Vector Field Heterogeneity for the Assessment of Locally Disorganised Cardiac Electrical Propagation Wavefronts From High-Density Multielectrodes.

High-density multielectrode catheters are becoming increasingly popular in cardiac electrophysiology for advanced characterisation of the cardiac tissue, due to their potential to identify impaired sites. These are often characterised by abnormal electrical conduction, which may cause locally disorganised propagation wavefronts. To quantify it, a novel heterogeneity parameter based on vector field analysis is proposed, utilising finite differences to measure direction changes between adjacent cliques. The proposed Vector Field Heterogeneity metric has been evaluated on a set of simulations with controlled levels of organisation in vector maps, and a variety of grid sizes. Furthermore, it has been tested on animal experimental models of isolated Langendorff-perfused rabbit hearts. The proposed parameter exhibited superior capturing ability of heterogeneous propagation wavefronts compared to the classical Spatial Inhomogeneity Index, and simulations proved that the metric effectively captures gradual increments in disorganisation in propagation patterns. Notably, it yielded robust and consistent outcomes for [Formula: see text] grid sizes, underscoring its suitability for the latest generation of orientation-independent cardiac catheters.

Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillator.

BACKGROUND: Risk stratification for ventricular arrhythmias currently relies on static measurements that fail to adequately capture dynamic interactions between arrhythmic substrate and triggers over time. We trained and internally validated a dynamic machine learning (ML) model and neural network that extracted features from longitudinally collected electrocardiograms (ECG), and used these to predict the risk of malignant ventricular arrhythmias. METHODS: A multicentre study in patients implanted with an implantable cardioverter-defibrillator (ICD) between 2007 and 2021 in two academic hospitals was performed. Variational autoencoders (VAEs), which combine neural networks with variational inference principles, and can learn patterns and structure in data without explicit labelling, were trained to encode the mean ECG waveforms from the limb leads into 16 variables. Supervised dynamic ML models using these latent ECG representations and clinical baseline information were trained to predict malignant ventricular arrhythmias treated by the ICD. Model performance was evaluated on a hold-out set, using time-dependent receiver operating characteristic (ROC) and calibration curves. FINDINGS: 2942 patients (61.7 ± 13.9 years, 25.5% female) were included, with a total of 32,129 ECG recordings during a mean follow-up of 43.9 ± 35.9 months. The mean time-varying area under the ROC curve for the dynamic model was 0.738 ± 0.07, compared to 0.639 ± 0.03 for a static (i.e. baseline-only model). Feature analyses indicated dynamic changes in latent ECG representations, particularly those affecting the T-wave morphology, were of highest importance for model predictions. INTERPRETATION: Dynamic ML models and neural networks effectively leverage routinely collected longitudinal ECG recordings for personalised and updated predictions of malignant ventricular arrhythmias, outperforming static models. FUNDING: This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council (NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T).

Robust Framework for Medical Time Series Classification and Application to Real Scenarios in Modern Bioengineering.

In this study, a novel unsupervised classification framework for time series of medical nature is presented. This framework is based on the intersection of machine learning, Hilbert Spaces algebra, and signal theory. The methodology is illustrated through the resolution of three biomedical engineering problems: neuronal activity tracking, protein functional classification, and non-invasive diagnosis of atrial flutter (AFL). The results indicate that the proposed algorithms exhibit high proficiency in solving these tasks and demonstrate robustness in identifying damaged neuronal units while tracking healthy ones. Moreover, the application of the framework in protein functional classification provides a new perspective for the development of pharmaceutical products and personalised medicine. Additionally, the controlled environment of the framework in AFL simulation problem underscores the algorithm's ability to encode information efficiently. These results offer valuable insights into the potential of this framework and lay the groundwork for future studies.Clinical relevance- The framework proposed in this study has the potential to yield novel insights into the effects of newly implanted electrodes in the brain. Furthermore, the categorization of proteins by function could facilitate the development of personalised and efficient medicines, ultimately reducing both time and cost. The simulation of atrial flutter also demonstrates the framework's ability to encode information for arrhythmia diagnosis and treatment, which has the potential to lead to improved patient outcomes.

Mini Peltier Cell Array System for the Generation of Controlled Local Epicardial Heterogeneities.

