ST segment nonlinear principal component analysis for ischemia detection

PCA is most effective for distributions which are close to Gaussian. However, typical ST segments are not nearly symmetric. Nonlinear principal component analysis (NLPCA) is a rather new technique for nonlinear feature extraction which is usually implemented by a 5-layer neural network. It has been observed to have better performance, compared to PCA, in complex problems where the relationships between the variables are not linear. The authors apply NLPCA techniques for ST segment feature extraction and they use the NLPCA features to classify each segment into one of 4 classes: normal, ST+, ST-, or artefact. The authors´ results from the European ST-T database show that using only 2 nonlinear components trained on a set of 1000 normal samples from each file they are often capable of achieving a classification rate of more than 90% with a false alarm rate of less than 10%, while the classification rate rarely falls below 80%. This is an encouraging result which can be further improved with the use of more nonlinear component features or more complex classifiers.