Multistage approach for clustering and classification of ECG data.

Accurate and fast approaches for automatic ECG data classification are vital for clinical diagnosis of heart disease. To this end, we propose a novel multistage algorithm that combines various procedures for dimensionality reduction, consensus clustering of randomized samples and fast supervised classification algorithms for processing of the highly dimensional large ECG datasets. We carried out extensive experiments to study the effectiveness of the proposed multistage clustering and classification scheme using precision, recall and F-measure metrics. We evaluated the performance of numerous combinations of various methods for dimensionality reduction, consensus functions and classification algorithms incorporated in our multistage scheme. The results of the experiments demonstrate that the highest precision, recall and F-measure are achieved by the combination of the rank correlation coefficient for dimensionality reduction, HBGF consensus function and the SMO classifier with the polynomial kernel.