Electrocardiographic Signal Classification with Evolutionary Artificial Neural Networks

  • Antonia Azzini
  • Mauro Dragoni
  • Andrea G. B. Tettamanzi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7248)

Abstract

This work presents an evolutionary ANN classifier system as an heart beat classification algorithm suitable for implementation on the PhysioNet/Computing in Cardiology Challenge 2011 [14], whose aim is to develop an efficient algorithm able to run within a mobile phone, that can provide useful feedback in the process of acquiring a diagnostically useful 12-lead Electrocardiography (ECG) recording.

The method used in such a problem is to apply a very powerful natural computing analysis tool, namely evolutionary neural networks, based on the joint evolution of the topology and the connection weights together with a novel similarity-based crossover.

The work focuses on discerning between usable and unusable electrocardiograms tele-medically acquired from mobile embedded devices. A prepropcessing algorithm based on the Discrete Fourier Trasform has been applied before the evolutionary approach in order to extract the ECG feature dataset in the frequency domain. Finally, a series of tests has been carried out in order to evaluate the performance and the accuracy of the classifier system for such a challenge.

Keywords

Signal Processing Heartbeat Classification Evolutionary Algorithms Neural Networks 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Antonia Azzini
    • 1
  • Mauro Dragoni
    • 2
  • Andrea G. B. Tettamanzi
    • 1
  1. 1.Dipartimento di Tecnologie dell’InformazioneUniversità degli Studi di MilanoCremaItaly
  2. 2.Fondazione Bruno Kessler (FBK-IRST)Povo (Trento)Italy

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