An integrated method using Hermite basis functions and self-organizing neural networks clustered QRS complexes from the MIT-BIH arrhythmia database with a very low misclassification rate of 1.5%.
An integrated computational method using Hermite functions and self-organizing neural networks effectively clusters QRS complexes with a low misclassification rate of 1.5%.
An integrated method for clustering of QRS complexes is presented which includes basis function representation and self-organizing neural networks (NN's). Each QRS complex is decomposed into Hermite basis functions and the resulting coefficients and width parameter are used to represent the complex. By means of this representation, unsupervised self-organizing NN's are employed to cluster the data into 25 groups. Using the MIT-BIH arrhythmia database, the resulting clusters are found to exhibit a very low degree of misclassification (1.5%). The integrated method outperforms, on the MIT-BIH database, both a published supervised learning method as well as a conventional template cross-correlation clustering method.
Lagerholm et al. (2000) studied Arrhythmia (n=48). Hermite functions and self-organizing maps (SOM) vs. Mixture-of-expert model and cross-correlation method was evaluated on Misclassification rate of QRS complexes. An integrated method using Hermite basis functions and self-organizing neural networks clustered QRS complexes from the MIT-BIH arrhythmia database with a very low misclassification rate of 1.5%.