Clustering Improvement for Electrocardiographic Signals

  • Pau Micó
  • David Cuesta
  • Daniel Novák
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)

Abstract

Holter signals are ambulatory long-term electrocardiographic (ECG) registers used to detect heart diseases which are difficult to find in normal ECG. 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 ECG 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 ECG labels from MIT-BIH database, are developed too. At the end, some results-accuracy values among several clustering algorithms is presented.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Pau Micó
    • 1
  • David Cuesta
    • 1
  • Daniel Novák
    • 2
  1. 1.Department of Systems Informatics and ComputersPolytechnic School of AlcoiAlcoiSpain
  2. 2.Department of CyberneticsCzech Technical University in PragueCzech Republic

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