Artificial neural network based wave complex detection in electrocardiograms

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Abstract
This paper describes a Predictive Neural Network (PNN) based technique to detect QRS complexes of electrocardiograms (ECGs). The PNN is trained, using the back propagation algorithm, on non-QRS portions of the ECG to predict the signal one-step ahead. High prediction error is then taken as an indication of the occurrence of a QRS complex. A simple peak detection logic is then invoked to mark the exact location and magnitude of either a Q- or an R- or an S- peak within the QRS complex. The performance of the detector software is illustrated with examples representative of different QRS morphologies. The accuracy of QRS complex detection has been tested using bipolar standard limb leads of a standard ECG library; a sensitivity of 98·96% has been achieved. A brief discussion on how well this technique performs in comparison with the other QRS detectors is also presented.

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