Fuzzy Clustering Application Research In The Classification Of ECG Waveform

Cardiovascular disease is one of major disease which endangers human's health. The analysis of ECG(Electrocardiogram) is an important means for diagnosing cardiovascular disease in clinic. The accurate automatic classification of ECG plays a key role for the diagnosis of cardiovascular disease.The cluster analysis is an important branch of non-supervision pattern recognition, which uses the methods of mathematics to research and deal with given classification of objects.Fuzzy clustering based on the uncertain description for sample to category, which can more objectively reflect the real world, thus becoming the mainstream of cluster analysis, and has been widely used in many areas.At present, many algorithms for fuzzy clustering are proposed, in which the most commonly used is the fuzzy c-means clustering algorithm based on the objective function.For the problem of the algorithm which requires clustering prior knowledge, we take the SOM neural network algorithm as the forerunner of FCM algorithm. Using the SOM neural network to train samples, and get the number of clustering categories. But it has a big deviation in the actual results and the results for using this method. Therefore, this paper proposes an improved method, which is a combination clustering method of the SOM neural network, the optimizing system algorithm and the FCM algorithm. First, optimizing the system clustering method, then using the optimized system clustering method to analyze the initial classification results of SOM neural network, ultimately get more reasonable number of cluster categories and cluster centers, through using the number of cluster categories and cluster centers as the input of FCM clustering algorithm to further cluster, so that get accurate clustering information.Finally, take the data in the MIT / BIH ECG database to simulate, and show that this method has good clustering effect through the results.