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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.

Notes: Electrogram Vector Timing and Correlation. Introduction: Discrimination of ventricular and supraventricular arrhythmias remains one of the major challenges for appropriate implantable defibrillator (ICD) therapy delivery. The electrogram vector timing and correlation (VTC) algorithm was developed for such rhythm discrimination. The VTC algorithm differentiates normally conducted supraventricular beats from abnormally conducted ventricular beats by comparing the timing and correlation of rate and shock channel electrograms. Methods and Results: Rate and shock channel electrograms of sinus rhythm and induced arrhythmias were collected from 93 patients during ICD placement. The algorithm was developed using data from 50 patients and prospectively tested in a software model with the remaining 43 patients. A sinus rhythm reference was formed by averaging complexes of the shock channel signal aligned by the peak amplitude of the rate channel. Eight features measuring the amplitude and timing of shock channel signal characteristics were extracted from the reference for comparison. When a high-rate rhythm was detected, the VTC algorithm computed the correlation of the arrhythmia complex features with the reference. Rhythms with a sufficient number of uncorrelated beats were classified as ventricular tachycardia (VT). In a dual-chamber implementation, the VTC algorithm is integrated with ventricular and atrial rate comparison (V 〉 A) and stability above an atrial fibrillation rate threshold. The test set consisted of 117 arrhythmias. Dual-chamber sensitivity was 100% (81/81 VT) and specificity was 97% (35/36 supraventricular tachycardia). Single-chamber analysis demonstrated 99% sensitivity and 97% specificity. Conclusion: The VTC algorithm demonstrated high sensitivity and specificity in discriminating between ventricular and supraventricular arrhythmias.

Notes: Atrial Defibrillation Thresholds. Introduction: Little investigation has been conducted to assess the atrial defibrillation thresholds of electrode configurations using electrodes designed for internal ventricular defibrillation (right ventricle [RV], superior vena cava [SVC], and pulse generator housing [Can]) combined with coronary sinus (CS) electrodes. We hypothesized that a CS → SVC+Can electrode configuration would have a lower atrial defibrillation threshold than a standard configuration for defibrillation, RV → SVC+Can. We also tested the atrial defibrillation thresholds of five other configurations. Methods and Results: In 12 closed chest sheep, we situated a two-coil (RV, SVC) defibrillation catheter, a left-pectoral subcutaneous Can, and a CS lead. Atrial fibrillation was burst induced and maintained with continuous infusion of intrapericardial acetyl-β-methylcholine chloride. Using fixed-tilt biphasic shocks, we determined the atrial defibrillation thresholds of seven test configurations in random order according to a multiple-reversal protocol. The peak voltage and delivered energy atrial defibrillation thresholds of CS → SVC+Can (168 ± 67 V, 2.68 ± 2.40 J) were significantly lower than those of RV → SVC+Can (215 ± 88 V, 4.46 ± 3.40 J). The atrial defibrillation thresholds of the other test configurations were RV+CS → SVC+Can 146 ± 59V, 1.92 ± 1.45 J; RV 
→ CS+SVC+Can:191 ± 89V, 3.53 ± 3.19 J; CS →
SVC: 188 ± 98V, 3.77 ± 4.14 J; SVC → CS+Can: 265 ±
145 V, 7.37 ± 9.12 J; and SVC → Can: 516 ± 209 V, 
24.5 ± 15.0 J. Conclusions: The atrial defibrillation threshold of CS → RV SVC+Can. In addition, the low atrial defibrillation threshold of RV+CS → SVC+Can merits further investigation. Based on corroboration of low atrial defibrillation thresholds of CS-based configurations in humans, physicians might consider using CS leads with atrioventricular defibrillators.