Segment Clustering for Holter Recordings Analysis

  • J. L. Rodríguez-SoteloEmail author
  • 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)


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.


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.



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


  1. 1.
    Cuesta, D., Biagetti, M., Quinteiro, R., Mico-Tormos, P., Aboy, M.: Unsupervised classification of ventricular extrasystoles using bounded clustering algorithms and morphology matching. Med. Biol. Eng. Comput. 45(3), 229–239 (2007)CrossRefGoogle Scholar
  2. 2.
    De-Chazal, P., O’Dwyer, M., Reilly, R.: Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 51(7), 1196–1206 (2004)CrossRefGoogle Scholar
  3. 3.
    Jain, A., Murty, M., Flynn, P.: Data clustering: a review. ACM Comput. Surv. 31(3), 265–323 (1999)CrossRefGoogle Scholar
  4. 4.
    Rodríguez-Sotelo, J.L., Delgado-Trejos, E., Peluffo-Ordoñez, D., Cuesta-Frau, D., Castellanos-Domínguez, G.: Weighted-PCA for unsupervised classification of cardiac arrhythmias. In: Conference proceedings of the IEEE Engineering in Medicine and Biology Society, pp. 1906–1909 (2010).
  5. 5.
    Lagerholm, M., Peterson, C., Braccini, G., Edenbrandt, L., Sörnmo, L.: Clustering ECG complexes using hermite functions and self-organising maps. IEEE Trans. Biomed. 48, 838–847 (2000)CrossRefGoogle Scholar
  6. 6.
    Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Advances in Neural Information Processing Systems 14, pp. 849–856. MIT Press (2001)Google Scholar
  7. 7.
    Rodríguez-Sotelo, J.L., Peluffo, D., Frau, D.C., Ordónez, D.P., Domínguez, G.C.: Non-parametric density-based clustering for cardiac arrhythmia analysis. In: Computers in Cardiology, CINC (2009)Google Scholar
  8. 8.
    Sotelo, J.L.R., Peluffo, D., Frau, D.C., Ordónez, D.P., Domínguez, G.C.: Non-parametric density-based clustering for cardiac arrhythmia analysis. In: Computers in cardiology, CINC (2009)Google Scholar
  9. 9.
    Yu, S.X., Shi, J.: Multiclass spectral clustering. In: Proceedings of the Ninth IEEE International Conference on Computer Vision ICCV 2003, p. 313. IEEE Computer Society, Washington, DC (2003)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • J. L. Rodríguez-Sotelo
    • 1
    Email author
  • 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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