Feature reduction method using self organizing maps

Kutlu Y., Kuntalp D.

6th International Conference on Electrical and Electronics Engineering, ELECO 2009, Bursa, Turkey, 5 - 08 November 2009 identifier

  • Publication Type: Conference Paper / Full Text
  • City: Bursa
  • Country: Turkey
  • Keywords: Arrhythmia, Ecg, Feature reduction, Knn, Self organizing maps
  • Dokuz Eylül University Affiliated: Yes

Abstract

In this work, five main groups of arrhythmias in electrocardiograph (ECG) signals are tried to be classified using the features obtained from the output of a Self Organizing Map (SOM) network. The raw ECG signal consists of 81 sample points (60 point before and 20 point after the R peak point of the ECG). Consecutive sample values of a moving window (20 points of width) are used as the input vector of the SOM network. The output of the SOM network is used as the input vector to a classifier. Knearest neighbor (k-NN) algorithm is chosen as the classifier. The performance of the classifier is evaluated by the average values of sensitivity, specificity, selectivity and overall accuracy. As a result, 96%, 91%, 99%, and 97% sensitivity, selectivity, specificity, and overall accuracy values are obtained.