A Neuro-Evolutionary Approach to Electrocardiographic Signal Classification

  • Antonia Azzini
  • Mauro Dragoni
  • Andrea G. B. Tettamanzi

Abstract

This chapter presents an evolutionary Artificial Neural Networks (ANN) classifier system as a heartbeat classification algorithm designed according to the rules of the PhysioNet/Computing in Cardiology Challenge 2011 (Moody, Comput Cardiol Challenge 38:273–276, 2011), whose aim is to develop an efficient algorithm able to run within a mobile phone that can provide useful feedback when acquiring a diagnostically useful 12-lead Electrocardiography (ECG) recording. The method used to solve this problem is a very powerful natural computing analysis tool, namely evolutionary neural networks, based on the joint evolution of the topology and the connection weights relying on a novel similarity-based crossover. The chapter focuses on discerning between usable and unusable electrocardiograms tele-medically acquired from mobile embedded devices. A preprocessing algorithm based on the Discrete Fourier Transform has been applied before the evolutionary approach in order to extract an 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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Antonia Azzini
    • 1
  • Mauro Dragoni
    • 2
  • Andrea G. B. Tettamanzi
    • 3
  1. 1.Dipartimento di Tecnologie dell’InformazioneUniversità degli Studi di MilanoCrema (CR)Italy
  2. 2.Fondazione Bruno Kessler (FBK-IRST)TrentoItaly
  3. 3.I3S Laboratory UMR 7271 - UNS/CNRS/INRIA, Ple GLC, WIMMICS Research TeamSophia Antipolis CEDEX, NiceFrance

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