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

Abstract

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. The PVC algorithm uses long-short-long RR interval sequence and morphology characteristics for three consecutive beats to detect the occurrence of single PVC beats. Data are expressed as gross incidence, patient average, and generalized estimating equation estimates, which were used to determine sensitivity, specificity, positive and negative predictive value (PPV, NPV). Results:The PVC detection algorithm was developed on eighty-seven 2-min EGM strips recorded by an ICM to obtain a sensitivity and specificity of 75.9% and 98.8%. The ICM validation data cohort consisted of 787 ICM recorded ECG strips 7-16 min in duration from 134 patients, in which the algorithm detected PVC beats with a sensitivity, specificity, PPV, and NPV of 75.2%, 99.6%, 75.9%, and 99.5%, respectively. In the Holter validation dataset with continuous 2-h snippets from 20 patients, the algorithm sensitivity, specificity, PPV, and NPV were 74.4%, 99.6%, 68.8%, and 99.7%, respectively, for detecting PVC beats.Conclusions: The PVC detection algorithm was able to achieve a high specificity with only 0.4% of the normal events being incorrectly identified as PVCs, while detecting around three of four PVCs on a continuous long-term basis in ICMs. K E Y W O R D Sinsertable cardiac monitor, premature ventricular contraction, PVC burden, PVC-induced cardiomyopathy

The missing link: Unlocking the power of cardiac rhythm monitoring device based QT interval detection

“…The R‐waves are sensed using a dual channel sensing scheme. A secondary sensing channel was used in addition to the primary sensing channel to reduce under sensing of ventricular beats such as premature ventricular complexes (PVCs) 8 . To distinctly identify the T‐wave signal, the electrogram signal is filtered, using a pass band filter of 6 to 20 Hz to enhance T‐waves, and then rectified.…”

“…A secondary sensing channel was used in addition to the primary sensing channel to reduce under sensing of ventricular beats such as premature ventricular complexes (PVCs). 8 To distinctly identify the T‐wave signal, the electrogram signal is filtered, using a pass band filter of 6 to 20 Hz to enhance T‐waves, and then rectified. To detect the T‐wave location, the algorithm calculates a T‐wave search window following the QRS complex.…”

“…A secondary sensing channel was used in addition to the primary sensing channel to reduce under sensing of ventricular beats such as premature ventricular complexes (PVCs). 8 To distinctly identify the T-wave signal, the electrogram signal is filtered, using a pass band filter of 6 to 20 Hz to las were used for calculation of the corrected QT interval (QTc). 9,10 The QTc intervals is computed using the Framingham's formula 11,12 as:…”

Deep-Learning Premature Contraction Localization in 12-lead ECG From Whole Signal Annotations

“…In the last decade, multiple approaches were applied on PVC detection in ECG signal. One of the most intuitive PVC detectors are those based on the decision rules, where the rhythm and morphological features are calculated from the beats and PVCs are identified by the feature assessment [1]. Another simple method is a template matching, where a set of appropriate beat's templates is required.…”