Novel algorithms improve arrhythmia detection accuracy in insertable cardiac monitors

Abstract Introduction Insertable cardiac monitors (ICMs) are commonly used to diagnose cardiac arrhythmias. False detections in the latest ICM systems remain an issue, primarily due to inaccurate R‐wave sensing. New discrimination algorithms were developed and tested to reduce false detections of atrial fibrillation (AF), pause, and tachycardia episodes in ICMs. Methods Stored electrograms (EGMs) of AF, pause, and tachycardia episodes detected by Abbott Confirm Rx™ ICMs were extracted from the Merlin.net™ Patient Care Network, and manually adjudicated to establish independent training and testing datasets. New discrimination algorithms were developed to reject false episodes due to inaccurate R‐wave sensing, P‐wave identification, and R–R interval patterns. The performance of these new algorithms was quantified by false positive reduction (FPR) and true positive maintenance (TPM), relative to the existing algorithms. Results The new AF detection algorithm was trained on 5911 EGMs from 744 devices, resulting in 66.9% FPR and 97.8% TPM. In the testing data set of 1354 EGMs from 119 devices, this algorithm achieved 45.8% FPR and 97.0% TPM. The new pause algorithm was trained on 7178 EGMs from 1490 devices, resulting in 70.9% FPR and 98.7% TPM. In the testing data set of 1442 EGMs from 340 devices, this algorithm achieved 74.4% FPR and 99.3% TPM. The new tachycardia algorithm was trained on 520 EGMs from 204 devices, resulting in 57.0% FPR and 96.6% TPM. In the testing data set of 459 EGMs from 237 devices, this algorithm achieved 57.9% FPR and 96.5% TPM. Conclusion The new algorithms substantially reduced false AF, pause, and tachycardia episodes while maintaining the majority of true arrhythmia episodes detected by the Abbott ICM algorithms that exist today. Implementing these algorithms in the next‐generation ICM systems may lead to improved detection accuracy, in‐clinic efficiency, and device battery longevity.