Why the study?
Does an adaptive, linear-predictive filter (ALPF) improve the identification of cardiac arrhythmias compared to correlation waveform analysis (CWA)?
Does an adaptive, linear-predictive filter (ALPF) improve the identification of cardiac arrhythmias compared to correlation waveform analysis (CWA)?
The adaptive, linear-predictive filter algorithm demonstrates comparable accuracy to correlation waveform analysis for identifying ventricular and sinus tachycardia, but appears superior for detecting ventricular fibrillation.
ALPF may enhance VF detection over CWA; leaves open its clinical utility pending larger validation studies.
In order to detect an abrupt change in morphology of intracardiac electrograms, the authors have developed a supplemental algorithm to rate criteria which utilizes the mean-squared error of an adaptive, linear-predictive filter (ALPF). Further analysis of the filter error is invoked when such a change is detected to identify the rhythm. ALPF successfully identified 10/11 (91%) cases of ventricular tachycardia (VT), 6/8 (75%) cases of ventricular fibrillation (VF), and 4/4 (100%) cases of sinus tachycardia (ST). As a basis of comparative analysis, correlation waveform analysis (CWA) was used to identify the same test set of arrhythmias. CWA correctly identified 11/11 (100%) cases of VT, 4/8 (50%) cases of VF, and 4/4 (100%) cases of ST. When compared to CWA, ALPF has similar accuracy in differentiating VT and ST. ALPF appears to be superior to CWA in identifying VF.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
No takes yet. Share an insight, caveat, or question.
Finelli et al. (2002) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: