A ventricular activity cancellation algorithm based on event synchronous adaptive filter for single-lead...

Area of Science:

• Biomedical Engineering
• Cardiovascular Physiology
• Signal Processing

Background:

• Accurate atrial activity (AA) analysis is crucial for diagnosing arrhythmias.
• Existing ventricular activity (VA) cancellation algorithms are limited for single-lead ECG.
• Effective VA cancellation is a prerequisite for reliable AA detection.

Purpose of the Study:

• To propose a novel VA cancellation algorithm for single-lead ECG.
• To enhance the accuracy of atrial activity estimation, particularly in atrial fibrillation (AF).
• To develop a computationally efficient and real-time implementable algorithm.

Main Methods:

• Modeled thoracic ECG to develop a novel algorithm.
• Utilized an event-synchronous adaptive filter (ESAF) with ECG as primary input and event-synchronous impulse train (ESIT) as reference.
• Generated ESIT synchronized with VA by QRS complex detection.
• Applied the algorithm to AA estimation in AF electrocardiograms.

Main Results:

• The ESAF-based algorithm demonstrated superior performance compared to the averaged beat subtraction (ABS) method.
• Achieved performance comparable to principal component analysis (PCA) and singular value decomposition (SVD) algorithms.
• An expanded ESAF version showed reasonable performance for AF ECGs with bimorphic VAs.
• The algorithm precisely estimates AA with low computational cost, suitable for real-time implementation.

Conclusions:

• The proposed ESAF algorithm effectively cancels VA in single-lead ECG.
• It enables precise AA estimation, outperforming existing methods like ABS.
• The algorithm's real-time capability and efficiency suggest its potential to replace current standards.