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Fuzzy Clustering Application Research in the Classification of ECG Waveform

Author: LiGuiJuan
Tutor: ZhaoQi
School: Hebei University of Engineering
Course: Applied Computer Technology
Keywords: Clustering FCM algorithm SOM neural network System clustering ECG
CLC: TP311.13
Type: Master's thesis
Year: 2011
Downloads: 21
Quote: 1
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Abstract


Cardiovascular disease is one of the major diseases that endanger human health today , ECG is an important method for the clinical diagnosis of cardiovascular disease . ECG accurate automatic classification plays a key role in the diagnosis of cardiovascular disease . The method of cluster analysis and non- supervision is an important branch of pattern recognition , which is used in mathematics research and treatment for a given object classification . The fuzzy clustering built on the basis of the sample for class uncertainties described more objectively reflect the real world , thus becoming the mainstream of the cluster analysis study , and has been widely used in many fields . Have been proposed many fuzzy clustering algorithm , which is the most commonly used is based on the objective function of fuzzy c- means clustering algorithm ( FCM ) . Need clustering algorithm priori knowledge SOM neural network algorithm FCM algorithm as the pilot stage , the first sample after the SOM training of the neural network to obtain the clustering number of categories , but this method the number of classes The large deviation actual results . Therefore proposes an improved method , upcoming SOM neural network system optimized clustering method and FCM clustering method combination . First of system clustering method to optimize , and then use the results of the analysis of the initial classification of SOM neural network system optimized clustering method , and ultimately get more reasonable clustering number of categories and cluster centers , this number of clusters and cluster Center for FCM algorithm input for further clustering , to obtain accurate information about the clustering . Finally , using the data in the MIT / BIH ECG database simulation results suggest that this method has good clustering effect .

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