A Neuro-Evolutionary Approach to Electrocardiographic Signal Classification

Antonia Azzini 1 Mauro Dragoni 2 Andrea G. B. Tettamanzi 3
3 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
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.
Document type :
Book sections
Liste complète des métadonnées

Cited literature [36 references]  Display  Hide  Download

Contributor : Andrea G. B. Tettamanzi <>
Submitted on : Thursday, April 24, 2014 - 9:10:37 PM
Last modification on : Monday, November 5, 2018 - 3:52:09 PM
Document(s) archivé(s) le : Thursday, July 24, 2014 - 11:56:06 AM


Files produced by the author(s)




Antonia Azzini, Mauro Dragoni, Andrea G. B. Tettamanzi. A Neuro-Evolutionary Approach to Electrocardiographic Signal Classification. Evolution, Complexity and Artificial Life, Springer, pp.193-207, 2014, 978-3-642-37576-7. ⟨10.1007/978-3-642-37577-4_13⟩. ⟨hal-00983194⟩



Record views


Files downloads