|Table of Contents|

Diagnosis of abnormal echocardiography based on moving window FICA and SOM

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2013年04期
Page:
530-
Research Field:
Publishing date:

Info

Title:
Diagnosis of abnormal echocardiography based on moving window FICA and SOM
Author(s):
Yang QingchuanYang QingYao XinLiu Hongbin
School of Information Science and Engineering,Shenyang Ligong University,Shenyang 110159,China
Keywords:
fast independent component analysis self-organizing maps abnormal echocardiography fault classification
PACS:
R318.6
DOI:
-
Abstract:
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.

References:

[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.
[2]邬诚.变学习速率在线ICA算法在雷达信号分选中的应用[J].现代雷达,2010,32(1):52-53.
Wu Cheng.Application of variable learning rate on-line ICA algorithm in radar signal classification[J].Modern Radar,2010,32(1):52-53.
[3]杨福生,洪波.独立分量分析的原理与应用[M].北京:清华大学出版社,2006.
[4]史习智.盲信号处理:理论与实践[M].上海:上海交通大学出版社,2008.
[5]郑宇杰,杨静宇,吴小俊,等.基于对称ICA的特征抽取方法及其在人脸识别中的应用[J].南京理工大学学报,2006,19(1):116-212.
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.
[6]曾生根.快速独立分量分析方法及其在图像分析中的若干应用研究[D].南京:南京理工大学自动化学院,2004:26-40.
[7]吴小培,叶中付,郭晓静,等.基于滑动窗口的独立分量分析算法[J].计算机研究与发展,2007,44(1):185-191.
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.
[9]曾志强,王俊元,马维金.基于ICA的特征提取实验研究及特征独立性评价[J].中北大学学报(自然科学版),2009,30(6):524-529.
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.
[10]李肃义,林君.一种综合小波变换的心电信号消噪法[J].仪器仪表学报,2009,30(4):689-690.
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.
[11]陈小丽.基于SOM算法的中文文本聚类[D].南京:南京理工大学自动化学院,2008:22-50.
[12]於东军,谌贻华,于海瑛.融合自组织映射与Wang-Mendel方法的模糊规则提取[J].南京理工大学学报,2011,35(6):759-763.
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.
[13]戚湧,胡俊,於东军.基于自组织映射与概率神经网络的增量式学习算法[J].南京理工大学学报,2013,37(1):1-6.
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.
[14]王君.基于SOM聚类分析的应用与研究[D].西安:西安工业大学自动化学院,2006:18-40.
[15]杨子华.睡眠呼吸暂停综合症自动监测分析系统——心电图自动检测与分析技术的研究[D].南京:南京理工大学自动化学院,2009:5-50.
[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.
[21]谢燕江,杨智,范正平,等.应用小波变换去除膈肌肌电图信号中的心电干扰[J].电子学报,2010,38(2):366-369.
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.
[22]ECG数据库[EB/OL].http://archive.ics.uci.edu/ml/machine-learning-databases/echocardiogram/2013-01-12.

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Last Update: 2013-08-31