生物信号噪声消除的数字滤波器研究
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摘要
微弱生物电信号通常混叠有不同种类的伪迹成分(如:眼电、脑电、心电、肌电、工频干扰等),具有幅值小、噪声强的特点,因此能否有效去除微弱生物电信号中的噪声,并提取有用信息对生物电信号的研究和临床应用具有重要意义。
此外,作为一种非平稳性比较突出的随机电生理信号,一般只有从大量的数据中得出的统计结果才具有诊断价值,传统的生物电检测仪器体积庞大、价格昂贵,得不到确切的诊断结果。
本文针对上述问题,在深入研究以脑电和心电为代表的微弱生物电信号基本知识的基础上,设计并详细研究了简单数字滤波器的算法和信号采集放大实现电路。推导出数字梳状滤波器的算法,并且首次把这种算法运用到振幅整合脑电仪(CFM)中,取得了良好的效果,文中对具体的电路设计原理图和实验方法进行了详细的介绍。在此基础上本文还创新的提出一种有效的滤波算法——带锁相环的自适应梳状滤波器。这种方法滤波利用锁相环对工频信号及其倍频进行采样,不但节约成本,容易实现,而且可以跟踪电源频率,完全滤除工频干扰及其高次谐波。这些算法和电路虽然以脑电和心电为例研究,但在研究其他生物信号的时候同样可以举一反三。
Biomedical signals, buried into the strong noises, are always mixed with kinds of artifacts such as EOG, EEG, ECG, EMG, 50 Hz interferences and so on. Therefore, it’s very important to remove noise and extract useful signal for biomedical science research and medical diagnosis.
As a kind of non-stationary random bioelectricity, the valuable data only can be got from statistical result from large numbers of experimental data.The traditional analyzers are expensive, and usually worke dinaccurate, which is not convenient to use.
According to these problems, this dissertation originally researched acquisition and processing of Biomedical signals on the foundation of basic Biomedical signals knowledge. The study focuses on the digital filter, especially simple digital filter with integer coefficients. Signal sampling and amplifying are also introduced in the ciucirt explanation. As results, we have deduced a useful digital comb filter, which is already sucssessfully imbedded in CFM (cerebral function monitor). We give the circirt and detailed explanation of this monitor in our project, which is still a fresh domestic study. Above all, we creatively give one good filtering algorithm here, which is the adaptive comb filter with PLL. It is to sample the Power Frequency Signal and its multiple frequencies with PLL. It could be used to follow the power frequence and the high harmonics without frequency interference. Not only could it help save cost, but also easy to come true.
Although we used ECG and EEG as our samples, these methods could also be wildly used in other Biomedical signals.
此外,作为一种非平稳性比较突出的随机电生理信号,一般只有从大量的数据中得出的统计结果才具有诊断价值,传统的生物电检测仪器体积庞大、价格昂贵,得不到确切的诊断结果。
本文针对上述问题,在深入研究以脑电和心电为代表的微弱生物电信号基本知识的基础上,设计并详细研究了简单数字滤波器的算法和信号采集放大实现电路。推导出数字梳状滤波器的算法,并且首次把这种算法运用到振幅整合脑电仪(CFM)中,取得了良好的效果,文中对具体的电路设计原理图和实验方法进行了详细的介绍。在此基础上本文还创新的提出一种有效的滤波算法——带锁相环的自适应梳状滤波器。这种方法滤波利用锁相环对工频信号及其倍频进行采样,不但节约成本,容易实现,而且可以跟踪电源频率,完全滤除工频干扰及其高次谐波。这些算法和电路虽然以脑电和心电为例研究,但在研究其他生物信号的时候同样可以举一反三。
Biomedical signals, buried into the strong noises, are always mixed with kinds of artifacts such as EOG, EEG, ECG, EMG, 50 Hz interferences and so on. Therefore, it’s very important to remove noise and extract useful signal for biomedical science research and medical diagnosis.
As a kind of non-stationary random bioelectricity, the valuable data only can be got from statistical result from large numbers of experimental data.The traditional analyzers are expensive, and usually worke dinaccurate, which is not convenient to use.
According to these problems, this dissertation originally researched acquisition and processing of Biomedical signals on the foundation of basic Biomedical signals knowledge. The study focuses on the digital filter, especially simple digital filter with integer coefficients. Signal sampling and amplifying are also introduced in the ciucirt explanation. As results, we have deduced a useful digital comb filter, which is already sucssessfully imbedded in CFM (cerebral function monitor). We give the circirt and detailed explanation of this monitor in our project, which is still a fresh domestic study. Above all, we creatively give one good filtering algorithm here, which is the adaptive comb filter with PLL. It is to sample the Power Frequency Signal and its multiple frequencies with PLL. It could be used to follow the power frequence and the high harmonics without frequency interference. Not only could it help save cost, but also easy to come true.
Although we used ECG and EEG as our samples, these methods could also be wildly used in other Biomedical signals.
