Computer Science ›› 2015, Vol. 42 ›› Issue (Z11): 32-35.

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Premature Beat Signal Recognition Algorithm Based on Wavelet Transform and Rough Set

TANG Xiao, SHU Lan and ZHENG Wei   

  • Online:2018-11-14 Published:2018-11-14

Abstract: The selection and extraction of electrocardiogram feature parameter are the base of the analysis of electrocardiogram(ECG).To improve recognition rate of detection algorithm and classification accuracy is the key to automatic analysis technology.Thus,a hybrid algorithm based on wavelet transform(WT) and attribute reduction of granular computing(GC) to detect premature beat signal of electrocardiogram(ECG) was present.At first,12 electrocardiogram feature parameters are chosen based on diagnostic criteria from cardiovascular experts.Then the feature detection algorithm based on wavelet transform is used for feature extraction,and an attribute reduction algorithm based on granular computing is also used for attribute reduction.Finally,the data are put into pattern classification and the result is verified by MIT-BIH database.As the experiment shows,the classification accuracy after reduction is much higher than it before reduction.Therefore,that reasonable selection of feature parameter is an important factor to improve the recognition efficiency was justified in this article.

Key words: ECG,WT,Feature extraction,Attribute reduction,Granular computing

[1] Trahanias P,Skordalakis E.Syntactic pattern recognition of the ECG[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,1990,12(7):648-657
[2] Koski A,Juhola M,Meriste M.Syntactic.Recognition of ECG Signals by Attributed Finite Automata[J].Pattern Recognition,1995,28(12):1927-1940
[3] 戚建新,卞正中,杨强.基于微机的心电信号实时自动分析系统[J].北京生物医学工程,1997,16(3):157-161
[4] Engin M.ECG beat classification using neuro-fuzzy network[J].Pattern Recognition,2004,25(15):1715-1722
[5] 季虎,孙即祥.心电信号自动分析关键技术研究[D].长沙:国防科技大学,2006
[6] Tantawi M M,Revett K,Salem A,et al.An evaluation of the generalisability and applicability of the PhysioNet electrocardiogram(ECG) repository as test cases for ECG-based biometrics[J].International Journal of Cognitive Biometrics,2012,1(1):66-97
[7] Tantawi M M,Revett K,Salem A,et al.Fiducial Feature Reduction Analysis for Electrocardiogram(ECG) Based Biometric Recognition[J].Journal of Intelligent Information Systems,2013,40:17-39
[8] Tang X,Shu L.A Frame work of Automatic Analysis System of Electrocardiogram Signals[J].International Journal of Signal Processing,Image Processing and Pattern Recognition,2014,7(2):211-222
[9] 陈文利,莫智文,郭文.基于小波变换和黄金分割搜索法的QRS波检测算法[J].生物医学工程学杂志,2009,6(4):748-751
[10] 张文修,吴伟志,梁吉业,等.粗糙集理论与方法[M].北京:科学出版社,2001
[11] 梁吉业,李德玉.信息系统中的不确定性与知识获取[M].北京:科学出版社,2005
[12] 赵敏,罗可,秦哲.基于粒计算的属性约简算法[J].计算机工程与应用,2008,44(30):157-159
[13] 唐孝,舒兰.基于粒计算的属性约简改进算法[J].计算机科学,2014,41(11A):313-315

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