Unsupervised classification of ECG beats using a MLVQ neural network

A modified Learning Vector Quantization (MLVQ) neural network is employed to develop an unsupervised ECG beat classifier. In order to improve the performance of the classifier for application to ECG signals, three modifications are made on the original LVQ: finding the clustering numbers in an unsupervised way by appending two counters, decreasing the inaccuracy caused by the imprecise input features of classifier using multiple assignment of datum, and finding the global optimal classification of input data using double objective functions. This unsupervised classifier is tested with selected ECG time series and experimental results show that the proposed technique offers a great potential in the unsupervised classification of ECG beats