Clustering improvement for electrocardiographic signals

Abstract

Holter signals are ambulatory long-term electrocardiographic (ECG) registers used to detect heart diseases which are difficult to find in normal EGG. These signals normally include several channels and its duration is up to 48 hours. The principal problem for the cardiologists consists of the manual inspection of the whole Holter EGG to find all those beats whose morphology differ from the normal cardiac rhythm. The later analysis of these abnormal beats yields a diagnostic from the pacient's heart condition. In this paper we compare the performance among several clustering methods applied over the beats processed by Principal Component Analysis (PCA). Moreover, an outlier removing stage is added, and a cluster estimation method is included. Quality measurements, based on EGG labels from MIT-BIH database, are developed too. At the end, some results-accuracy values among several clustering algorithms is presented. © Springer-Verlag Berlin Heidelberg 2005.