Title :
Feasibility of neural network based QRS-T cancellation schemes for P-wave detection
Author :
Vásquez, C. ; Hernandez, Alfredo I. ; Carrault, G. ; Mora, F.A. ; Passariello, G.
Author_Institution :
Grupo de Bioingenieria y Biofisica Aplicada, Univ. Simon Bolivar, Caracas, Venezuela
Abstract :
This work reviews the possible application of artificial neural networks (ANNs) to the problem of ventricular activity (VA) cancellation. The system proposed consists of estimating a non-linear time-varying transfer function between two ECG channels using a Time Delay Neural Network (TDNN). Three different TDNN topologies (purely feed-forward, simple recurrent, and fully recurrent) were implemented and tested using record 108 of the MIT-BIH DB. In order to evaluate these networks, two performance measures were introduced in this work, namely VA energy change and Signal to Noise Ratio (SNR) improvement. Preliminary results using VA energy change and SNR improvement indicators showed that the simple recurrent topology presents the best performance
Keywords :
electrocardiography; feedforward neural nets; medical signal detection; medical signal processing; recurrent neural nets; ECG channels; P-wave detection; artificial neural networks; electrodiagnostics; fully recurrent neural nets; neural network based QRS-T cancellation schemes; nonlinear time-varying transfer function; purely feedforward neural nets; simple recurrent neural nets; time delay neural network; Artificial neural networks; Delay effects; Delay estimation; Electrocardiography; Feedforward systems; Network topology; Neural networks; Signal to noise ratio; Time varying systems; Transfer functions;
Conference_Titel :
Computers in Cardiology 1998
Conference_Location :
Cleveland, OH
Print_ISBN :
0-7803-5200-9
DOI :
10.1109/CIC.1998.731951