The present study aims to design and fabricate a system capable of generating heterogeneities on the epicardial surface of an isolated rabbit heart perfused in a Langendorff system. The system consists of thermoelectric modules that can be independently controlled by the developed hardware, thereby allowing for the generation of temperature gradients on the epicardial surface, resulting in conduction slowing akin to heterogeneities of pathological origin. A comprehensive analysis of the system's viability was performed through modeling and thermal simulation, and its practicality was validated through preliminary tests conducted at the experimental cardiac electrophysiology laboratory of the University of Valencia. The design process involved the use of Fusion 360 for 3D designs, MATLAB/Simulink for algorithms and block diagrams, LTSpice and Altium Designer for schematic captures and PCB design, and the integration of specialized equipment for animal experimentation. The objective of the study was to efficiently capture epicardial recordings under varying conditions.Clinical relevance- The proposed system aims to induce local epicardial heterogeneities to generate labeled correct signals that can serve as a golden standard for improving algorithms that identify and characterize fibrotic substrates. This improvement will enhance the efficacy of ablation processes and potentially reduce the ablated surface area.

Segmenting computed tomograms for cardiac ablation using machine learning leveraged by domain knowledge encoding.

Background: Segmentation of computed tomography (CT) is important for many clinical procedures including personalized cardiac ablation for the management of cardiac arrhythmias. While segmentation can be automated by machine learning (ML), it is limited by the need for large, labeled training data that may be difficult to obtain. We set out to combine ML of cardiac CT with domain knowledge, which reduces the need for large training datasets by encoding cardiac geometry, which we then tested in independent datasets and in a prospective study of atrial fibrillation (AF) ablation. Methods: We mathematically represented atrial anatomy with simple geometric shapes and derived a model to parse cardiac structures in a small set of N = 6 digital hearts. The model, termed "virtual dissection," was used to train ML to segment cardiac CT in N = 20 patients, then tested in independent datasets and in a prospective study. Results: In independent test cohorts (N = 160) from 2 Institutions with different CT scanners, atrial structures were accurately segmented with Dice scores of 96.7% in internal (IQR: 95.3%-97.7%) and 93.5% in external (IQR: 91.9%-94.7%) test data, with good agreement with experts (r = 0.99; p < 0.0001). In a prospective study of 42 patients at ablation, this approach reduced segmentation time by 85% (2.3 ± 0.8 vs. 15.0 ± 6.9 min, p < 0.0001), yet provided similar Dice scores to experts (93.9% (IQR: 93.0%-94.6%) vs. 94.4% (IQR: 92.8%-95.7%), p = NS). Conclusions: Encoding cardiac geometry using mathematical models greatly accelerated training of ML to segment CT, reducing the need for large training sets while retaining accuracy in independent test data. Combining ML with domain knowledge may have broad applications.

Optimizing patient selection for primary prevention implantable cardioverter-defibrillator implantation: utilizing multimodal machine learning to assess risk of implantable cardioverter-defibrillator non-benefit.

AIMS: Left ventricular ejection fraction (LVEF) is suboptimal as a sole marker for predicting sudden cardiac death (SCD). Machine learning (ML) provides new opportunities for personalized predictions using complex, multimodal data. This study aimed to determine if risk stratification for implantable cardioverter-defibrillator (ICD) implantation can be improved by ML models that combine clinical variables with 12-lead electrocardiograms (ECG) time-series features. METHODS AND RESULTS: A multicentre study of 1010 patients (64.9 ± 10.8 years, 26.8% female) with ischaemic, dilated, or non-ischaemic cardiomyopathy, and LVEF ≤ 35% implanted with an ICD between 2007 and 2021 for primary prevention of SCD in two academic hospitals was performed. For each patient, a raw 12-lead, 10-s ECG was obtained within 90 days before ICD implantation, and clinical details were collected. Supervised ML models were trained and validated on a development cohort (n = 550) from Hospital A to predict ICD non-arrhythmic mortality at three-year follow-up (i.e. mortality without prior appropriate ICD-therapy). Model performance was evaluated on an external patient cohort from Hospital B (n = 460). At three-year follow-up, 16.0% of patients had died, with 72.8% meeting criteria for non-arrhythmic mortality. Extreme gradient boosting models identified patients with non-arrhythmic mortality with an area under the receiver operating characteristic curve (AUROC) of 0.90 [95% confidence intervals (CI) 0.80-1.00] during internal validation. In the external cohort, the AUROC was 0.79 (95% CI 0.75-0.84). CONCLUSIONS: ML models combining ECG time-series features and clinical variables were able to predict non-arrhythmic mortality within three years after device implantation in a primary prevention population, with robust performance in an independent cohort.

