No abstract

This study demonstrated that the LVCM algorithm is safe, accurate, and highly reliable. LVCM worked with different types of leads and different lead locations. LVCM was demonstrated to be clinically equivalent to the manual LV threshold test. LVCM offers automatic measurement, output adaptation, and trends of the LV threshold and should result in improved ability to maintain LV capture without sacrificing device longevity.

The Search AV+ algorithm in the EnPulse pacemaker effectively promotes intrinsic ventricular activation and substantially reduces unnecessary ventricular pacing.

This study was undertaken to develop and test a morphology-based adaptive algorithm for real-time detection of P waves and far-field R waves (FFRWs) in pacemaker patient atrial electrograms. Cardiac event discrimination in right atrial electrograms has been a problem resulting in improper atrial sensing in implantable devices; potentially requiring clinical evaluation and device reprogramming. A morphology-based adaptive algorithm was first evaluated with electrograms recorded from 25 dual chamber pacemaker implant patients. A digital signal processing (DSP) system was designed to implement the algorithm and test real-time detection. In the second phase, the DSP implementation was evaluated in 13 patients. Atrial and ventricular electrograms were processed in real-time following algorithm training performed in the first few seconds for each patient. Electrograms were later manually annotated for comparative analysis. The sensitivity for FFRW detection in the atrial electrogram during off-line analysis was 92.5% (+/- 10.9) and the positive predictive value was 99.1% (+/- 1.8). Real-time P wave detection using a DSP system had a sensitivity of 98.9% (+/- 1.3) and a positive predictivity of 97.3% (+/- 3.5). FFRW detection had a sensitivity of 91.0% (+/- 12.4) and a positive predictivity of 97.1% (+/- 4.2) in atrial electrograms. DSP algorithm tested can accurately detect both P waves and FFRWs in right atrium real-time. Advanced signal processing techniques can be applied to arrhythmia detection and may eventually improve detection, reduce clinician interventions, and improve unipolar and bipolar lead sensing.