Unsupervised Classification of Atrial Electrograms for Electroanatomic Mapping of Human Persistent Atrial Fibrillation

• Published in: IEEE transactions on biomedical engineering Vol. 68; no. 4; pp. 1131 - 1141
• Main Authors: Almeida, Tiago P., Soriano, Diogo C., Mase, Michela, Ravelli, Flavia, Bezerra, Arthur S., Li, Xin, Chu, Gavin S., Salinet, Joao, Stafford, Peter J., Andre Ng, G., Schlindwein, Fernando S., Yoneyama, Takashi
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-04-2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ablation; Arrhythmia; Atrial fibrillation; Cardiac arrhythmia; catheter ablation; Catheters; Classification; Determinism; Electrocardiography; electrophysiology mapping; Entropy; Feature extraction; Fibrillation; Fractionation; Ground truth; k-means; Markers; Rotors; Substrates; Target recognition; Three-dimensional displays; unsupervised classification
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2020.3021480

Objective: Ablation treatment for persistent atrial fibrillation (persAF) remains challenging due to the absence of a 'ground truth' for atrial substrate characterization and the presence of multiple mechanisms driving the arrhythmia. We implemented an unsupervised classification to identify clusters of atrial electrograms (AEGs) with similar patterns, which were then validated by AEG-derived markers. Methods : 956 bipolar AEGs were collected from 11 persAF patients. CARTO variables (Biosense Webster; ICL, ACI and SCI) were used to create a 3D space, and subsequently used to perform an unsupervised classification with k-means. The characteristics of the identified groups were investigated using nine AEG-derived markers: sample entropy (SampEn), dominant frequency, organization index (OI), determinism, laminarity, recurrence rate (RR), peak-to-peak (PP) amplitude, cycle length (CL), and wave similarity (WS). Results : Five AEG classes with distinct characteristics were identified (F = 582, P<0.0001). The presence of fractionation increased from class 1 to 5, as reflected by the nine markers. Class 1 (25%) included organized AEGs with high WS, determinism, laminarity, and RR, and low SampEn. Class 5 (20%) comprised fractionated AEGs with in low WS, OI, determinism, laminarity, and RR, and in high SampEn. Classes 2 (12%), 3 (13%) and 4 (30%) suggested different degrees of AEG organization. Conclusions : Our results expand and reinterpret the criteria used for automated AEG classification. The nine markers highlighted electrophysiological differences among the five classes found by the k-means, which could provide a more complete characterization of persAF substrate during ablation target identification in future clinical studies.