Band‐Limited Morphometric Analysis of the Intracardiac Signal: Implications for Antitachycardia Devices

Abstract

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 cut-off 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 > or = 80% correct classification using all four measurement statistics with passbands having low frequency cutoffs < or = 15 Hz and high frequency cutoffs > or = 50 Hz. Correct classification of > or = 90% of unipolar electrograms during NSR, VT, and VF occurred using all four measurement statistics with a passband of 1-50 Hz. There was > or = 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 outperformed unipolar data. In conclusion, morphometric analysis of bipolar and unipolar intraventricular electrograms appears to be achievable using band limited data and reduced sampling rates.

ICD Arrhythmia Detection and Discrimination: Are We There Yet?

“…Morphology algorithms were not applied in early ICDs because the required calculations exceeded the capability of the devices' microprocessors. As device technology has improved, morphology algorithms for rhythm discrimination have been implemented by most device manufacturers in both single‐ and dual‐chamber ICDs 18–22 …”

“…As device technology has improved, morphology algorithms for rhythm discrimination have been implemented by most device manufacturers in both single-and dual-chamber ICDs. [18][19][20][21][22] Dual-chamber detection algorithms improve tachycardia discrimination by analyzing the relation between atrial and ventricular activity. Aside from making sense intuitively, dual-chamber detection has been reported to be superior to single-chamber detection by some, but not all, recent clinical trials.…”

Clinical Evaluation of Morphology Discrimination: An Algorithm for Rhythm Discrimination in Cardioverter Defibrillators

“…Consequently, while T waves are filtered, avoiding oversensing and consequent mismatch with the template, depolarization characteristics are completely preserved. In addition, Morris et al 17 stated that different types of morphometric analyses of bipolar intraventricular electrograms appear to be achievable using bandlimited data and reduced sampling rates that usually occur with implantable devices. In regard to signal processing performed by the ICD, signal filtering does not particularly affect R wave morphology as the frequency band of short-gap bipole (D , 1cm) and integrated sensing systems, which is typically found in the range 10-50 Hz, 21,22 is accommodated by the band-pass of the device.…”

“…The theoretical underpinnings of rhythm discrimination based on bipolar electrograms has been described. [14][15][16][17] The aim of the present study was to evaluate the diagnostic accuracy of the new morphology discrimination algorithm for enhanced rhythm discrimination. This algorithm was tested on induced atrial fibrillation and atrial and ventricular paced rhythms (simulating supraventricular and ventricular tachyarrhythmias) during acute invasive testing performed at predischarge, and upon spontaneous supraventricular and ventricular tachyarrhythmias occurring during follow-up.…”

Impact of Pacing and High-Pass Filter Settings on Ventricular Bipolar Electrograms in Implantable Cardioverter Defibrillator Systems - Implication of Predictors for Inappropriate Therapy Caused by Oversensing of Repolarization Electrograms -

“…25, 26 Tuzcu et al reported the efficacy of the changeable high-pass filter function as one of the methods to avoid sensing failure. 27 In this study, we demonstrated the excellent predictors for T wave oversensing using the existence of Brugada syndrome and the T/R ratio during AAI pacing by the Biotronik ICDs.…”