Novel algorithms improve arrhythmia detection accuracy in insertable cardiac monitors.

Area of Science:

• Biomedical Engineering
• Cardiology
• Medical Devices

Background:

• Insertable cardiac monitors (ICMs) are crucial for diagnosing cardiac arrhythmias.
• Inaccurate R-wave sensing leads to false detections in current ICM systems, impacting diagnostic reliability.

Purpose of the Study:

• To develop and evaluate novel discrimination algorithms for ICMs.
• To reduce false positive detections of atrial fibrillation (AF), pause, and tachycardia episodes.
• To maintain high true positive rates for accurate arrhythmia detection.

Main Methods:

• Extracted and adjudicated electrograms (EGMs) from Abbott Confirm Rx™ ICMs.
• Developed new algorithms targeting R-wave sensing, P-wave identification, and R-R interval patterns.
• Quantified algorithm performance using false positive reduction (FPR) and true positive maintenance (TPM).

Main Results:

• The AF algorithm achieved 45.8% FPR and 97.0% TPM in testing.
• The pause algorithm achieved 74.4% FPR and 99.3% TPM in testing.
• The tachycardia algorithm achieved 57.9% FPR and 96.5% TPM in testing.

Conclusions:

• New algorithms substantially decrease false AF, pause, and tachycardia detections.
• High true positive rates are maintained, preserving detection of genuine arrhythmias.
• Implementation in next-generation ICMs promises enhanced accuracy, efficiency, and battery life.