基于容性耦合电极的可穿戴心电信号检测及其去噪算法研究
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摘要
针对传统心电检测系统存在佩戴电极不方便和电极导电膏易脱水等问题,在研究分析电容耦合工作原理的基础上,设计了一种可穿戴容性耦合电极。针对这种可穿戴容性耦合电极,提出了一种改进小波阈值去噪算法,该算法结合心电信号与噪声小波系数分布特性,采用改进阈值函数对分解后小波系数量化处理并重构心电信号。利用MIT-BIH数据库验证,该算法能有效消除心电信号中的噪声干扰,相比平滑滤波、数学形态滤波和经验模式分解信噪比提高了10.72%,均方误差减小了27.29%。心电检测实验表明可穿戴容性耦合心电信号检测系统能够准确检测出人体心电信号主要特征。
In most traditional electrocardiogram( ECG) detection procedures,wet electrodes may cause problems of inconvenience and glue dehydrates over time. The paper designs a kind of wearable capacitive coupling electrode based on the principle of coupling capacity. Due to this kind of wearable capacitive coupling electrode,an improved wavelet threshold de-noising algorithm is proposed. The algorithm uses the improved threshold function to deal with wavelet coefficients after decomposition and reconstruct ECG signal combining the characteristics of wavelet coefficients of ECG signal and noise. The MIT-BIH database was used to validate the algorithm and it indicates that the algorithm can effectively eliminate the noise. The SNR increased by 10. 72% and the RMSE reduced by 27. 29%compared to the other methods,such as,smoothing filtering,morphological filtering and empirical mode decomposition.The results of the experiment show that the system can accurately detect the main characteristics of the ECG signal.
In most traditional electrocardiogram( ECG) detection procedures,wet electrodes may cause problems of inconvenience and glue dehydrates over time. The paper designs a kind of wearable capacitive coupling electrode based on the principle of coupling capacity. Due to this kind of wearable capacitive coupling electrode,an improved wavelet threshold de-noising algorithm is proposed. The algorithm uses the improved threshold function to deal with wavelet coefficients after decomposition and reconstruct ECG signal combining the characteristics of wavelet coefficients of ECG signal and noise. The MIT-BIH database was used to validate the algorithm and it indicates that the algorithm can effectively eliminate the noise. The SNR increased by 10. 72% and the RMSE reduced by 27. 29%compared to the other methods,such as,smoothing filtering,morphological filtering and empirical mode decomposition.The results of the experiment show that the system can accurately detect the main characteristics of the ECG signal.
引文
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[2]Baek H J,Chung G S,Kim K K,et al.A Smart Health Monitoring Chair for Nonintrusive Measurement of Biological Signals[J].IEEE Transactions on Information Technology in Biomedicine,2012,16(1):150-158.
[3]高山,李永勤,魏良,等.基于电容耦合的非接触式心电监测技术研究[J].生物医学工程学杂志,2015,32(3):553-557.
[4]周平,汪丰,刘梦星,等.非接触式心电测量的噪声抑制研究[J].仪器仪表学报,2013,34(10):2226-2230.
[5]田絮资,杨建,黄力宇.心电信号去噪的数学形态学滤波器[J].计算机工程与应用,2012,48(2):124-126.
[6]黄进文,王威廉.平滑滤波对ECG的R波幅值影响研究[J].云南大学学报,2008,30(6):559-563.
[7]王玉静,宋立新.基于EMD和Hilbert变换的心电信号去噪方法[J].哈尔滨理工大学学报,2007,12(4):70-73.
[8]周鹏,高雄飞,张玉满,等.基于驻极体传声器的脉象检测系统和方法[J].传感技术学报,2015,28(3):374-380.
[9]高云,周聪聪,田健,等.基于多传感器的无创血压测量系统的研究[J].传感技术学报,2015,28(5):763-767.
[10]杨斌,董永贵.电容耦合非接触电极及心电信号拾取[J].仪器仪表学报,2015,36(5):1072-1078.
[11]曹京京,胡辽林,赵瑞.一种改进小波阈值函数的光纤光栅传感信号去噪方法[J].传感技术学报,2015,28(4):521-525.
[12]王小飞,李鸿强,陈磊,等.基于提升小波和改进包络的心电特征检测算法[J].系统仿真学报,2013,25(12):2893-2899.
[13]徐洁,王阿,郑小锋.基于小波阈值去噪的心电信号分析[J].计算机仿真,2011,28(12):260-263.
[14]欧阳波,程栋,王玲.改进小波阈值算法在心电信号去噪中的应用[J].计算机工程与应用,2015,51(4):213-217.
[15]Donoho D L,Johnstone J M.Ideal Spatial Adaptation by Waveletshrinkage[J].Biometrika,1994,81(3):425-455.
[16]Mallat S G.A Theory for Multiresolution Signal Decomposition the Wavelet Representation[J].IEEE Transaction on Pattern Analysis and Machine Itelligence,1989,11(7):674-693.