Sensitive and precise detection of ventricular oversensing using deep learning
A Thiyagarajah, M Strik, L Van Krimpen, J Duchateau, B Sacristan, R Dubois, S Ploux, P BordacharAbstract
Background
Ventricular oversensing in patients with implantable cardioverter defibrillators and permanent pacemakers can cause inappropriate shocks or inhibition of pacing and non-physiological oversensing is usually the first manifestation of ventricular lead dysfunction. Ventricular oversensing is often incorrectly logged as non-sustained ventricular tachycardia (NSVT) by cardiac devices, but identifying these episodes is time consuming since some episodes are subtle and the overall prevalence of oversensing is low.
Purpose
To develop a clinically meaningful deep learning model with a high sensitivity for detecting ventricular oversensing amongst NSVT episodes.
Methods
NSVT remote monitoring transmissions from three French hospitals were used for training and internal validation and data from a fourth hospital served as an external validation cohort. After pretraining on atrial high rate events and synthetic NSVT episodes engineered from periodic electrograms, a deep ensemble classifier was trained to differentiate physiological or non-physiological oversensing from true NSVT.
Results
The external validation cohort comprised 2856 NSVT episodes (2732 true NSVT (95.6%) and 124 oversensing (4.4%)) with diverse oversensing episodes including non-physiological signals due to lead dysfunction, P wave, T wave and diaphragmatic oversensing (Figure). The deep ensemble classifier correctly identified all 124 oversensing episodes (100%) with only 8 false positive results (0.3%). This yielded sensitivity 100%, specificity 99.7%, positive predictive value 93.9% and F2 score 98.1 for detection of ventricular oversensing.
Conclusion
A deep learning classifier can reliably differentiate physiological and non-physiological oversensing from true NSVT with very high sensitivity and precision. This can improve early detection of lead dysfunction, prevent inappropriate therapies and reduce the workload of remote monitoring staff.