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Notes: This study investigated the effectiveness of correlation waveform analysis for identifying different ventricular electrogram morphologies of multiple VTs in the same patient. Patients with implantable antitachycardia devices are commonly subject to the occurrence of more than one distinct monomorphic VT. Each of these VTs may have unique therapeutic alternatives for termination. VTs with identical and different monomorphic configurations were recorded (1–500 Hz) using distal bipolar (1 cm) and distal unipolar electrograms from the right ventricular apex. Thirty-six distinct monomorphic VTs induced in 15 patients were analyzed. Nine VTs with identical morphologies (12/12 surface ECGs) were induced twice and used as a control. A template was created for each VT induced. Correlation waveform analysis was used to compare eacb depolarization of all other VTs induced subsequently in tbe same patient. The mean correlation coefficient (pμ) of cycle-by-cycle analysis was used as a discriminant function: pμ≥ 0.95 was considered matched; and pμ 〈 0.95 was considered distinct. From the control population, VTs were successfully classified as identical in 9 of 9 cases (100%) using both bipolar and unipolar electrograms. VTs with different monomorphic configurations were successfully classified as being different in 31 of 33 cases (94%) using bipolar electrogram analysis and in 29 of 33 cases (88%) using the unipolar. Template matcbing is effective for detecting: (1) the recurrence of VTs, which are identical; and (2) the occurrence of a VT with a different configuration. This method appears effective using either unipolar or bipolar intracardiac waveforms.

Notes: Inappropriate electrical therapy and power efficiency play a major role in algorithm implementation for antitachycardia devices (ATD) that capture, store, and analyze the patient electrogram as an adjunct to rate determination. Morphologically based algorithms have been demonstrated to improve specificity, thereby decreasing occurrences of inappropriate electrical therapy. However, morphologically based algorithms are power demanding. Optimization of power efficiency can be achieved by eliminating unnecessary algorithmic computation, but must not compromise the effectiveness of algorithms, which perform direct analysis on raw signals. Significant reductions can be achieved by reduced sampling rates, which allow for increased overall ATD efficiency via concomitant decreases in computation and data storage. This investigation determined the upper and lower bounds for filter cutoff frequency beyond which detection precision by an established morphometric method for arrhythmia classification, correlation waveform analysis (CWA), was unfavorable. Four measurement statistics were used. In ten patients with inducible VT and VF, all bipolar intraventricular electrograms were classified correctly with a minimum passband of 10–50 Hz using any of the four measurement statistics. There was ± 80% correct classification using all four measurement statistics with passbands having low frequency cutoffs ± 15 Hz and high frequency cutoffs ± 50 Hz. Correct classification of ± 90% of unipolar electrograms during NSR, VT, and VF occurred using all four measurement statistics with a passband of 1–50 Hz. There was ± 80% correct classification with passbands 1, 10, 15, or 20–500 Hz and 10–50 Hz. The classification of NSR, VT, and VF was most accurate on an intrapatient basis. Accuracy decreased using an interpatient rhythm classification. Optimum filter settings of 1–50 Hz and 10–50 Hz were determined for unipolar and bipolar electrograms, respectively. Sampling data at 120 Hz was found to be sufficient. Bipolar electrode configuration statistically outperfomed unipolar data. In conclusion, morphometric analysis of bipolar and unipolar intraventricular electrograms appears to be achievable using band limited data and reduced sampling rates.