Premature ventricular contraction detection for long-term monitoring in an implantable cardiac monitor.

Background: Premature ventricular complexes (PVCs) are an important therapeutic target in symptomatic patients and in the setting of PVC-induced cardiomyopathy; however, measuring burden and therapeutic response is challenging. We developed and validated an algorithm for continuous long-term monitoring of PVC burden in an insertable cardiac monitor (ICM).

Methods: A high-specificity PVC detection algorithm was developed using real-world ICM data and validated using simultaneous Holter data and real-world ICM data.

Adapting detection sensitivity based on evidence of irregular sinus arrhythmia to improve atrial fibrillation detection in insertable cardiac monitors.

Aims: Intermittent change in p-wave discernibility during periods of ectopy and sinus arrhythmia is a cause of inappropriate atrial fibrillation (AF) detection in insertable cardiac monitors (ICM). To address this, we developed and validated an enhanced AF detection algorithm.

Methods And Results: Atrial fibrillation detection in Reveal LINQ ICM uses patterns of incoherence in RR intervals and absence of P-wave evidence over a 2-min period.

Development and validation of a dual sensing scheme to improve accuracy of bradycardia and pause detection in an insertable cardiac monitor.

Background: Undersensing of premature ventricular beats and low-amplitude R waves are primary causes for inappropriate bradycardia and pause detections in insertable cardiac monitors (ICMs).

Objective: The purpose of this study was to develop and validate an enhanced algorithm to reduce inappropriately detected bradycardia and pause episodes.

Methods: Independent data sets to develop and validate the enhanced algorithm were derived from a database of ICM-detected bradycardia and pause episodes in de-identified patients monitored for unexplained syncope.