Cardiac Activation Maps Reconstruction: A Comparative Study Between Data-Driven and Physics-Based Methods

Deep learning based estimation of heart surface potentials

Electrocardiographic imaging (ECGI) aims to noninvasively estimate heart surface potentials starting from body surface potentials. This is classically based on geometric information on the torso and the heart from imaging, which complicates clinical application. In this study, we aim to develop a deep learning framework to estimate heart surface potentials solely from body surface potentials, enabling wider clinical use. The framework introduces two main components: the transformation of 3D torso and heart geometries into standard 2D representations, and the development of a customized deep learning network model. The 2D torso and heart representations maintain a consistent layout across different subjects, making the proposed framework applicable to different torso-heart geometries. With spatial information incorporated in the 2D representations, the torso-heart physiological relationship can be learnt by the network. The deep learning model is based on a Pix2Pix network, adapted to work with 2.5D data in our task, i.e., 2D body surface potential maps (BSPMs) and 2D heart surface potential maps (HSPMs) with time sequential information. We propose a new loss function tailored to this specific task, which uses a cosine similarity and different weights for different inputs. BSPMs and HSPMs from 11 healthy subjects (8 females and 3 males) and 29 idiopathic ventricular fibrillation (IVF) patients (11 females and 18 males) were used in this study. Performance was assessed on a test set by measuring the similarity and error between the output of the proposed model and the solution provided by mainstream ECGI, by comparing HSPMs, the concatenated electrograms (EGMs), and the estimated activation time (AT) and recovery time (RT). The mean of the mean absolute error (MAE) for the HSPMs was 0.012 ± 0.011, and the mean of the corresponding structural similarity index measure (SSIM) was 0.984 ± 0.026. The mean of the MAE for the EGMs was 0.004 ± 0.004, and the mean of the corresponding Pearson correlation coefficient (PCC) was 0.643 ± 0.352. Results suggest that the model is able to precisely capture the structural and temporal characteristics of the HSPMs. The mean of the absolute time differences between estimated and reference activation times was 6.048 ± 5.188 ms, and the mean of the absolute differences for recovery times was 18.768 ± 17.299 ms. Overall, results show similar performance between the proposed model and standard ECGI, exhibiting low error and consistent clinical patterns, without the need for CT/MRI. The model shows to be effective across diverse torso-heart geometries, and it successfully integrates temporal information in the input. This in turn suggests the possible use of this model in cost effective clinical scenarios like patient screening or post-operative follow-up.

3D Spatiotemporal Activation Mapping of Cardiac Organoids Using Conformal Shell Microelectrode Arrays (MEAs)

Cardiac organoids have emerged as transformative models for investigating cardiogenesis and cardiac diseases. While traditional 2D microelectrode arrays (MEAs) have been used to assess the functionality of cardiac organoids, they are limited to electrophysiological measurements from a single plane and do not capture the 3D propagation of electrical signals. Here, we present a programmable, shape-adaptive shell MEA designed to map the electrical activity across the entire surface of cardiac organoids. These shell MEAs are fabricated on-chip, with tunable dimensions and electrode layout, enabling precise encapsulation of spherical organoids. Using shell MEAs, we generated 3D isochrone maps with conduction velocity vectors, revealing the speed and trajectory of electrical signal propagation in spontaneously beating cardiac organoids. The optical transparency of the shell MEAs allowed for simultaneous calcium imaging, validating the electrophysiological propagation pattern. To demonstrate their utility in cardiotoxicity screening, we monitored the electrophysiological changes of organoids treated with isoproterenol and E-4031 over nine days. We anticipate that shell MEAs, combined with spatiotemporal mapping, can significantly advance the development of spatially organized cardiac organoids, structural disease models, and high-throughput drug screening platforms.

Solving the Inverse Problem of Electrocardiography for Cardiac Digital Twins: A Survey

Cardiac digital twins (CDTs) are personalized virtual representations used to understand complex cardiac mechanisms. A critical component of CDT development is solving the ECG inverse problem, which enables the reconstruction of cardiac sources and the estimation of patient-specific electrophysiology (EP) parameters from surface ECG data. Despite challenges from complex cardiac anatomy, noisy ECG data, and the ill-posed nature of the inverse problem, recent advances in computational methods have greatly improved the accuracy and efficiency of ECG inverse inference, strengthening the fidelity of CDTs. This paper aims to provide a comprehensive review of the methods for solving ECG inverse problems, their validation strategies, their clinical applications, and their future perspectives. For the methodologies, we broadly classify state-of-the-art approaches into two categories: deterministic and probabilistic methods, including both conventional and deep learning-based techniques. Integrating physics laws with deep learning models holds promise, but challenges such as capturing dynamic electrophysiology accurately, accessing accurate domain knowledge, and quantifying prediction uncertainty persist. Integrating models into clinical workflows while ensuring interpretability and usability for healthcare professionals is essential. Overcoming these challenges will drive further research in CDTs.

