Assisting electrophysiological substrate quantification in atrial fibrillation ablation

Luca Faes, Raul Alcaraz, Fernando Hornero, Aikaterini Vraka, Jose J. Rieta, Joaquin Osca

Risultato della ricerca: Conference contribution


Catheter ablation (CA) is the most popular treatment of atrial fibrillation (AF) with good results in paroxysmal AF, while its efficiency is significantly reduced in persistent AF. With the equipment used for CA strongly depending on electro-gram (EGM) fractionation quantification, the use of a reliable fractionation estimator is crucial to reduce the high recurrence rates in persistent AF. This work introduces a non-linear EGM fractionation quantification technique, which is based on coarse-grained correlation dimension (CGCD) computed over epochs of 1 second. Recordings were firstly normalized, denoised and lowpass filtered. The final CGCD value was calculated by the median CGCD value of all the epochs that a recording consisted of. Results were evaluated on three groups. Groups 1 and 2 contained 24 high-quality and 119 mid-range EGMs, respectively, manually pre-classified by AF types following Wells' criteria, then classified according to their CGCD values. 20 pseudo-real Type IV EGMs formed group 3 that was also automatically classified by AF type. In Groups 1 and 2, classification accuracy was 100% and 84-85.7%, respectively, using 10-fold cross-validation. The receiver-operating characteristics (ROC) analysis for highly fractionated EGMs, showed 100% specificity and sensitivity in Group 1 and 87.5% specificity and 93.6% sensitivity in Group 2. CGCD was always consistent with the fractionation degree of EGMs. 100% of the EGMs in Group 3 were correctly identified as Type IV AF. High accuracy results indicate that the method can estimate precisely the AF Type and detect the existence of AF Type IV cases. Both things are crucial in assisting improved substrate mapping during CA procedures of persistent AF.
Lingua originaleEnglish
Titolo della pubblicazione ospite2019 7th E-Health and Bioengineering Conference, EHB 2019
Numero di pagine4
Stato di pubblicazionePublished - 2019

All Science Journal Classification (ASJC) codes

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  • ???subjectarea.asjc.3100.3105???
  • ???subjectarea.asjc.2700.2718???


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