Classification of branch block beats using higher order spectral analysis and neural networks

Torun M. U., Isler Y., GÜRKAN KUNTALP D., KUNTALP M.

IEEE 14th Signal Processing and Communications Applications, Antalya, Turkey, 16 - 19 April 2006, pp.437-438 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/siu.2006.1659736
  • City: Antalya
  • Country: Turkey
  • Page Numbers: pp.437-438
  • Dokuz Eylül University Affiliated: Yes

Abstract

In this study, it is aimed to classify branch block beats. A total number of 6170 beats related to 3 types of classes are extracted from MIT/BIH arrhythmia database and their bispectrums are calculated using TOR method. The area defined by the frequency values where the value of the energy of bispectrum is 95% of the maximum value in both axes is calculated. This area information is used as a one dimensional feature vector to feed the neural network designed as a classifier. The overall performance of the system is calculated as 94.2%. This study shows that higher order spectral analysis is a promising tool for arrhythmia beat classification.