4):601-603.
[141]芦继来,胡广书.基于小波变换的运动心电ST段检测方法[J].北京生物医学工程. 2005,24(5):329-333.
[142]陈海燕,黄敏,姜云霞,等.利用小波变换检测心电图ST段[J].电机与控制学报.2006, 10(5):531-533.
[143] JOSéGARCíA, LEIF S?RNMO, SALVADOR OLMOS,et al. Automatic Detection of ST-T Complex Changes on the ECG Using Filtered RMS Difference Series: Application to Ambulatory Ischemia Monitoring [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 2000,47(9):1195-1211.
[144] FAYYAZ A AFSAR, M ARIF, J YANG. Detection of ST segment deviation episodes in ECG using KLT with an ensemble neural classifier [J]. Physiol. Meas. 2008, 29:747–760.
[145]朱伟芳,王文斌,齐春,等.一种适用于Holter系统的ST段分析方案[J].生物医学工程学杂志.2004,21(6):943-946.
[146]滕晓菲,张元亭.移动医疗:穿戴式医疗仪器的发展趋势[J].中国医疗器械杂志. 2006,30(5):330-340.
[147]卜茉.人体生理参数监测系统原理样机的研究[D].吉林大学硕士论文. 2007.
[148]朱建新,高蕾娜,张新访.初探普适时代的健康穿戴系统[J].计算机系统应用.2008, 17(10):2-6.
[149]卢喜烈,主编.心电图诊断与鉴别诊断[M].北京:科学技术文献出版社. 1995,5: 2-11,19-27,170-176.
[150]刘广芝.心电图学概论[M].贵州:贵州科技出版社.2003:157-162, 224-232, 282-397.
[151]卢志刚,薛保延,周立坤.一种心电图自动检测算法[J].山西技术.2005,6:118-123.
[152]田蕴青,郝冬梅.心电图自动诊断系统的研制[J].中国医学物理学杂志. 2000,17 (4):215-217.
[153]朱凌云.移动心电监护系统ECG信号的实时检测算法研究[D].重庆大学博士学位论文. 2003:60-63.
[154] M. G. TSIPOURAS, D. I. FOTIADIS, D. SIDERIS. Arrhythmia classification using the RR-interval duration signal [C]. Computers in Cardiology. 2002: 485-488.
[155] MG TSIPOURAS, Y GOLETSIS1, DI FOTIADIS.A Method for Arrhythmic Episode Classification in ECGs Using Fuzzy Logic and Markov Models [C]. Computers in Cardiology. 2004:361-364.
[156] REN-GUEY LEE,CHI CHOU,CHIEN-CHIN LAI,et al. A Novel QRS Detection Algorithm Applied to the Analysis for Heart Rate Variability of Patients with Sleep Apnea [J]. Biomedical Engineering Applications, Basis & Communications. 2005,17(5): 258-262.
[157] VESSELA T. KRASTEVA, IRENA I. JEKOVA, IVAYLO I. CHRISTOV. Automatic detection of premature atrial contractions in the electrocardiogram [J]. Electrotechniques & Electronics. 2006,9(10): 49-56.
[158] PHILIP DE CHAZAL, MARIA O’DWYER, RICHARD B. Reilly. AutomaticClassification of Heartbeats Using ECG Morphology and Heartbeat Interval Features [J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 2004,51(7): 1196-1206.
[159] DINGFEI GE, NARAYANAN SRINIVASAN, SHANKAR M KRISHNAN. Cardiac arrhythmia classification using autoregressive modeling [J/OL]. BioMedical Engineering OnLine.2002:1-5. http://www.biomedical-engineering-online.com/content/1/1/5.
[160] NICHOLAS ANDRISEVIC, KHALED EJAZ, FERNANDO RIOS-GUTIERREZ, et al. Detection of Heart Murmurs Using Wavelet Analysis and Artificial Neural Networks. Journal of Biomechanical Engineering [J]. 2005,127: 899-995.
[161] E.A.FERNANDEZ. Detection of abnormality in the electrocardiogram without prior knowledge by using the quantization error of a self-organising map, tested on the European ischaemia database [J]. Med. Biol. Eng. Comput. 2001, 39:330-337.
[162] M.G. TSIPOURAS, D.I. FOTIADIS, D. SIDERIS. An arrhythmia classification system based on the RR-interval signal [J]. Artificial Intelligence in Medicine. 2005,33:237-250.