Dominant Frequency Variability Mapping for Identifying Stable Drivers during Persistent Atrial Fibrillation using Non‐Contact Mapping

Catheter ablation is a widely-used therapy to treat atrial fibrillation (AF), but the identification of ablation targets remain challenging in persistent AF (persAF). Dominant frequency (DF) mapping has been shown to be spatiotemporally unstable in persAF, with DF variability (DFV) correlating with the spectral organisation index (OI). This study aims to assess DFV at ablation sites between patients with AF termination and non-termination. 10 persAF patients undergoing LA ablation were enrolled. AF was terminated in 4 patients after ablating highest DFs. 2048-channel virtual electrograms (Ensite Array) were analysed in Matlab. DFV index (DFVI) was proposed to quantify DF temporal stability. Mock ablation targets were identified based on DFVI and the percentage of region actually ablated was computed. Ablation sites in termination patients revealed higher OI and lower DFVI. In the termination group, a greater proportion of DFVI was ablated. Atrial regions with higher temporal stability and organisation may offer more precise locations of stable focal drivers and may lead to higher success in AF termination following ablation

Unsupervised k‐mean classification of atrial electrograms from human persistent atrial fibrillation

The dichotomous criterion for atrial electrogram (AEG) classification as proposed by commercial systems (normal/fractionated) to guide ablation has been shown insufficient for persistent atrial fibrillation (persAF) therapy. In this study, we used unsupervised classification to investigate possible sub-groups of persAF AEGs. 3745 bipolar AEGs were collected from 14 persAF patients after pulmonary vein isolation. Automated AEG classification (normal/fractionated) was performed using the CARTO criterion (Biosense Webster). The CARTO attributes (ICL, ACI and SCI) were used to create a 3D space distribution. K-mean with five groups was implemented. Group 1 (43%) represents normal AEGs with low ICL, high ACI and SCI. Groups 2 (9%) and 3 (9%) have shown similar low ICL, but Group 3 has shown AEGs with short activation intervals, as opposed to Group 2. Group 4 (23%) suggests moderated fractionation, with high ACI but low SCI. Group 5 (15%) has shown highly fractionated AEGs with high ICL, low ACI and SCI. The three attributes were significantly different among the five groups (P<0.0001), except ICL between Groups 3 and 4 (P>0.999) and SCI between Groups 3 and 5 (P>0.999). The five sub-groups of AEGs found by the k-mean have shown distinct characteristics, which could provide a more detailed characterization of the atrial substrate during ablation

A K-Nearest Neighbour Classifier for Predicting Catheter Ablation Responses Using Noncontact Electrograms During Persistent Atrial Fibrillation

The mechanisms for the initiation and maintenance of atrial fibrillation (AF) are still poorly understood. Identification of atrial sites which are effective ablation targets remains challenging. Supervised machine learning has emerged as an effective tool for handling classification problems with multiple features. The main goal of this work is to use learning algorithms in predicting the responses of ablating electrograms and their effect on terminating AF and the cycle length changes. A total of 3,206 electrograms (EGMs) from ten persistent AF (persAF) patients were used. 5-fold cross-validation was applied, in which 80 % of the data were used as training set and 20 % used as validation. Dominant frequency (DF) and organisation index (OI) were calculated from EGMs (264 seconds) for all patients and used as input features. A k-nearest neighbour (KNN) classifier was trained using ablation lesion data and deployed in additional 17,274 EGMs that were not ablated. The classification accuracy of 85.2 % was achieved for the KNN classifier. We have proposed a supervised learning algorithm using DF features, which has shown the ability of accurately performing EGM signal classification that could be potentially used to identify ablation targets and become a robust real-time patient diagnosis system

Machine learning classifiers for predicting catheter ablation responses using non-contact electrograms during persistent atrial fibrillation

Background:Identification of atrial sites which are effective ablation targets remains challenging in atrial fibrillation (AF) therapy.Purpose: We thought to test machine learning algorithm in predicting the responses of ablating electrograms (EGMs) and their effect on terminating persistent atrial fibrillation (persAF).Methods:A total of 3,206 non-contact electrograms (EGMs) of 51 ablation lesion sites from ten persAF patients undergoing left atrial (LA) catheter ablation (2048-channel Ensite Array) were used as training dataset. AF cycle length (AFCL) changes before and after ablating each LA site were recorded in Labsystem Pro recording system. The EGMs were labelled in four classes by AFCL changes (with 10 ms threshold) after ablation: AF termination; AFCL increase; AFCL unchanged and AFCL decrease. Dominant frequency (DF) and organisation index (OI) were calculated from all EGMs (264 seconds) and used as input features. A group of machine classifiers were trained using the training dataset to predict the ablation response and deployed. 5-fold cross-validation was considered (80% of the data for training; 20% for validation).Results:The accuracy of the investigated classifiers was 69.66 % ± 12.60 %. The best performing Fine k-nearest neighbour (KNN) classifier achieved 85.3% of accuracy in the classification of the four classes. For AF termination classification (Area under the curve (AUC) = 0.98) from all four classes, a sensitivity of 87% and a specificity of 98% were achieved, whilst classifying AFCL increase group (AUC = 0.96) resulted in a sensitivity of 84% and a specificity of 92%.Conclusion:This work presents a machine learning framework to identify EGMs that are responsible for maintenance of persAF and potential targets for catheter ablation using panoramic non-contact mapping. Supervised learning algorithms on frequency features of long EGMs showed the ability to predict ablation responses measured by AFCL changes and AF termination. Targeting atrial regions with appropriate frequency characteristics might improve ablation outcome in persAF.</p

Characteristics of Ablated Rotors in Terminating Persistent Atrial Fibrillation Using Non-Contact Mapping

Early data has shown beneficial outcomes after rotor-guided catheter ablation in persistent atrial fibrillation (persAF). We aim to investigate the rotor characteristics at ablation sites that terminated AF (terminators) compared to those at sites that did not (non-terminators)

P439Could regional electrogram desynchronization identified using mean phase coherence be potential ablation targets in persistent atrial fibrillation?

