Preclustering of Electrocardiographic Signals Using Left-to-Right Hidden Markov Models

  • Pau Micó
  • David Cuesta
  • Daniel Novák
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)


Holter signals are ambulatory long-term electrocardiographic (ECG) registers used to detect heart diseases which are difficult to find in normal ECGs. 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 synus rhythm. The later analisys of these arrhythmia beats yields a diagnostic from the pacient’s heart condition. The Hidden Markov Models (HMM) can be used in ECG diagnosis avoiding the manual inspection. In this paper we improve the performance of the HMM clustering method introducing a preclustering stage in order to diminish the number of elements to be finally processed and reducing the global computational cost. An experimental comparative study is carried out, utilizing records form the MIT-BIH Arrhythmia database. Finally some results are presented in order to validate the procedure.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

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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