Clustering and visualization of ECG signals

Abstract

Holter electrocardiographic (ECG) signals are ambulatory long-term registers used to detect heart diseases. These signals normally include more, than one channels and its duration is up to 24 hours. The principal problem of the cardiologists is the manual inspection of the whole Holter ECG to find all those beats which morphology differs from the normal beats. In this paper we present our method. We apply firstly a grid clustering technique. Secondly we use a special density-based clustering algorithm, named Optics. Then we visualize every heart beat in the record, heartbeats in a cluster, furthermore we represent every cluster with median of heartbeats. We can perform manual and further automatic clustering. With this method, the result is easy ECG analysis and optimization of time of processing.