A pilot study examining the performance of polynomial-modeled ventricular shock electrograms for rhythm discrimination in implantable devices.
BACKGROUND
Inappropriate shocks continue to be a problem for patients with implantable defibrillators (ICD). We evaluated the performance of polynomial-modeled ventricular electrograms (EGM) to discriminate between supraventricular tachycardia (SVT) and ventricular tachycardia (VT).


METHODS
Seven sets of EGM from patients having both SVT and VT documented during a single ICD interrogation were included. The cardiac cycle was analyzed off-line in two parts, QR and RQ segments, which were modeled separately using third-order and sixth-order polynomial equations, respectively. These segments were then analyzed to determine which polynomial coefficients were most significant for rhythm discrimination.


RESULTS
When analyzing the QR segment during arrhythmia, there were statistically significant (P<0.05) correlations in 4 of 4 (100%) of the QR coefficients when comparing normal sinus rhythm (NSR) to SVT and 2 of 4 (50%) when comparing NSR to VT or SVT to VT. When analyzing the RQ segment during arrhythmia, there were statistically significant (P<0.05) correlations in 4 of 7 (57%) of the RQ coefficients when comparing NSR to SVT, 5 of 7 (71%) when comparing NSR to VT, and 3 of 7 (43%) when comparing SVT to VT. Using a cutoff value of 50% change from NSR, the ratio of first-order to zero-order QR coefficient was able to completely separate VT from SVT (P=0.03) in this series of patients.


CONCLUSIONS
Our data demonstrate the feasibility of simple polynomial equations that reproduce the depolarization and repolarization phases of human ventricular shock EGM. The ratio of first-order to zero-order QR coefficient was able to reliably discriminate between SVT and VT while reducing the polynomial model to a first-order system. The results of this pilot trial may serve as the basis for a larger prospective trial implementing a discrimination algorithm for use in low computational power implantable devices.
Williams
Jeffrey L
JL
Division of Cardiac Electrophysiology, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA. williamsjl4@upmc.edu
Shusterman
Vladimir
V
Saba
Samir
S
D000465:Algorithms; D017147:Defibrillators, Implantable; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D003937:Diagnosis, Differential; D016002:Discriminant Analysis; D004554:Electric Countershock / Q000295:instrumentation / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010507:Periodicity; D010865:Pilot Projects; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013617:Tachycardia, Supraventricular / Q000175:diagnosis* / Q000517:prevention & control*; D017180:Tachycardia, Ventricular / Q000175:diagnosis* / Q000517:prevention & control*; D013813:Therapy, Computer-Assisted / Q000379:methods*; D016896:Treatment Outcome