Artificial neural network for ECG arrhythmia monitoring

The application of a multilayer perceptron artificial neural network model to detect the QRS complex in the electrocardiographic (ECG) signal processing is presented. The objective is to improve the heart beat detection rate under the presence of severe background noise. An adaptively tuned multi-layer perceptron structure is used to model the nonlinear, time varying background noise. The noise is removed by subtracting the predicted noise from the original signal. Preliminary experiment results indicate that the artificial neural network (ANN)-based approach consistently out-performs the conventional bandpass filtering approach and the linear adaptive filtering approach. Such performance enhancement is critical in the development of a practical automated online ECG arrhythmia monitoring system