QRS detection using morphological and rhythm information

An approach has been developed using artificial neural networks to detect QRS complexes within an ambulatory ECG signal. The method employs the use of an artificial neural network classifier to recognise the morphology of a QRS complex based on amplitude and derivative features. The feature vectors are derived from a representative annotated ECG trace and are used in the formulation of the ANN´s training set. The outputs, or p.d.f. estimates generated by the neural network are then used to determined if a “QRS-like spike” has occurred. These spike detections then undergo further post-processing which, biases these detections such that the spike detection “nearest” the anticipated location of the next QRS is confirmed as a QRS complex. This anticipation of the QRS complex location is based on the estimation of the next RR interval using past RR intervals of previously confirmed QRS complexes. Such post-processing has the effect of greatly reducing the number of false positive detections, particularly in noisy ECG traces