Evaluation and assessment of clique arrangements for the estimation of omnipolar electrograms in high density electrode arrays: an experimental animal model study.

High-density catheters combined with Orientation Independent Sensing (OIS) methods have emerged as a groundbreaking technology for cardiac substrate characterisation. In this study, we aim to assess the arrangements and constraints to reliably estimate the so-called omnipolar electrogram (oEGM). Performance was evaluated using an experimental animal model. Thirty-eight recordings from nine retrospective experiments on isolated perfused rabbit hearts with an epicardial HD multielectrode were used. We estimated oEGMs according to the classic triangular clique (4 possible orientations) and a novel cross-orientation clique arrangement. Furthermore, we tested the effects of interelectrode spacing from 1 to 4 mm. Performance was evaluated by means of several parameters that measured amplitude rejection ratios, electric field loop area, activation pulse width and morphology distortion. Most reliable oEGM estimations were obtained with cross-configurations and interelectrode spacings [Formula: see text] mm. Estimations from triangular cliques resulted in wider electric field loops and unreliable detection of the direction of the propagation wavefront. Moreover, increasing interelectrode distance resulted in increased pulse width and morphology distortion. The results prove that current oEGM estimation techniques are insufficiently accurate. This study opens a new standpoint for the design of new-generation HD catheters and mapping software.

Quantifying a spectrum of clinical response in atrial tachyarrhythmias using spatiotemporal synchronization of electrograms.

AIMS: There is a clinical spectrum for atrial tachyarrhythmias wherein most patients with atrial tachycardia (AT) and some with atrial fibrillation (AF) respond to ablation, while others do not. It is undefined if this clinical spectrum has pathophysiological signatures. This study aims to test the hypothesis that the size of spatial regions showing repetitive synchronized electrogram (EGM) shapes over time reveals a spectrum from AT, to AF patients who respond acutely to ablation, to AF patients without acute response. METHODS AND RESULTS: We studied n = 160 patients (35% women, 65.0 ± 10.4 years) of whom (i) n = 75 had AF terminated by ablation propensity matched to (ii) n = 75 without AF termination and (iii) n = 10 with AT. All patients had mapping by 64-pole baskets to identify areas of repetitive activity (REACT) to correlate unipolar EGMs in shape over time. Synchronized regions (REACT) were largest in AT, smaller in AF termination, and smallest in non-termination cohorts (0.63 ± 0.15, 0.37 ± 0.22, and 0.22 ± 0.18, P < 0.001). Area under the curve for predicting AF termination in hold-out cohorts was 0.72 ± 0.03. Simulations showed that lower REACT represented greater variability in clinical EGM timing and shape. Unsupervised machine learning of REACT and extensive (50) clinical variables yielded four clusters of increasing risk for AF termination (P < 0.01, χ2), which were more predictive than clinical profiles alone (P < 0.001). CONCLUSION: The area of synchronized EGMs within the atrium reveals a spectrum of clinical response in atrial tachyarrhythmias. These fundamental EGM properties, which do not reflect any predetermined mechanism or mapping technology, predict outcome and offer a platform to compare mapping tools and mechanisms between AF patient groups.

Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies.

