Artificial intelligence for ventricular arrhythmia capability using ambulatory electrocardiograms.

Aims: European and American clinical guidelines for implantable cardioverter defibrillators are insufficiently accurate for ventricular arrhythmia (VA) risk stratification, leading to significant morbidity and mortality. Artificial intelligence offers a novel risk stratification lens through which VA capability can be determined from the electrocardiogram (ECG) in normal cardiac rhythm. The aim of this study was to develop and test a deep neural network for VA risk stratification using routinely collected ambulatory ECGs. Methods and results: A multicentre case-control study was undertaken to assess VA-ResNet-50, our open source ResNet-50-based deep neural network. VA-ResNet-50 was designed to read pyramid samples of three-lead 24 h ambulatory ECGs to decide whether a heart is capable of VA based on the ECG alone. Consecutive adults with VA from East Midlands, UK, who had ambulatory ECGs as part of their NHS care between 2014 and 2022 were recruited and compared with all comer ambulatory electrograms without VA. Of 270 patients, 159 heterogeneous patients had a composite VA outcome. The mean time difference between the ECG and VA was 1.6 years (⅓ ambulatory ECG before VA). The deep neural network was able to classify ECGs for VA capability with an accuracy of 0.76 (95% confidence interval 0.66-0.87), F1 score of 0.79 (0.67-0.90), area under the receiver operator curve of 0.8 (0.67-0.91), and relative risk of 2.87 (1.41-5.81). Conclusion: Ambulatory ECGs confer risk signals for VA risk stratification when analysed using VA-ResNet-50. Pyramid sampling from the ambulatory ECGs is hypothesized to capture autonomic activity. We encourage groups to build on this open-source model. Question: Can artificial intelligence (AI) be used to predict whether a person is at risk of a lethal heart rhythm, based solely on an electrocardiogram (an electrical heart tracing)? Findings: In a study of 270 adults (of which 159 had lethal arrhythmias), the AI was correct in 4 out of every 5 cases. If the AI said a person was at risk, the risk of lethal event was three times higher than normal adults. Meaning: In this study, the AI performed better than current medical guidelines. The AI was able to accurately determine the risk of lethal arrhythmia from standard heart tracings for 80% of cases over a year away-a conceptual shift in what an AI model can see and predict. This method shows promise in better allocating implantable shock box pacemakers (implantable cardioverter defibrillators) that save lives.

ECGVEDNET: A Variational Encoder-Decoder Network for ECG Delineation in Morphology Variant ECGs.

Electrocardiogram (ECG) delineation to identify the fiducial points of ECG segments, plays an important role in cardiovascular diagnosis and care. Whilst deep delineation frameworks have been deployed within the literature, several factors still hinder their development: (a) data availability: the capacity of deep learning models to generalise is limited by the amount of available data; (b) morphology variations: ECG complexes vary, even within the same person, which degrades the performance of conventional deep learning models. To address these concerns, we present a large-scale 12-leads ECG dataset, ICDIRS, to train and evaluate a novel deep delineation model-ECGVEDNET. ICDIRS is a large-scale ECG dataset with 156,145 QRS onset annotations and 156,145 T peak annotations. ECGVEDNET is a novel variational encoder-decoder network designed to address morphology variations. In ECGVEDNET, we construct a well-regularized latent space, in which the latent features of ECG follow a regular distribution and present smaller morphology variations than in the raw data space. Finally, a transfer learning framework is proposed to transfer the knowledge learned on ICDIRS to smaller datasets. On ICDIRS, ECGVEDNET achieves accuracy of 86.28%/88.31% within 5/10 ms tolerance for QRS onset and accuracy of 89.94%/91.16% within 5/10 ms tolerance for T peak. On QTDB, the average time errors computed for QRS onset and T peak are -1.86 ± 8.02 ms and -0.50 ± 12.96 ms, respectively, achieving state-of-the-art performances on both large and small-scale datasets. We will release the source code and the pre-trained model on ICDIRS once accepted.

