A Ventricular Far-Field Artefact Filtering Technique for Atrial Electrograms

Intracardiac atrial electrograms (EGM) are prone to ventricular far-field potentials due to ventricular depolarization. In this study, a filtering technique integrating independent component analysis (ICA) and wavelet decomposition has been proposed to significantly reduce the ventricular far-field contents while preserving the EGM morphology related to atrial activations. First, the wavelet decomposition is applied to each unipolar EGM. Then, ICA is applied to the decomposed unipolar EGM components and surface ECG template. Each independent component is cross-correlated with the simultaneously recorded ECG template and the three components with higher correlation coefficients were eliminated before applying inverse ICA. Total of 126 unipolar EGM collected from an atrial fibrillation patient have been included. Results indicate that the proposed filtering can reduce the ventricular signal power by around 17 dB (decibel). Furthermore, the signal-to-noise ratio is increased by approximately 17 dB after applying the proposed filtering. In conclusion, the proposed filtering method could be used for atrial fibrillation-related intracardiac mapping for catheter ablation. Further studies on a larger dataset are essential to quantify the exact impact of ventricular artefacts on both unipolar and bipolar EGM and the effectiveness of the proposed filtering technique.

[1]  D. Chakrabarti,et al.  A fast fixed - point algorithm for independent component analysis , 1997 .

[2]  Elad Anter,et al.  Bipolar voltage amplitude: What does it really mean? , 2016, Heart rhythm.

[3]  Rajiv Mahajan,et al.  Pathophysiology of Paroxysmal and Persistent Atrial Fibrillation: Rotors, Foci and Fibrosis. , 2017, Heart, lung & circulation.

[4]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[5]  J. J. Rieta,et al.  Comparative study of methods for ventricular activity cancellation in atrial electrograms of atrial fibrillation , 2007, Physiological measurement.

[6]  Sabine Van Huffel,et al.  Source Separation From Single-Channel Recordings by Combining Empirical-Mode Decomposition and Independent Component Analysis , 2010, IEEE Transactions on Biomedical Engineering.

[7]  Mathias Baumert,et al.  Quantitative-Electrogram-Based Methods for Guiding Catheter Ablation in Atrial Fibrillation , 2016, Proceedings of the IEEE.

[8]  Mathias Baumert,et al.  Beamforming-inspired Spatial Filtering Technique for Intracardiac Electrograms , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[9]  José Millet-Roig,et al.  Atrial activity extraction for atrial fibrillation analysis using blind source separation , 2004, IEEE Transactions on Biomedical Engineering.

[10]  Mathias Baumert,et al.  Automatic A-Phase Detection of Cyclic Alternating Patterns in Sleep Using Dynamic Temporal Information , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.