BACKGROUND: Ventricular arrhythmia (VA) precipitating sudden cardiac arrest (SCD) is among the most frequent causes of death and pose a high burden on public health systems worldwide. The increasing availability of electrophysiological signals collected through conventional methods (e.g. electrocardiography (ECG)) and digital health technologies (e.g. wearable devices) in combination with novel predictive analytics using machine learning (ML) and deep learning (DL) hold potential for personalised predictions of arrhythmic events. METHODS: This systematic review and exploratory meta-analysis assesses the state-of-the-art of ML/DL models of electrophysiological signals for personalised prediction of malignant VA or SCD, and studies potential causes of bias (PROSPERO, reference: CRD42021283464). Five electronic databases were searched to identify eligible studies. Pooled estimates of the diagnostic odds ratio (DOR) and summary area under the curve (AUROC) were calculated. Meta-analyses were performed separately for studies using publicly available, ad-hoc datasets, versus targeted clinical data acquisition. Studies were scored on risk of bias by the PROBAST tool. FINDINGS: 2194 studies were identified of which 46 were included in the systematic review and 32 in the meta-analysis. Pooling of individual models demonstrated a summary AUROC of 0.856 (95% CI 0.755-0.909) for short-term (time-to-event up to 72 h) prediction and AUROC of 0.876 (95% CI 0.642-0.980) for long-term prediction (time-to-event up to years). While models developed on ad-hoc sets had higher pooled performance (AUROC 0.919, 95% CI 0.867-0.952), they had a high risk of bias related to the re-use and overlap of small ad-hoc datasets, choices of ML tool and a lack of external model validation. INTERPRETATION: ML and DL models appear to accurately predict malignant VA and SCD. However, wide heterogeneity between studies, in part due to small ad-hoc datasets and choice of ML model, may reduce the ability to generalise and should be addressed in future studies. FUNDING: This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council (NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T).

Performance assessment of electrode configurations for the estimation of omnipolar electrograms from high density arrays.

OBJECTIVE: The aim of this study is to propose a method to reduce the sensitivity of the estimated omnipolar electrogram (oEGM) with respect to the angle of the propagation wavefront. METHODS: A novel configuration of cliques taking into account all four electrodes of a squared cell is proposed. To test this approach, simulations of HD grids of cardiac activations at different propagation angles, conduction velocities, interelectrode distance and electrogram waveforms are considered. RESULTS: The proposed approach successfully provided narrower loops (essentially a straight line) of the electrical field described by the bipole pair with respect to the conventional approach. Estimation of the direction of propagation was improved. Additionally, estimated oEGMs presented larger amplitude, and estimations of the local activation times were more accurate. CONCLUSIONS: A novel method to improve the estimation of oEGMs in HD grid of electrodes is proposed. This approach is superior to the existing methods and avoids pitfalls not yet resolved. RELEVANCE: Robust tools for quantifying the cardiac substrate are crucial to determine with accuracy target ablation sites during an electrophysiological procedure.

Non-invasive characterisation of macroreentrant atrial tachycardia types from a vectorcardiographic approach with the slow conduction region as a cornerstone.

BACKGROUND AND OBJECTIVES: Macroreentrant atrial tachyarrhythmias (MRATs) can be caused by different reentrant circuits. The treatment for each MRAT type may require ablation at different sites, either at the right or left atria. Unfortunately, the reentrant circuit that drives the arrhythmia cannot be ascertained previous to the electrophysiological intervention. METHODS: A noninvasive approach based on the comparison of atrial vectorcardiogram (VCG) loops is proposed. An archetype for each group was created, which served as a reference to measure the similarity between loops. Methods were tested in a variety of simulations and real data obtained from the most common right (peritricuspid) and left (perimitral) macroreentrant circuits, each divided into clockwise and counterclockwise subgroups. Adenosine was administered to patients to induce transient AV block, allowing the recording of the atrial signal without the interference of ventricular signals. From the vectorcardiogram, we measured intrapatient loop consistence, similarity of the pathway to archetypes, characterisation of slow velocity regions and pathway complexity. RESULTS: Results show a considerably higher similarity with the loop of its corresponding archetype, in both simulations and real data. We found the capacity of the vectorcardiogram to reflect a slow velocity region, consistent with the mechanisms of MRAT, and the role that it plays in the characterisation of the reentrant circuit. The intra-patient loop consistence was over 0.85 for all clinical cases while the similarity of the pathway to archetypes was found to be 0.85 ± 0.03, 0.95 ± 0.03, 0.87 ± 0.04 and 0.91 ± 0.02 for the different MRAT types (and p<0.02 for 3 of the 4 groups), and pathway complexity also allowed to discriminate among cases (with p<0.05). CONCLUSIONS: We conclude that the presented methodology allows us to differentiate between the most common forms of right and left MRATs and predict the existence and location of a slow conduction zone. This approach may be useful in planning ablation procedures in advance.