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Segment Clustering for Holter Recordings Analysis

  • J. L. Rodríguez-Sotelo
  • D. H. Peluffo-Ordoñez
  • D. López-Londoño
  • A. Castro-Ospina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10337)

Abstract

In this work, an efficient non-supervised algorithm for clustering of ECG signals is presented. The method is assessed over a set of records from MIT/BIH arrhythmia database with different types of heartbeats, including normal (N) heartbeats, as well as the arrhythmia heartbeats recommended by the AAMI, usually found in Holter recordings: ventricular extra systoles (VE), left and right branch bundles blocks (LBBB and RBBB) and atrial premature beats (APB). The results are assessed by means the sensitivity and specificity measures, taking advantage of the database labels. Also, unsupervised performance measures are used. Finally, the performance of the algorithm is in average 95%, improving results reported by previous works of the literature.

Keywords

Heart Rate Variability Minority Class Initial Partition Holter Recording Ideal Partition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work is part of project number 249-028, funded by the Universidad Autónoma de Manizales.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • J. L. Rodríguez-Sotelo
    • 1
  • D. H. Peluffo-Ordoñez
    • 2
  • D. López-Londoño
    • 1
  • A. Castro-Ospina
    • 3
  1. 1.Universidad Autónoma de ManizalesManizalesColombia
  2. 2.Universidad Técnica del NorteIbarraEcuador
  3. 3.Instituto Tecnológico MetropolitanoMedellínColombia

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