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Diagnosis of abnormal echocardiography based on moving window FICA and SOM


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Diagnosis of abnormal echocardiography based on moving window FICA and SOM
Yang QingchuanYang QingYao XinLiu Hongbin
School of Information Science and Engineering,Shenyang Ligong University,Shenyang 110159,China
fast independent component analysis self-organizing maps abnormal echocardiography fault classification
To deal with the problem of learning rate and convergence in online independent component analysis(FICA)algorithm,an improved fast independent component analysis algorithm with variable moving windows attached to real-time signal is presented here.This algorithm which saves storage space and computing time,can not only meet the requirements of online processing,but also do not need to consider learning rate.With the advantage of self-organizing map neural network algorithm on the dynamic classification,the combined approach based on variable moving window FICA and self-organizing maps(SOM)neural network is used to classify the abnormal echocardiography data.The experiments show that this method can effectively improve the rate and realize real-time fault classification.


[1] 王恩美,范鑫,李春胜,等.一种新型心电信号采集及分析系统[J].仪器仪表学报,2001,22(4):368-369.
Wang Enmei,Fan Xin,Li Chunsheng,et al.A new sampling and analytic system for ECG signal[J].Chinese Journal of Scientific Instrument,2001,22(4):368-369.
Wu Cheng.Application of variable learning rate on-line ICA algorithm in radar signal classification[J].Modern Radar,2010,32(1):52-53.
Zheng Yujie,Yang Jingyu,Wu Xiaojun,et al.Feature extraction based on symmetrical ICA and its application to face recognition[J].Journal of Nanjing University of Science and Technology,2006,19(1):116-212.
Wu Xiaopei,Ye Zhongfu,Guo Xiaojing,et al.Independent component analysis based on sliding window[J].Journal of Computer Research and Development,2007,44(1):185-191.
[8]Zhou Shaoyuan,Xie Lei,Wang Shuqing.On-line fault diagnosis in industrial processes using variable moving window and hidden Markov model[J].Chinese Journal of Chemical Engineering,2005,13(3):388-395.
Zeng Zhiqiang,Wang Junyuan,Ma Weijin.Experimental study of feature extraction based on independent component analysis and evaluation of feature independence[J].Journal of North University of China(Natural Science Edition),2009,30(6):524-529.
Li Suyi,Lin Jun.ECG signal de-noising using a combined wavelet transform algorithm[J].Chinese Journal of Scientific Instrument,2009,30(4):689-690.
Yu Dongjun,Chen Yihua,Yu Haiying.Fuzzy rule extraction by fusing SOM and Wang-Mendel method[J].Journal of Nanjing University of Science and Technology,2011,35(6):759-763.
Qi Yong,Hu Jun,Yu Dongjun.Incremental learning algorithm based on self organizing map and probabilistic neural network[J].Journal of Nanjing University of Science and Technology,2013,37(1):1-6.
[16]Sufi F,Khalil I,Mahmood A N.A clustering based system for instant detection of cardiac abnormalities from compressed ECG[J].Expert Systems with Applications,2011:38(5):4705-4713.
[17]Martis R J,Acharya U R,Min L C.ECG beat classification using PCA,LDA,ICA and discrete wavelet transform[J].Biomedical Signal Processing and Control,2013,8(5):437-448.
[18]Kabir M A,Shahnaz C.Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains[J].Biomedical Signal Processing and Control,2012:7(5):481-489.
[19]Ari S,Das M K,Chacko A.ECG signal enhancement using S-transform[J].Computers in Biology and Medicine,2013,43(6):649-660.
[20]Goldberger A L,Amaral L A N,Glass L,et al.Components of a new research resource for complex physiologic signals[EB/OL].http://www.physionet.org/physiobank/database/ptbdb/2013-01-04.
Xie Yanjiang,Yang Zhi,Fan Zhengping,et al.Application of wavelet to the cancellation of ECG interference in diaphragmatic EMG[J].Acta Electronic Sinica,2010,38(2):366-369.


Last Update: 2013-08-31