Bipolar Intracardiac Electrogram Active Interval Extraction During Atrial Fibrillation

Area of Science:

• Cardiology
• Biomedical Engineering
• Signal Processing

Background:

• Atrial fibrillation (AF) is a complex arrhythmia requiring precise analysis of intracardiac electrograms (IEGMs).
• Identifying active intervals (AIs) within IEGMs is crucial for understanding arrhythmia mechanisms and guiding treatment.
• Current methods for AI detection may lack accuracy or real-time applicability.

Purpose of the Study:

• To introduce novel methods for accurate identification of active intervals (AIs) in intracardiac electrograms (IEGMs).
• To address the challenge of analyzing complex arrhythmias like atrial fibrillation (AF) through precise AI detection.
• To enable real-time automated analysis of IEGMs during AF.

Main Methods:

• Formulated AI extraction as a sequence of hypothesis tests comparing segment variances.
• Proposed modified general-likelihood ratio (MGLR) and separating-function-estimation tests.
• Derived five test statistics (TSs) for AI detection via threshold crossing.

Main Results:

• Achieved high similarity between proposed methods and manual annotation (MA).
• Demonstrated true positive rates of 97.8% and false positive rates of 1.4% for an MGLR-based method.
• Reported mean absolute errors of 8.7 ms for AI onset, 13 ms for AI end, and 4.2 ms for mean cycle length.

Conclusions:

• The proposed methods accurately identify the onset and duration of AIs in IEGMs during AF.
• These novel techniques offer a reliable tool for real-time automated analysis of AF.
• The findings contribute to improved understanding and management of complex cardiac arrhythmias.