By Topic

Synaptic delay based artificial neural networks and discrete time backpropagation applied to QRS complex detection

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Duro, R.J. ; Dipartimento Ingenieria Ind., Univ. de La Coruna, Ferrol, Spain ; Santos, J.

In this paper we make use of an extension of the backpropagation algorithm to discrete time feedforward networks that include internal time delays in the synapses. The structure of the network is similar to the one presented by Day-Davenport (1993), that is, in addition to the weights of the synaptic connections, we model their length through a parameter that indicates the delay a discrete event suffers when going from the origin neuron to the target neuron through a synaptic connection. Like the weights, these delays are also trainable, and a training algorithm can be obtained that is almost as simple as the backpropagation algorithm, and which is really an extension of it. We present an application of these networks to the task of identifying normal QRS and ventricular QRS complexes in an ECG signal with the network receiving the signal sequentially, that is, no windowing or segmentation is applied

Published in:

Neural Networks,1997., International Conference on  (Volume:4 )

Date of Conference:

9-12 Jun 1997