Risk stratifier for sudden cardiac death beyond the left ventricular ejection fraction in Chagas cardiomyopathy.

BACKGROUND: Sudden cardiac death (SCD) risk markers are needed in Chagas cardiomyopathy (CC). Action potential duration restitution (APDR) dynamics is capable of extracting information on cardiac regional heterogeneity. This study intends to develop a patient-specific variables-based algorithm to predict SCD in the low-intermediate subgroups of the Rassi risk score. METHODS: Cross-sectional study of patients who underwent 24-h Holter for research purposes between January 1992 and February 2017. From 4-h ECG segment, RR series were generated and APDR dynamics metrics were calculated. Classification tree and sensitivity analysis were applied. As outcomes, SCD, SCD-free and non-cardiovascular death and 34 variables were included. RESULTS: Two hundred twenty-one (129 in the group SCD-free, 80 in the SCD group and 12 non-cardiovascular death group) were analyzed. In the groups with and without SCD (209 patients), the median age was 66 years, 52% were female, the cardiac involvement was mild to moderate in 72% with a Rassi point median of 8 (IQ: 3 to 11). The SCD group had more ventricular remodeling and more ventricular electrical instability. The occurrence of a %beats QTend/TendQ ratio > 1 (AUC, 0.96 (95% CI 0.89-0.98) present in more than 56.7% of the 4-h ECG segments was sufficient to identify patients of the SCD subgroup. Variables representing different stages of CC were also relevant in the model. CONCLUSION: It is possible to use APDR dynamics as an adjuvant in the SCD risk assessment in a subgroup of patients with a high risk of SCD and a very low risk of non-CV death with high power of discrimination.

Correction: Mäkynen et al. Wearable Devices Combined with Artificial Intelligence-A Future Technology for Atrial Fibrillation Detection? Sensors 2022, 22, 8588.

The authors wish to add two authors to the original paper [...].

Wearable Devices Combined with Artificial Intelligence-A Future Technology for Atrial Fibrillation Detection?

Atrial fibrillation (AF) is the most common cardiac arrhythmia in the world. The arrhythmia and methods developed to cure it have been studied for several decades. However, professionals worldwide are still working to improve treatment quality. One novel technology that can be useful is a wearable device. The two most used recordings from these devices are photoplethysmogram (PPG) and electrocardiogram (ECG) signals. As the price lowers, these devices will become significant technology to increase sensitivity, for monitoring and for treatment quality support. This is important as AF can be challenging to detect in advance, especially during home monitoring. Modern artificial intelligence (AI) has the potential to respond to this challenge. AI has already achieved state of the art results in many applications, including bioengineering. In this perspective, we discuss wearable devices combined with AI for AF detection, an approach that enables a new era of possibilities for the future.

Machine learning in sudden cardiac death risk prediction: a systematic review.

AIMS: Most patients who receive implantable cardioverter defibrillators (ICDs) for primary prevention do not receive therapy during the lifespan of the ICD, whilst up to 50% of sudden cardiac death (SCD) occur in individuals who are considered low risk by conventional criteria. Machine learning offers a novel approach to risk stratification for ICD assignment. METHODS AND RESULTS: Systematic search was performed in MEDLINE, Embase, Emcare, CINAHL, Cochrane Library, OpenGrey, MedrXiv, arXiv, Scopus, and Web of Science. Studies modelling SCD risk prediction within days to years using machine learning were eligible for inclusion. Transparency and quality of reporting (TRIPOD) and risk of bias (PROBAST) were assessed. A total of 4356 studies were screened with 11 meeting the inclusion criteria with heterogeneous populations, methods, and outcome measures preventing meta-analysis. The study size ranged from 122 to 124 097 participants. Input data sources included demographic, clinical, electrocardiogram, electrophysiological, imaging, and genetic data ranging from 4 to 72 variables per model. The most common outcome metric reported was the area under the receiver operator characteristic (n = 7) ranging between 0.71 and 0.96. In six studies comparing machine learning models and regression, machine learning improved performance in five. No studies adhered to a reporting standard. Five of the papers were at high risk of bias. CONCLUSION: Machine learning for SCD prediction has been under-applied and incorrectly implemented but is ripe for future investigation. It may have some incremental utility in predicting SCD over traditional models. The development of reporting standards for machine learning is required to improve the quality of evidence reporting in the field.

