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A QRS detection algorithm has been dewloped based on a multiwiate model. It included three normalized measums -amplitude, first difference, and spatial jkquency. The terms are combined in a weighted sum to genemte an indicator variable that is then compared to a detection threshold. An FIR, band-pass filter, consisting of a cascaded series of running medians and means prefiltered the mw ECG signal to Educe high frequency noise as well as low frequency base line drift. nte technique appears to be exceedingly robust, correctly detecting even abermnt QRS complexes in noise-compted ECGs.

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We have developed a QRS detection algorithm based on a multivariate model, in which three independent, normalized measures --amplitude, first difference, and spatial frequency --are combined in a wighted s u m to generate an indicator variable that is then compared to a detection threshold. To increase sensitivity, we first applied an FIR, band-pass fdter consisting of a cascaded series of running medians and means. The technique appears to be exceedingly robust, correctly detecting even aberrant QRS complexes in noise-corrupted ECGs.