The invention provides an automatic shockable
rhythm identification and classification method combined with electrocardio time-
frequency domain feature analysis. The method comprises specific steps as follows: S1, pretreating electrocardio signals; S2, automatically identifying cardiac arrest rhythms, and if discrimination conditions are not met, implementing S3; S3, on the basis of an
integral coefficient band-pass filter, calculating the
maximum amplitude proportion value (Pa), the average amplitude proportion value (Pb) and the average deviation proportion value (Pc) of output signals; S4, S5, S6 and S7, discriminating shockable rhythms and non-shockable rhythms according to
frequency domain feature values such as the Pa, the Pb, the Pc and the like, and implementing S8 in case of failure; S8, calculating an electrocardio standard grid bar projection standard deviation; S9, discriminating the shockable rhythms and the non-shockable rhythms according to the standard deviation. The method can be applied to instruments and equipment which automatically identify and classify the shockable rhythms according to
body surface electrocardiograms, the shockable
rhythm identification sensitivity and the non-shockable
rhythm specificity are improved, and the
algorithm operating efficiency is improved.