引文
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[2] 王宝华. 生物医学电子学. 高等教育出版社,1992
[3] G.Zouridakis, D, Iyer. “Comparison between ICA and Wavelet-Based Denoising of Single-TrialEvoked Potentials”.Proceedings of the 26th Anuual International Conference of the IEEE EMBS.September, 2004: 87-90
[4] 季忠, 秦树人, 彭丽玲. 脑电信号的现代分析方法.重庆大学学报,Vol.25, No.9, 2002: 108-112
[5] 沈民奋, 沈凤鳞等. 不同状态下脑电信号的双谱分析,生物物理学报,Vol.14,No.1, 1998: 51-57
[6] 张贤达. 现代信号处理. 北京:清华大学出版社,1995
[7] 成礼智, 王红霞, 罗永.小波的理论于应用. 北京:科学出版社,2004
[8] 沈凤麟, 陈和晏. 《生物医学随机信号处理》第1 版, 中国科学技术大社, 1999: P416-456
[9] 孟欣, 欧阳楷. 脑电信号的几个非线性动力学分析方法. 北京生物医学工程,1997,16(3).
[10] 田心. 脑电的非线性动力学研究中的问题和进展. 国外医学BME分册.1999: 22(4)
[11] Maciej Drozd, Peter Husar, Antoni Nowakowski, Guenter Henning. Detecting evoked potentials with SVD-and ICA-based statistical Models. IEEE Engineering in medicine and biology magazine. January/February 2005:51-58.
[12] C.Bigan, M, Woolfson.Independent component extraction methods in biosignal processing. Proceedings of the 26th Annual International Conference of the IEEE EMBS.2004:511-513
[13] Yong Hee Lee, Sun I.Kim, Doo Soo Lee.Estimation of Evoked Potentials Based on the Wavelet Analysis.Proceedings19thInternationalConference-IEEE/EMBS.Oct.-Nov.1997:14641467
[14] M.Potter, N.Gadhok, W.Kinsner.Separation performance of ICA on simulated EEG and ECG signals contaminated by noise.2002:1099-1104.
[15] Zhou Weidong, Li Yingyuan. EEG multi-resolution analysis using wavelet transform Proceeding of the 23rd Annual EMBS International Conference.Oct.2001:1854-1856
[16] 吴小培, 冯焕清, 周荷琴, 王淘. 基于小波变换的脑电信号噪声消除方法.Journal of Circuits and Systems.Vol.5, No.3, 2000:96-98
[17] 李锡杰, 师硕, 王旭, 生物电信号的滤波与现代分析方法, 医学信息, 2005年12月, 第18卷第12期, 1595～1598
[18] 王林泓, 杨浩, 心电信号处理中滤波器设计的研究,北京生物医学工程, 2002年9月, 21卷3期,218～221
[19] 王志中, 王一抗, 朱贻盛.数字陷波器的实现方法[J].上海交通大学学报, Vol.29 Sup.I 1995: 21-25
[20] Haykin S, et al. Monitoring Neuronal Oscillations and Signal Transmission Between Cortical Regions Using Time-Frequency Analysis of Electroencephalographic Activity, Proceedings of the IEEE,Sep.1996(9),84(9):1295-1301
[21] Blanco S,Figliola A,Quiroga Rq et al.,Time-Frequency Analysis of Electroencephalogram series.Ⅲ. Wavelet packets and imformation cost function,Phys. Rev. E,1998(1),57(1),932-940
[22] Boashash B, Estimating and Interpreting the Instantaneous Frequency of A signal—Part 1: Fundermentals, Proceedings of the IEEE,1992(4),80(4),520-568
[23] Blanco S, Quiroga RQ,Rosso QA et al.,Time-Frequency Analysis of Electroencephalograms series,Phys. Rev. E,1995(3), 51(3),2624-2631
[24] Williams WJ, Reduced Interference Distributions: Biological Applications and Interpretations,Proceedings of the IEEE, 1996(9),84(9),1264-1280
[25] Sun Mingui, Qian Shie, Yan Xiaopu et al., Localizing Functional Activity in the Brain Through time-Frequency Analysis and Synthesis of the EEG, proceedings of the IEEE 1996(9),84(9),1302-1311
[26] Jan Axelson[美].并行端口大全[M].中国电力出版,2001年
[27] 邓思豪, 曾春年. C54x DSP的HPI与PC机并口技术研究[[J]电子产品世界.2002年7月
[28] 俞仁康, 寿文德. 医学仪器原理与设计.上海交通大学出版社, 1990
[29] 谭郁玲.临床脑电图气脑电地形图学.人民卫生出版社, 1999.
[30] 中华医学会儿科学会新生儿学组. 新生儿缺氧缺血性脑病诊断依据和临床分度. 中华儿科杂志, 1997,35(2): 99-100
[31] Hellstrom-Westas L, Linda S, Rosen I, et al. An atlas of amplitude-integrated EEGs in the newborn. London: The Parthenon Publishing Group, 2002.11-22.
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