Classifying tachycardias via high dimensional linear discriminant function and perceptron with mult-piece domain activation function

This paper proposes a novel method for discriminating the supraventricular tachycardias and the ventricular tachycardias via a high dimensional linear discriminant function and a perceptron with a multi-piece domain activation function having multi-level functional values. The algorithm is implemented via the mobile application. First, the discrete cosine transform is applied to each training electrocardiogram. Then, these discrete cosine transform coefficients are scaled down according to their frequency indices. These scaled discrete cosine transform coefficients of each electrocardiogram are employed as features for performing the discrimination. Second, the high order statistic moments of each feature of the training electrocardiograms corresponding to the same type of tachycardias are evaluated. These high order statistic moments of each feature corresponding to same type of tachycardias form a vector. Third, the high dimensional linear discriminant function is employed to minimize the intraclass separation and maximize the interclass separation of these statistic moment vectors. In particular, new vectors are formed by projecting these statistic moment vectors to the high dimensional linear discriminant function. Fourth, the principal component analysis is employed to reduce the dimension of the projected vectors. Finally, a bank of perceptrons with multi-piece domain activation functions having multi-level functional values is employed for performing the discrimination. By using this bank of perceptrons, the condition for general two class pattern recognition problems achieving the error free pattern recognition performance is guaranteed. Computer numerical simulation results show that our proposed method is robust and effective.