Background It remains controversial as to whether rotors detected using phase mapping during persistent atrial fibrillation (persAF) represent main drivers of the underlying mechanism as others found rotors to be located near line of conduction block. Regional electrogram desynchronization (RED) has been suggested as successful targets for persAF ablation, but automatic tools and quantitative measures are lacking. Purpose We aim to use mean phase coherence (MPC) to automatically identify RED regions during persAF. This method was compared with phase singularity density (PSD) maps. Methods Patients undergoing left atrial (LA) persAF ablation were enrolled (n = 10). 2048-channel virtual electrograms (VEGMs) were collected from each patient using non-contact mapping (St Jude Velocity System, Ensite Array) for 10 seconds. To remove far field ventricular activities, QRS onset and T wave end locations were detected from ECG lead I (Figure 1A) and only the VEGM segments from T end to QRS onset were included in the analysis. VEGMs were reconstructed using sinusoidal wavelets fitting and the phase of VEGMs determined using Hilbert transform. Phase singularities (PS) were detected using the topological charge method and repetitive PSD maps were generated. RED was defined as the average of MPC of each node against direct neighbouring nodes on the 3D mesh (Figure 1A-B). Linear regression analysis was used to compare the average MPC vs. PSD and vs. the standard deviation of MPC (MPC_SD). ResultsA total of 221,184 VEGM segments were analysed with mean duration of 364.2 milliseconds. MPC has shown the ability to quantify the level of synchronisation between VEGMs (Figure 1B). Inverse correlation was found between PSD and average MPC values for all 10 patients (p Conclusion We have proposed a method to quantify the level of synchronisation between VEGMs. Phase density mapping showed a considerable agreement with RED regions reflecting regional conducting delays, which supports the previous finding where rotors found at conduction block. Inverse correlation between local average MPC and MPC_SD suggests that conduction delays of the identified regions are not heterogenous, posing directional preferences. Rather than solely looking for rotational activities, this method could identify comprehensive RED regions, which may also explain the conflicting results from different studies targeting rotational activities, where incomplete subsets of RED regions could have been targeted. Atrial RED regions can easily be identified with simultaneously collected electrograms from multi-polar catheters and should be targeted in future persAF studies. </div

The temporal behavior and consistency of bipolar atrial electrograms in human persistent atrial fibrillation

The unstable temporal behavior of atrial electrical activity during persistent atrial fibrillation (persAF) might influence ablation target identification, which could explain the conflicting persAF ablation outcomes in previous studies. We sought to investigate the temporal behavior and consistency of atrial electrogram (AEG) fractionation using different segment lengths. Seven hundred ninety-seven bipolar AEGs were collected with three segment lengths (2.5, 5,and 8 s) from 18 patients undergoing persAF ablation. The AEGs with 8-s duration were divided into three 2.5-s consecutive segments. AEG fractionation classification was applied off-line to all cases following the CARTO criteria; 43% of the AEGs remained fractionated for the three consecutive AEG segments, while nearly 30% were temporally unstable. AEG classification within the consecutive segments had moderate correlation (segment 1 vs 2: Spearman's correlation ρ = 0.74, kappa score κ = 0.62; segment 1 vs 3: ρ = 0.726, κ = 0.62; segment 2 vs 3: ρ = 0.75, κ = 0.68). AEG classifications were more similar between AEGs with 5 and 8 s (ρ = 0.96, κ = 0.87) than 2.5 versus 5 s (ρ = 0.93, κ = 0.84) and 2.5 versus 8 s (ρ = 0.90, κ = 0.78). Our results show that the CARTO criteria should be revisited and consider recording duration longer than 2.5 s for consistent ablation target identification in persAF

Time and frequency domain similarities of atrial activations during chronic atrial fibrillation

Time and frequency domain similarities of atrial activations during chronic atrial fibrillatio

ElectroMap: High-throughput open-source software for analysis and mapping of cardiac electrophysiology.

The ability to record and analyse electrical behaviour across the heart using optical and electrode mapping has revolutionised cardiac research. However, wider uptake of these technologies is constrained by the lack of multi-functional and robustly characterised analysis and mapping software. We present ElectroMap, an adaptable, high-throughput, open-source software for processing, analysis and mapping of complex electrophysiology datasets from diverse experimental models and acquisition modalities. Key innovation is development of standalone module for quantification of conduction velocity, employing multiple methodologies, currently not widely available to researchers. ElectroMap has also been designed to support multiple methodologies for accurate calculation of activation, repolarisation, arrhythmia detection, calcium handling and beat-to-beat heterogeneity. ElectroMap implements automated signal segmentation, ensemble averaging and integrates optogenetic approaches. Here we employ ElectroMap for analysis, mapping and detection of pro-arrhythmic phenomena in silico, in cellulo, animal model and in vivo patient datasets. We anticipate that ElectroMap will accelerate innovative cardiac research and enhance the uptake, application and interpretation of mapping technologies leading to novel approaches for arrhythmia prevention