Evaluating spatial disparities of rotor sites and high dominant frequency regions during catheter ablation for PersAF patients targeting high dominant frequency sites using non-contacting mapping.

Purpose: Several studies have emphasised the significance of high dominant frequency (HDF) and rotors in the perpetuation of AF. However, the co-localisation relationship between both attributes is not completely understood yet. In this study, we aim to evaluate the spatial distributions of HDF regions and rotor sites within the left atrium (LA) pre and post HDF-guided ablation in PersAF. Methods: This study involved 10 PersAF patients undergoing catheter ablation targeting HDF regions in the LA. 2048-channels of atrial electrograms (AEG) were collected pre- and post-ablation using a non-contact array (EnSite, Abbott). The dominant frequency (DF, 4-10 Hz) areas with DF within 0.25 Hz of the maximum out of the 2048 points were defined as "high" DF (HDF). Rotors were defined as PSs that last more than 100 ms and at a similar location through subsequent phase frames over time. Results: The results indicated an extremely poor spatial correlation between the HDF regions and sites of the rotors in pre-versus post-ablation cases for the non-terminated (pre: CORR; 0.05 ± 0.17. vs. post: CORR; -0.030 ± 0.19, and with terminated patients (pre: CORR; -0.016 ± 0.03. post: CORR; -0.022 ± 0.04). Rotors associated with AF terminations had a long-lasting life-span post-ablation (non-terminated vs. terminated 120.7 ± 6.5 ms vs. 139.9 ± 39.8 ms), high core velocity (1.35 ± 1.3 mm/ms vs. 1.32 ± 0.9 mm/ms), and were less meandering (3.4 ± 3.04 mm vs. 1.5 ± 1.2 mm). Although the results suggest a poor spatial overlapping between rotors' sites and sites of AFCL changes in terminated and non-terminated patients, a higher correlation was determined in terminated patients (spatial overlapping percentage pre: 25 ± 4.2% vs. 17 ± 3.8% vs. post: 8 ± 4.2% vs. 3.7 ± 1.7% p < 0.05, respectively). Conclusion: Using non-contact AEG, it was noted that the correlation is poor between the spatial distribution of HDF regions and sites of rotors. Rotors were longer-lasting, faster and more stationary in patients with AF termination post-ablation. Rotors sites demonstrated poor spatial overlapping with sites of AFCL changes that lead to AF termination.

Simultaneous Whole-Chamber Non-contact Mapping of Highest Dominant Frequency Sites During Persistent Atrial Fibrillation: A Prospective Ablation Study.

Purpose: Sites of highest dominant frequency (HDF) are implicated by many proposed mechanisms underlying persistent atrial fibrillation (persAF). We hypothesized that prospectively identifying and ablating dynamic left atrial HDF sites would favorably impact the electrophysiological substrate of persAF. We aim to assess the feasibility of prospectively identifying HDF sites by global simultaneous left atrial mapping. Methods: PersAF patients with no prior ablation history underwent global simultaneous left atrial non-contact mapping. 30 s of electrograms recorded during AF were exported into a bespoke MATLAB interface to identify HDF regions, which were then targeted for ablation, prior to pulmonary vein isolation. Following ablation of each region, change in AF cycle length (AFCL) was documented (≥ 10 ms considered significant). Baseline isopotential maps of ablated regions were retrospectively analyzed looking for rotors and focal activation or extinction events. Results: A total of 51 HDF regions were identified and ablated in 10 patients (median DF 5.8Hz, range 4.4-7.1Hz). An increase in AFCL of was seen in 20 of the 51 regions (39%), including AF termination in 4 patients. 5 out of 10 patients (including the 4 patients where AF termination occurred with HDF-guided ablation) were free from AF recurrence at 1 year. The proportion of HDF occurrences in an ablated region was not associated with change in AFCL (τ = 0.11, p = 0.24). Regions where AFCL decreased by 10 ms or more (i.e., AF disorganization) after ablation also showed lowest baseline spectral organization (p < 0.033 for any comparison). Considering all ablated regions, the average proportion of HDF events which were also HRI events was 8.0 ± 13%. Focal activations predominated (537/1253 events) in the ablated regions on isopotential maps, were modestly associated with the proportion of HDF occurrences represented by the ablated region (Kendall's τ = 0.40, p < 0.0001), and very strongly associated with focal extinction events (τ = 0.79, p < 0.0001). Rotors were rare (4/1253 events). Conclusion: Targeting dynamic HDF sites is feasible and can be efficacious, but lacks specificity in identifying relevant human persAF substrate. Spectral organization may have an adjunctive role in preventing unnecessary substrate ablation. Dynamic HDF sites are not associated with observable rotational activity on isopotential mapping, but epi-endocardial breakthroughs could be contributory.

