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Background and Objectives: Accurate diagnosis of cardiovascular diseases often relies on the electrocardiogram (ECG). Since the cardiac vector is located within a three-dimensional space and the standard ECG comprises 12 projections or leads derived from it, redundant information is inherently present. This study aims to quantify this redundancy and its impact on classification tasks using Convolutional Neural Networks (CNNs) in cardiovascular diseases., Methods: We employed signal theory and mutual information to introduce a novel redundancy metric and explored techniques for redundancy augmentation and reduction. This involved lead selection and transformation to evaluate the effects on neural network performance., Results: Our findings indicate that optimizing input configurations through redundancy reduction techniques can enhance the performance of deep learning models in cardiovascular diagnostics, provided that the information is preserved and minimally distorted., Conclusion: For the first time, this research has quantified the redundancy present in the input by validating various redundancy reduction techniques using a CNN. This discovery paves the way for advancing biomedical signal processing research, simplifying model complexity, and enhancing diagnostic performance in cardiovascular medicine within reduced lead systems, such as Holter monitors or wearables., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Ramirez, Ruiperez-Campillo, Casado-Arroyo, Merino, Vogt, Castells and Millet.)

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., Competing Interests: The authors of this manuscript declare no conflicts of interest., (© 2023 The Authors.)

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., (© 2023. The Author(s).)

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.

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.

The development of high-density multielectrode catheters has significantly advanced cardiac electrophysiology mapping. High-density grid catheters have enabled the creation of a novel technique for reconstructing electrogram (EGM) signals known as "omnipole," which is believed to be more reliable than other methods, especially in terms of orientation independence. This study aims to evaluate how distance affects the omnipolar reconstruction of EGMs by comparing different configurations. Using an animal set up of perfused isolated rabbit hearts, recordings were taken using an ad hoc high-density epicardial multielectrode catheter. Inter-electrode distances ranging from 1 to 4 mm were analysed for their effect on the quality of resulting EGMs. Two biomarkers were computed to evaluate the robustness of the reconstructions: the areas contained within the bipolar loops and the amplitudes of the omnipoles. We hypothesised that both bipolar and omnipolar electrograms would be more robust at shorter inter-electrode distances. The results showed that an increase in distance triggers an increase in loop areas and amplitudes, which supports the hypothesis. This finding provides a more reliable estimate of wavefront propagation for the cross-omnipolar reconstruction method. These results emphasise the importance of distance in cardiac electrophysiology mapping and provide valuable insights into the use of high-density multielectrode catheters for EGM reconstruction.Clinical Relevance- The results of this study have direct clinical relevance in the application of the described techniques to recording systems in the cardiac electrophysiology laboratory, enabling clinicians to obtain more precise characterisation of signals in the myocardium.

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., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr. JLM reports receiving speaker fees, honoraria, consultancy, advisory board fees, investigator, committee member, etc. from Medtronic, Microport, Sanofi Aventis, Boston Scientific and Abbott, Dr. RCA reports educational talks for Abbott and Boston scientific. Drs. FC, RC, JM and Mr. SRC and IS report no disclosures., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Objective . Detecting different cardiac diseases using a single or reduced number of leads is still challenging. This work aims to provide and validate an automated method able to classify ECG recordings. Performance using complete 12-lead systems, reduced lead sets, and single-lead ECGs is evaluated and compared. Approach . Seven different databases with 12-lead ECGs were provided during the PhysioNet/Computing in Cardiology Challenge 2021, where 88 253 annotated samples associated with none, one, or several cardiac conditions among 26 different classes were released for training, whereas 42 896 hidden samples were used for testing. After signal preprocessing, 81 features per ECG-lead were extracted, mainly based on heart rate variability, QRST patterns and spectral domain. Next, a One-versus-Rest classification approach made of independent binary classifiers for each cardiac condition was trained. This strategy allowed each ECG to be classified as belonging to none, one or several classes. For each class, a classification model among two binary supervised classifiers and one hybrid unsupervised-supervised classification system was selected. Finally, we performed a 3-fold cross-validation to assess the system's performance. Main results . Our classifiers received scores of 0.39, 0.38, 0.39, 0.38, and 0.37 for the 12, 6, 4, 3 and 2-lead versions of the hidden test set with the Challenge evaluation metric (CM). Also, we obtained a meanG-score of 0.80, 0.78, 0.79, 0.79, 0.77 and 0.74 for the 12, 6, 4, 3, 2 and 1-lead subsets with the public training set during our 3-fold cross-validation. Significance . We proposed and tested a machine learning approach focused on flexibility for identifying multiple cardiac conditions using one or more ECG leads. Our minimal-lead approach may be beneficial for novel portable or wearable ECG devices used as screening tools, as it can also detect multiple and concurrent cardiac conditions., (Creative Commons Attribution license.)