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A Neuro-Evolutionary Approach to Electrocardiographic Signal Classification

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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.

Keywords

Mobile Phone Crossover Operator Scaled Conjugate Gradient Entire Evolutionary Process Heart State Evaluation 
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.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  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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