Ischemic classification techniques using an advanced neural network algorithm

The correct classification of the beats relies heavily on the efficiency of the features extracted from the ST-segment and on the desired abilities of algorithm on sensitivity and specificity indices. Nonlinear Principal Component Analysis (NLPCA) is a recently proposed method for nonlinear feature extraction. It has been observed to have better performance for representing complex ST segment features of normal and abnormal cases. The function of representation was created using only normal patterns from the same patient. The distribution of these patterns is modeled using a Radial Basis Function Network (RBFN). This model is capable of finding abnormal patterns with high sensitivity while the specificity is also acceptable (>70%), and the authors can accomplish correct classification rates of higher than 90% for the ischemic beats in many files of the European ST-T database. This technique may be used, in general, for other classification problems in medicine and other disciplines