[1]杨向林,严?? 洪,任兆瑞,等.基于PCA特征和融合特征的ECG身份识别方法[J].智能系统学报,2010,5(5):458-463.[doi:10.3969/j.issn.1673-4785.2010.05.014]
YANG Xiang-lin,YAN Hong,REN Zhao-rui,et al.A method based on the PCA feature and fusion feature for ECG human identification[J].CAAI Transactions on Intelligent Systems,2010,5(5):458-463.[doi:10.3969/j.issn.1673-4785.2010.05.014]
点击复制
YANG Xiang-lin,YAN Hong,REN Zhao-rui,et al.A method based on the PCA feature and fusion feature for ECG human identification[J].CAAI Transactions on Intelligent Systems,2010,5(5):458-463.[doi:10.3969/j.issn.1673-4785.2010.05.014]
基于PCA特征和融合特征的ECG身份识别方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
5
期数:
2010年第5期
页码:
458-463
栏目:
学术论文—机器感知与模式识别
出版日期:
2010-10-25
- Title:
- A method based on the PCA feature and fusion feature for ECG human identification
- 文章编号:
- 1673-4785(2010)05-0458-06
- Keywords:
- principal component analysis(PCA); wavelet decomposition; fusion feature; electrocardiogram(ECG); identification
- 分类号:
- TP301.6
- 文献标志码:
- A
- 摘要:
- ECG作为一种活体生物特征用于身份识别在国际上引起了广泛重视.针对基于解析特征的ECG身份识别方法对特征点检测精度要求较高的缺点，提出一种仅需R波峰值点检测的ECG身份识别方法，该方法通过有针对性的设定相应阈值，将PCA特征和小波融合特征方法相结合.实验结果表明该方法优于PCA特征方法、波形特征方法和小波特征方法，既减少了特征点检测的复杂性和特征点检测不准确带来的误差，又可获得较高的识别率，是一种实时、高效算法.
- Abstract:
- As a new biometric for identification, ECG attracted widespread attention in the international community. The method was based on the analytic feature for ECG identification and required high precision for fiducial points detection. To overcome this disadvantage, a method, in which only the peak point of the R-wave detection is needed, was proposed. As for setting the relevant threshold, this method combined the PCA feature method and fusion feature method based on wavelet decomposition. The experiment demonstrates that the method proposed in this paper is better than the PCA feature method, waveform feature method, and wavelet feature method. This method, which not only reduces the complexity and error of the fiducial points detection but also achieves high accuracy, is a realistic and efficient algorithm.
- 参考文献/References:
-
［1］BIEL L, PETTERSSON O, PHILIPSON L, et al. ECG analysis: a new approach in human identification［J］. IEEE Transactions on Instrumentation and Measurement, 2001, 50(3): 808-812.
［2］李中健,王庚勤,贾耀勤,等.应用心电图检查技术在活体个人识别中的研究［J］.中国实用医刊, 2008, 35(4): 23-25.
LI Zhongjian, WANG Gengqin, JIA Yaoqin， et al. Investigation on the possibility of electrocardiography in the identification of individuals［J］. Chinese Journal of Practical Medicine, 2008, 35(4): 23-25.
［3］李中健,井艳,张巧玲,等.活体个人心电波形唯一性探讨［J］.医药论坛杂志, 2008, 29(4): 45-46.
LI Zhongjian, JING Yan, ZHANG Qiaoling, et al. The discussion of the uniqueness of ECG wave［J］. Medicament Forum Magazine, 2008, 29(4): 45-46.
［4］IRVINE J M, WIEDERHOLD B K, GAVSHON L W, et al. Heart rate variability: a new biometric for human identification［C］//International Conference on Artificial Intelligence. Las Vegas, USA, 2001: 1106-1111.
［5］杨向林,严洪.基于心电图身份识别技术综述［J］.仪器仪表学报, 2009, 29(增刊): 695-699.
?YANG Xianglin,YAN Hong. Survey on ECG based huam identification technique［J］.Chinese Journal of Scientific Instrument, 2009, 29(Suppl): 695-699.
［6］WANG Y, PLATANIOTIS K N, HATZINAKOS D. Integrating analytic and appearance attributes for human identification from ECG signal［C］//Proceedings of Biometrics Symposiums. Baltimore, USA, 2006: 1-6.
［7］PALANIAPPAN R, KRISHNUN S M. Identifying individuals using ECG beats［C］//International Conference on Signal Processing and Communications (SPCOM). Bangalore, India, 2004: 569-572.
