A Neuro-Evolutionary Approach to Electrocardiographic Signal Classification

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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Chapitre d'ouvrage
Evolution, Complexity and Artificial Life, Springer, pp.193-207, 2014, 978-3-642-37576-7. <10.1007/978-3-642-37577-4_13>
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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>

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