ECG classification with neural networks and cluster analysis

The combination of two techniques of pattern recognition i.e., cluster analysis and neural networks, is investigated in the specific problem of the diagnostic classification of 12-lead electrocardiograms (ECGs). For this study a previously used database, established at the University of Leuven, has been employed. Sensitivity, specificity, and total and partial accuracy were the indices used for the assessment of the performance. Several neural networks have been obtained by either varying the training set (considering clusters of the original learning set) or adjusting some components of the architecture of the networks. The combination of different neural networks has shown satisfactory performances in the diagnostic classification task.<<ETX>>