TY - GEN
T1 - A Ventricular Far-Field Artefact Filtering Technique for Atrial Electrograms
AU - Saha, Simanto
AU - Hartmann, Simon
AU - Linz, Dominik
AU - Sanders, Prashanthan
AU - Baumert, Mathias
N1 - Publisher Copyright:
© 2019 Creative Commons.
PY - 2019/9
Y1 - 2019/9
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85081117626&partnerID=8YFLogxK
U2 - 10.23919/CinC49843.2019.9005813
DO - 10.23919/CinC49843.2019.9005813
M3 - Conference contribution
AN - SCOPUS:85081117626
T3 - Computing in Cardiology
BT - 2019 Computing in Cardiology, CinC 2019
PB - IEEE Computer Society
T2 - 2019 Computing in Cardiology, CinC 2019
Y2 - 8 September 2019 through 11 September 2019
ER -