INTRODUCTION: Present @entity737 ( @entity737 ) have algorithms that discriminate @entity4710 ( @entity4710 ) from @entity1165 ( @entity1165 ). One type of algorithm is based on differences in morphology of ventricular electrograms during @entity1165 and @entity4710 . Prior @entity4710 algorithms have not undergone real-time evaluation in ambulatory @entity1 until they were incorporated permanently into @entity737 . This approach may result in incomplete testing of electrogram morphology algorithms because they are influenced by posture, activity, and electrogram maturation. We downloaded software into implanted @entity737 to study a novel algorithm that compares morphologies of baseline and @entity591 electrograms based on differences between corresponding coefficients of their wavelet transforms. This comparison is expressed as a match-percent score. METHODS AND RESULTS: In 23 @entity1 , we downloaded the wavelet algorithm into implanted @entity737 to assess the temporal and postural stability of baseline electrograms as measured by this algorithm and its accuracy for @entity4710 . Median follow-up was 6 months. Software was downloaded into all @entity737 without altering other device functions. With few exceptions, percent template match in baseline rhythm was stable with changes in body position, rest versus walking, isometric exercise, and over time (1 and 3 months). Using the nominal match-percent threshold of 70%, sensitivity for detection of 38 VTs was 100%. Specificity for rejection of 65 SVTs was 78%. SVTs were rejected for a total of 2.7 hours. Inappropriate detections of @entity4710 as @entity1165 were caused by electrogram truncation, myopotential interference with low-amplitude electrograms, waveform alignment error, and rate-dependent aberrancy. The first three accounted for 69% of inappropriate detections and could have been prevented by optimal programming. The optimal match-percent threshold was 60% to 70% based on a receiver-operator characteristic curve. After shocks, the median time for baseline electrogram morphology to normalize was 85 seconds. CONCLUSION: The wavelet morphology algorithm has high sensitivity for @entity1165 detection. Inappropriate detections of @entity4710 as @entity1165 may be reduced by optimal programming. Downloadable software permits evaluation of new algorithms in implanted @entity737 .

Discrimination of XXXX from @entity4710 by a downloaded wavelet-transform morphology algorithm: a paradigm for development of implantable cardioverter defibrillator detection algorithms.

@entity1 :: ('9606', 'Species') :: ['patients'] @entity1165 :: ('MESH:D017180', 'Disease') :: ['VT', 'ventricular tachycardia'] @entity737 :: ('MESH:D057873', 'Disease') :: ['ICDs', 'implantable cardioverter defibrillators'] @entity591 :: ('MESH:D013610', 'Disease') :: ['tachycardia'] @entity4710 :: ('MESH:D013617', 'Disease') :: ['supraventricular tachycardia', 'SVT', 'SVT-VT discrimination']
@entity1165 :: (MESH:D017180,Disease) :: ['ventricular tachycardia']
