Cardiac conduction velocity estimation from sequential mapping assuming known Gaussian distribution for activation...

Area of Science:

• Biomedical Engineering
• Cardiovascular Physiology
• Signal Processing

Background:

• Accurate estimation of cardiac conduction velocity (CCV) is crucial for diagnosing cardiac arrhythmias.
• Sequential intracardiac mapping provides electrograms from multiple cardiac sites, enabling CCV assessment.
• Existing methods may be sensitive to noise in activation time (AT) extraction.

Purpose of the Study:

• To develop a novel maximum likelihood estimator for CCV.
• To address the challenge of unknown synchronization times between recording sites.
• To account for estimation errors in activation times during CCV calculation.

Main Methods:

• Derivation of a maximum likelihood CCV estimator under stable planar wavefront propagation assumptions.
• Modeling activation time estimation errors using zero-mean white Gaussian noise with known variances.
• Analytical evaluation of the estimator's performance, including mean square estimation error.

Main Results:

• The proposed maximum likelihood estimator provides accurate CCV estimation.
• Analytical error analysis quantifies the estimator's performance.
• Simulation results validate the accuracy of the developed CCV estimation method.

Conclusions:

• The novel CCV estimator is accurate and robust to noise in activation times.
• The analytical framework provides a reliable performance assessment.
• This method enhances the precision of intracardiac mapping for cardiac electrophysiology studies.