Rationale and study design of the MINERVA study: Multicentre Investigation of Novel Electrocardiogram Risk markers in Ventricular Arrhythmia prediction-UK multicentre collaboration.

INTRODUCTION: The purpose of this study is to assess the ability of two new ECG markers (Regional Repolarisation Instability Index (R2I2) and Peak Electrical Restitution Slope) to predict sudden cardiac death (SCD) or ventricular arrhythmia (VA) events in patients with ischaemic cardiomyopathy undergoing implantation of an implantable cardioverter defibrillator for primary prevention indication. METHODS AND ANALYSIS: Multicentre Investigation of Novel Electrocardiogram Risk markers in Ventricular Arrhythmia prediction is a prospective, open label, single blinded, multicentre observational study to establish the efficacy of two ECG biomarkers in predicting VA risk. 440 participants with ischaemic cardiomyopathy undergoing routine first time implantable cardioverter-defibrillator (ICD) implantation for primary prevention indication are currently being recruited. An electrophysiological (EP) study is performed using a non-invasive programmed electrical stimulation protocol via the implanted device. All participants will undergo the EP study hence no randomisation is required. Participants will be followed up over a minimum of 18 months and up to 3 years. The first patient was recruited in August 2016 and the study will be completed at the final participant follow-up visit. The primary endpoint is ventricular fibrillation or sustained ventricular tachycardia >200 beats/min as recorded by the ICD. The secondary endpoint is SCD. Analysis of the ECG data obtained during the EP study will be performed by the core lab where blinding of patient health status and endpoints will be maintained. ETHICS AND DISSEMINATION: Ethical approval has been granted by Research Ethics Committees Northern Ireland (reference no. 16/NI/0069). The results will inform the design of a definitive Randomised Controlled Trial (RCT). Dissemination will include peer reviewed journal articles reporting the qualitative and quantitative results, as well as presentations at conferences and lay summaries. TRIAL REGISTRATION NUMBER: NCT03022487.

Electrocardiographic Imaging for Atrial Fibrillation: A Perspective From Computer Models and Animal Experiments to Clinical Value.