［8］CASTELLS F, LAGUNA P, BOLLMANN A, et al. Principal component analysis in ECG signal processing: Article ID 74 580［J］. EURASIP Journal on Advances in Signal Processing, 2007(1): 98.
［9］张敏,李陶深,钟淑瑛.基于MATLAB的主成分分析方法(PCA)的实现［J］.广西大学学报：自然科学版, 2005, 30(S2): 74-77.
ZHANG Min, LI Taoshen, ZHONG Shuying. Implementation of principal components analysis method based on Matlab［J］. Journal of Guangxi University: Natural Science Edition, 2005, 30(S2): 74-77.
［10］杨向林,严洪,李延军,等.基于小波分解和数据融合方法的ECG身份识别［J］.航天医学与医学工程, 2009, 22(4): 296-301.
YANG Xianglin, YAN Hong, LI Yanjun, et al. Human identification based on ECG with wavelet decomposition and data fusion method［J］. Space Medicine & Medical Engineering, 2009, 22(4): 296-301.
［11］IRVINE J M, ISRAEL S A, SCRUGGS W T, et al. Eigenpulse: robust human identification from cardiovascular function［J］. Pattern Recognition, 2008, 41: 3427-3435.
［12］CHAN A D C, HAMDY M M, BADRE A, et al. Wavelet distance measure for person identification using electrocardiograms［J］. IEEE Transactions on Instrumentation and Measurement, 2008, 57(2): 248-253.
- 相似文献/References:
-
[1]朱帮助,林? 健.基于支持向量数据描述的无标签数据多类分类[J].智能系统学报,2009,4(2):131.
ZHU Bang-zhu,LIN Jian.Multiclass classification algorithm for unlabeled data using SVDD[J].CAAI Transactions on Intelligent Systems,2009,4():131.
[2]吴朝阳.小波变换和GM-ARMA组合模型的股指预测[J].智能系统学报,2011,6(3):279.
WU Zhaoyang.Using wavelet transformation and a GM-ARMA model to forecast stock index[J].CAAI Transactions on Intelligent Systems,2011,6():279.
[3]李海林,郭韧,万校基.基于特征矩阵的多元时间序列最小距离度量方法[J].智能系统学报,2015,10(3):442.[doi:10.3969/j.issn.1673-4785.201405047]
LI Hailin,GUO Ren,WAN Xiaoji.A minimum distance measurement method for amultivariate time series based on the feature matrix[J].CAAI Transactions on Intelligent Systems,2015,10():442.[doi:10.3969/j.issn.1673-4785.201405047]
[4]刘欢,王骏,邓赵红,等.堆叠隐空间模糊C回归算法及其在发酵数据多模型建模中的应用[J].智能系统学报,2016,11(5):670.[doi:10.11992/tis.201508015]
LIU Huan,WANG Jun,DENG Zhaohong,et al.A cascaded hidden space fuzzy C-regression algorithmand its application in multi-model modeling for thefermentation process[J].CAAI Transactions on Intelligent Systems,2016,11():670.[doi:10.11992/tis.201508015]
[5]杨晓兰,强彦,赵涓涓,等.基于医学征象和卷积神经网络的肺结节CT图像哈希检索[J].智能系统学报,2017,12(6):857.[doi:10.11992/tis.201706035]
YANG Xiaolan,QIANG Yan,ZHAO Juanjuan,et al.Hashing retrieval for CT images of pulmonary nodules based on medical signs and convolutional neural networks[J].CAAI Transactions on Intelligent Systems,2017,12():857.[doi:10.11992/tis.201706035]
[6]许子微,陈秀宏.自步稀疏最优均值主成分分析[J].智能系统学报,2021,16(3):416.[doi:10.11992/tis.201911028]
XU Ziwei,CHEN Xiuhong.Sparse optimal mean principal component analysis based on self-paced learning[J].CAAI Transactions on Intelligent Systems,2021,16():416.[doi:10.11992/tis.201911028]
备注/Memo
收稿日期：2009-12-12.
基金项目：中国航天医学工程预先研究项目（SJ200903）.
通信作者：严??? 洪.Email:hholden@sina.com.
作者简介：
杨向林，男，1984年生,研究实习员，主要研究方向为生物医学信息处理和模式识别.负责承担中国航天医学工程预先研究项目1项，申请国家发明专利1项，发表学术论文13篇.
严?? 洪，男，1962年生,研究员,主要研究方向为生物医学信号处理和图像处理.
任兆瑞，男，1978年生，助理研究员，主要研究方向为科研管理.
更新日期/Last Update:
2010-11-26