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, 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.

Electrocardiographic signal classification with evolutionary artificial neural networks / A. Azzini, M. Dragoni, A.G.B. Tettamanzi - In: Applications of evolutionary computation: EvoApplications 2012 : Málaga, Spain, april 11-13, 2012 : proceedings / [a cura di] C. Di Chio ... [et al.]. - Berlin : Springer, 2012. - ISBN 9783642291777. - pp. 295-304 (( convegno EvoApplications tenutosi a Málaga nel 2012.

Electrocardiographic signal classification with evolutionary artificial neural networks

A. Azzini
Primo
;
A.G.B. Tettamanzi
Ultimo
2012

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, 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.
Signal processing ; Heartbeat classification ; Evolutionary algorithms ; Neural networks
Settore INF/01 - Informatica
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/178287
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