Electrocardiographic imaging (ECGI) is a technique to reconstruct non-invasively the electrical activity on the heart surface from body-surface potential recordings and geometric information of the torso and the heart. ECGI has shown scientific and clinical value when used to characterize and treat both atrial and ventricular arrhythmias. Regarding atrial fibrillation (AF), the characterization of the electrical propagation and the underlying substrate favoring AF is inherently more challenging than for ventricular arrhythmias, due to the progressive and heterogeneous nature of the disease and its manifestation, the small volume and wall thickness of the atria, and the relatively large role of microstructural abnormalities in AF. At the same time, ECGI has the advantage over other mapping technologies of allowing a global characterization of atrial electrical activity at every atrial beat and non-invasively. However, since ECGI is time-consuming and costly and the use of electrical mapping to guide AF ablation is still not fully established, the clinical value of ECGI for AF is still under assessment. Nonetheless, AF is known to be the manifestation of a complex interaction between electrical and structural abnormalities and therefore, true electro-anatomical-structural imaging may elucidate important key factors of AF development, progression, and treatment. Therefore, it is paramount to identify which clinical questions could be successfully addressed by ECGI when it comes to AF characterization and treatment, and which questions may be beyond its technical limitations. In this manuscript we review the questions that researchers have tried to address on the use of ECGI for AF characterization and treatment guidance (for example, localization of AF triggers and sustaining mechanisms), and we discuss the technological requirements and validation. We address experimental and clinical results, limitations, and future challenges for fruitful application of ECGI for AF understanding and management. We pay attention to existing techniques and clinical application, to computer models and (animal or human) experiments, to challenges of methodological and clinical validation. The overall objective of the study is to provide a consensus on valuable directions that ECGI research may take to provide future improvements in AF characterization and treatment guidance.

Automatic Extraction of Recurrent Patterns of High Dominant Frequency Mapping During Human Persistent Atrial Fibrillation.

Purpose: Identifying targets for catheter ablation remains challenging in persistent atrial fibrillation (persAF). The dominant frequency (DF) of atrial electrograms during atrial fibrillation (AF) is believed to primarily reflect local activation. Highest DF (HDF) might be responsible for the initiation and perpetuation of persAF. However, the spatiotemporal behavior of DF remains not fully understood. Some DFs during persAF were shown to lack spatiotemporal stability, while others exhibit recurrent behavior. We sought to develop a tool to automatically detect recurrent DF patterns in persAF patients. Methods: Non-contact mapping of the left atrium (LA) was performed in 10 patients undergoing persAF HDF ablation. 2,048 virtual electrograms (vEGMs, EnSite Array, Abbott Laboratories, USA) were collected for up to 5 min before and after ablation. Frequency spectrum was estimated using fast Fourier transform and DF was identified as the peak between 4 and 10 Hz and organization index (OI) was calculated. The HDF maps were identified per 4-s window and an automated pattern recognition algorithm was used to find recurring HDF spatial patterns. Dominant patterns (DPs) were defined as the HDF pattern with the highest recurrence. Results: DPs were found in all patients. Patients in atrial flutter after ablation had a single DP over the recorded time period. The time interval (median [IQR]) of DP recurrence for the patients in AF after ablation (7 patients) decreased from 21.1 s [11.8 49.7 s] to 15.7 s [6.5 18.2 s]. The DF inside the DPs presented lower temporal standard deviation (0.18 ± 0.06 Hz vs. 0.29 ± 0.12 Hz, p < 0.05) and higher OI (0.35 ± 0.03 vs. 0.31 ± 0.04, p < 0.05). The atrial regions with the highest proportion of HDF region were the septum and the left upper pulmonary vein. Conclusion: Multiple recurrent spatiotemporal HDF patterns exist during persAF. The proposed method can identify and quantify the spatiotemporal repetition of the HDFs, where the high recurrences of DP may suggest a more organized rhythm. DPs presented a more consistent DF and higher organization compared with non-DPs, suggesting that DF with higher OI might be more likely to recur. Recurring patterns offer a more comprehensive dynamic insight of persAF behavior, and ablation targeting such regions may be beneficial.

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

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.

Standardizing Single-Frame Phase Singularity Identification Algorithms and Parameters in Phase Mapping During Human Atrial Fibrillation.

