Electrocardiographic Signal Classification with Evolutionary Artificial Neural Networks

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 [14], 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.