Clustering ECG complexes using Hermite functions and self-organizing maps

An integrated method for clustering of QRS complexes is presented which inc ludes basis function representation and self-organizing neural networks (NN 's), Each QRS complex is decomposed into Hermite basis functions and the re sulting coefficients and width parameter are used to represent the complex. By means of this representation, unsupervised self-organizing NN's are emp loyed to cluster the data into 25 groups. Using the MIT-BIH arrhythmia data base, the resulting clusters are found to exhibit a very low degree of misc lassification (1.5%). The integrated method outperforms, on the MIT-BIH dat abase, both a published supervised learning method as well as a conventiona l template cross-correlation clustering method.