PURPOSE: Recent investigations failed to reproduce the positive rotor-guided ablation outcomes shown by initial studies for treating persistent atrial fibrillation (persAF). Phase singularity (PS) is an important feature for AF driver detection, but algorithms for automated PS identification differ. We aim to investigate the performance of four different techniques for automated PS detection. METHODS: 2048-channel virtual electrogram (VEGM) and electrocardiogram signals were collected for 30 s from 10 patients undergoing persAF ablation. QRST-subtraction was performed and VEGMs were processed using sinusoidal wavelet reconstruction. The phase was obtained using Hilbert transform. PSs were detected using four algorithms: (1) 2D image processing based and neighbor-indexing algorithm; (2) 3D neighbor-indexing algorithm; (3) 2D kernel convolutional algorithm estimating topological charge; (4) topological charge estimation on 3D mesh. PS annotations were compared using the structural similarity index (SSIM) and Pearson's correlation coefficient (CORR). Optimized parameters to improve detection accuracy were found for all four algorithms using F ß score and 10-fold cross-validation compared with manual annotation. Local clustering with density-based spatial clustering of applications with noise (DBSCAN) was proposed to improve algorithms 3 and 4. RESULTS: The PS density maps created by each algorithm with default parameters were poorly correlated. Phase gradient threshold and search radius (or kernels) were shown to affect PS detections. The processing times for the algorithms were significantly different (p < 0.0001). The F ß scores for algorithms 1, 2, 3, 3 + DBSCAN, 4 and 4 + DBSCAN were 0.547, 0.645, 0.742, 0.828, 0.656, and 0.831. Algorithm 4 + DBSCAN achieved the best classification performance with acceptable processing time (2.0 ± 0.3 s). CONCLUSION: AF driver identification is dependent on the PS detection algorithms and their parameters, which could explain some of the inconsistencies in rotor-guided ablation outcomes in different studies. For 3D triangulated meshes, algorithm 4 + DBSCAN with optimal parameters was the best solution for real-time, automated PS detection due to accuracy and speed. Similarly, algorithm 3 + DBSCAN with optimal parameters is preferred for uniform 2D meshes. Such algorithms - and parameters - should be preferred in future clinical studies for identifying AF drivers and minimizing methodological heterogeneities. This would facilitate comparisons in rotor-guided ablation outcomes in future works.

Systematic differences of non-invasive dominant frequency estimation compared to invasive dominant frequency estimation in atrial fibrillation.

Non-invasive analysis of atrial fibrillation (AF) using body surface mapping (BSM) has gained significant interest, with attempts at interpreting atrial spectro-temporal parameters from body surface signals. As these body surface signals could be affected by properties of the torso volume conductor, this interpretation is not always straightforward. This paper highlights the volume conductor effects and influences of the algorithm parameters for identifying the dominant frequency (DF) from cardiac signals collected simultaneously on the torso and atrial surface. Bi-atrial virtual electrograms (VEGMs) and BSMs were recorded simultaneously for 5 min from 10 patients undergoing ablation for persistent AF. Frequency analysis was performed on 4 s segments. DF was defined as the frequency with highest power between 4 and 10 Hz with and without applying organization index (OI) thresholds. The volume conductor effect was assessed by analyzing the highest DF (HDF) difference of each VEGM HDF against its BSM counterpart. Significant differences in HDF values between intra-cardiac and torso signals could be observed, independent of OI threshold. This difference increases with increasing endocardial HDF (BSM-VEGM median difference from -0.13 Hz for VEGM HDF at 6.25 ±â€¯0.25 Hz to -4.24 Hz at 9.75 ±â€¯0.25 Hz), thereby confirming the theory of the volume conductor effect in real-life situations. Applying an OI threshold strongly affected the BSM HDF area size and location and atrial HDF area location. These results suggest that volume conductor and measurement algorithm effects must be considered for appropriate clinical interpretation.

The temporal stability of recurrence quantification analysis attributes from chronic atrial fibrillation electrograms

