Principal Component Analysis based on data characteristics for dimensionality reduction of ECG recordings in arrhythmia classification

Abstract

Due to the growing problem of heart diseases, the computer improvement of their diagnostics becomes of great importance. One of the most common heart diseases is cardiac arrhythmia. It is usually diagnosed by measuring the heart activity using electrocardiograph (ECG) and collecting the data as multidimensional medical datasets. However, their storage, analysis and knowledge extraction become highly complex issues. Feature reduction not only enables saving storage and computing resources, but it primarily makes the process of data interpretation more comprehensive. In the paper the new igPCA (in-group Principal Component Analysis) method for feature reduction is proposed. We assume that the set of attributes can be split into subgroups of similar characteristic and then subjected to principal component analysis. The presented method transforms the feature space into a lower dimension and gives the insight into intrinsic structure of data. The method has been verified by experiments done on a dataset of ECG recordings. The obtained effects have been evaluated regarding the number of kept features and classification accuracy of arrhythmia types. Experiment results showed the advantage of the presented method compared to base PCA approach.