Reliable paroxysmal atrial fibrillation substrate assessment during sinus rhythm through optimal estimation of local activation waves dynamics

Luca Faes, Raúl Alcaraz, Fernando Hornero, Aikaterini Vraka, José J. Rieta, Aurelio Quesada

Risultato della ricerca: Conference contribution


The analysis of coronary sinus (CS) electrograms (EGMs) during catheter ablation (CA) of atrial fibrillation (AF) is highly important for AF substrate evaluation. However, channels of the CS catheter may be affected by vigorous cardiac movement and bad contact. This work investigates the most reliable channels in preserving the AF dynamics during sinus rhythm (SR). Local activation waves (LAWs) were detected in 44 bipolar CS recordings of 60-300 seconds duration in 28 paroxysmal AF patients undergoing CA. Recordings consisted of five channels: distal, mid-distal, medial, mid-proximal and proximal. LAW duration, amplitude, area and correlation between dominant morphologies of each channel were calculated. Multichannel comparison and analysis in pairs of channels were performed using Kruskal-Wallis and Mann-Whitney U-test, respectively. The latter, with Bonferroni correction, was also used for comparison between one and the remaining channels. Median values were calculated. Distal channel presented the longest duration (p = 0.047) and the lowest amplitude LAWs (pmax < 0.002), with the smallest area (pmax < 0.020). LAWs in medial were the shortest (p = 0.084) and with the highest amplitude (pmax < 0.003), followed by LAWs in mid-proximal channel (pmax < 0.050). The latter, showed the highest area (pmax < 0.070). LAWs in mid-proximal channel showed the highest correlation with proximal (96.02%) and medial (95.02%) channels, while distal channel contained the least correlated LAWs with medial (85.82%) and proximal (85.57%) channels. Recordings of medial and mid-proximal channels seem to preserve at most the AF dynamics and their analysis is encouraged for studies assessing the AF substrate during SR.
Lingua originaleEnglish
Titolo della pubblicazione ospiteE-HEALTH AND BIOENGINEERING CONFERENCE
Numero di pagine4
Stato di pubblicazionePublished - 2020

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems and Management
  • Biomedical Engineering
  • Health Informatics
  • Health(social science)

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