Feature selection for diagnosis of vectorcardiograms

The automatic classification of vectorcardiograms and electrocardiograms into disease classes using computerized pattern recognition techniques has been a much studied problem. To date, however, no system exists which meets desired accuracy and noise immunity requirements and development of new techniques continues. An important aspect of the problem is that of feature selection, in which the functions of data reduction and information preservation are performed. In this paper, the problem of linear feature extraction is studied and a modified form of the Karhunen-Loeve expansion is developed which appears to have some advantages for the present application. Comparison with other feature selection methods is made using a two-dimensional example. Finally, some areas for future research are pointed out.