Abstract Introduction The temporal behavior of atrial electrograms (AEGs) collected during persistent atrial fibrillation (persAF) directly affects ablative treatment outcomes. We investigated different durations of AEGs collected during persAF using recurrence quantification analysis (RQA). Methods 797 bipolar AEGs with different durations (from 0.5 s to 8 s) from 18 patients were investigated. Four RQA-based attributes were evaluated based on AEG durations: determinism (DET); recurrence rate (RR); laminarity (LAM); and diagonal lines' entropy (ENTR). The Spearman correlation (ρ) between each duration versus 8 s was calculated. AEG classification was performed following the CARTO criteria (Biosense Webster) and receiving operating characteristic (ROC) curves were created for the RQA variables. Results The RQA variables successfully discriminated the AEGs: the area under the ROC curves were as high as 0.70 for AEGs with 3.5 s or greater. Three types of AEGs were found using these variables: normal, fractionated and temporally unstable. The number of unstable AEGs decreased with longer AEG segments. Different AEG durations significantly affected the RQA variables (P<0.0001), with no statistical difference between the durations 6 s, 7 s and 8 s for DET, LAM and ENTR, and no difference between 7 s and 8 s for RR (P<0.0001). AEGs with 3 s or longer have shown ρ ≥ 80% for all variables. Conclusion The RQA variables have been shown effective in the characterization of AEGs collected during persAF with a shorter duration than current recommendations, which motivates their use for the characterization of atrial substrate during persAF ablation.

Characterization of human persistent atrial fibrillation electrograms using recurrence quantification analysis.

Atrial fibrillation (AF) is regarded as a complex arrhythmia, with one or more co-existing mechanisms, resulting in an intricate structure of atrial activations. Fractionated atrial electrograms (AEGs) were thought to represent arrhythmogenic tissue and hence have been suggested as targets for radiofrequency ablation. However, current methods for ablation target identification have resulted in suboptimal outcomes for persistent AF (persAF) treatment, possibly due to the complex spatiotemporal dynamics of these mechanisms. In the present work, we sought to characterize the dynamics of atrial tissue activations from AEGs collected during persAF using recurrence plots (RPs) and recurrence quantification analysis (RQA). 797 bipolar AEGs were collected from 18 persAF patients undergoing pulmonary vein isolation (PVI). Automated AEG classification (normal vs. fractionated) was performed using the CARTO criteria (Biosense Webster). For each AEG, RPs were evaluated in a phase space estimated following Takens' theorem. Seven RQA variables were obtained from the RPs: recurrence rate; determinism; average diagonal line length; Shannon entropy of diagonal length distribution; laminarity; trapping time; and Shannon entropy of vertical length distribution. The results show that the RQA variables were significantly affected by PVI, and that the variables were effective in discriminating normal vs. fractionated AEGs. Additionally, diagonal structures associated with deterministic behavior were still present in the RPs from fractionated AEGs, leading to a high residual determinism, which could be related to unstable periodic orbits and suggesting a possible chaotic behavior. Therefore, these results contribute to a nonlinear perspective of the spatiotemporal dynamics of persAF.

LifeMap: towards the development of a new technology in sudden cardiac death risk stratification for clinical use.

Sudden cardiac death (SCD) is a major cause of mortality presenting a significant unmet clinical need. Patients at risk of SCD are implanted with implantable cardioverter-defibrillators (ICDs) according to international guidelines based on clinical trial evidence. Implantable cardioverter-defibrillators are not inexpensive and not without problem in terms of inappropriate shocks and infection risk. Also, only a minority of patients implanted with the ICD ever use the device during its battery lifetime highlighting the fact that methods used for SCD risk stratification are inadequate. Better ways of predicting who is at risk of SCD are needed. In addition, there is no effective prevention due to the lack of understanding of the electrical mechanisms underlying SCD. Our group has been investigating the electrophysiological basis of ventricular fibrillation and have successfully applied our preclinical findings to translational studies in patients with ischaemic cardiomyopathy. We have developed two ECG markers which have been shown to be strong predictors of ventricular arrhythmias and SCD. Ongoing clinical studies are being carried out including a multicentre UK study to consolidate the evidence base. They are being incorporated into the technology, LifeMap, with the aim to develop a successful clinical tool for the assessment of SCD risk. We hereby present the scientific data leading to the technology and the development to date. The information provided here was presented at the European Heart Rhythm Association (EHRA) Europace/Cardiostim conference at which LifeMap won the EHRA Inventors Award 2016.

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.