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

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.