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 [7], 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 preprocessing 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.
A neuro-evolutionary approach to electrocardiographic signal classification / M. Dragoni, A. Azzini, A.G.B. Tettamanzi - In: Proceedings of the Italian workshop on artificial life and evolutionary computation, WIVACE 2012 : Parma, Campus Universitario, 20-21 febbraio 2012 / [a cura di] S. Cagnoni, M. Mirolli, M. Villan. - Parma : Università degli Studi di Parma, Dipartimento di Scienze Sociali, 2012. - ISBN 9788890358128. - pp. 1-11 (( convegno Italian Workshop on Artificial Life and Evolutionary Computation (WIVACE) tenutosi a Parma nel 2012.
A neuro-evolutionary approach to electrocardiographic signal classification
A. AzziniSecondo
;A.G.B. TettamanziUltimo
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 [7], 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 preprocessing 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.Pubblicazioni consigliate
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