A review of evaluation approaches for explainable AI with applications in cardiology

Explainable artificial intelligence (XAI) elucidates the decision-making process of complex AI models and is important in building trust in model predictions. XAI explanations themselves require evaluation as to accuracy and reasonableness and in the context of use of the underlying AI model. This review details the evaluation of XAI in cardiac AI applications and has found that, of the studies examined, 37% evaluated XAI quality using literature results, 11% used clinicians as domain-experts, 11% used proxies or statistical analysis, with the remaining 43% not assessing the XAI used at all. We aim to inspire additional studies within healthcare, urging researchers not only to apply XAI methods but to systematically assess the resulting explanations, as a step towards developing trustworthy and safe models.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10462-024-10852-w.

Hybrid Neural State-Space Modeling for Supervised and Unsupervised Electrocardiographic Imaging

State-space modeling (SSM) provides a general framework for many image reconstruction tasks. Error in a priori physiological knowledge of the imaging physics, can bring incorrectness to solutions. Modern deep-learning approaches show great promise but lack interpretability and rely on large amounts of labeled data. In this paper, we present a novel hybrid SSM framework for electrocardiographic imaging (ECGI) to leverage the advantage of state-space formulations in data-driven learning. We first leverage the physics-based forward operator to supervise the learning. We then introduce neural modeling of the transition function and the associated Bayesian filtering strategy. We applied the hybrid SSM framework to reconstruct electrical activity on the heart surface from body-surface potentials. In unsupervised settings of both in-silico and in-vivo data without cardiac electrical activity as the ground truth to supervise the learning, we demonstrated improved ECGI performances of the hybrid SSM framework trained from a small number of ECG observations in comparison to the fixed SSM. We further demonstrated that, when in-silico simulation data becomes available, mixed supervised and unsupervised training of the hybrid SSM achieved a further 40.6% and 45.6% improvements, respectively, in comparison to traditional ECGI baselines and supervised data-driven ECGI baselines for localizing the origin of ventricular activations in real data.

Recurrence quantification analysis for fine-scale characterisation of arrhythmic patterns in cardiac tissue

This paper uses recurrence quantification analysis (RQA) combined with entropy measures and organization indices to characterize arrhythmic patterns and dynamics in computer simulations of cardiac tissue. We performed different simulations of cardiac tissues of sizes comparable to the human heart atrium. In these simulations, we observed four classic arrhythmic patterns: a spiral wave anchored to a highly fibrotic region resulting in sustained re-entry, a meandering spiral wave, fibrillation, and a spiral wave anchored to a scar region that breaks up into wavelets away from the main rotor. A detailed analysis revealed that, within the same simulation, maps of RQA metrics could differentiate regions with regular AP propagation from ones with chaotic activity. In particular, the combination of two RQA metrics, the length of the longest diagonal string of recurrence points and the mean length of diagonal lines, was able to identify the location of rotor tips, which are the active elements that maintain spiral waves and fibrillation. By proposing low-dimensional models based on the mean value and spatial correlation of metrics calculated from membrane potential time series, we identify RQA-based metrics that successfully separate the four different types of cardiac arrhythmia into distinct regions of the feature space, and thus might be used for automatic classification, in particular distinguishing between fibrillation driven by self-sustaining chaos and that created by a persistent rotor and wavebreak. We also discuss the practical applicability of such an approach.

Electrocardiographic imaging in the atria

The inverse problem of electrocardiography or electrocardiographic imaging (ECGI) is a technique for reconstructing electrical information about cardiac surfaces from noninvasive or non-contact recordings. ECGI has been used to characterize atrial and ventricular arrhythmias. Although it is a technology with years of progress, its development to characterize atrial arrhythmias is challenging. Complications can arise when trying to describe the atrial mechanisms that lead to abnormal propagation patterns, premature or tachycardic beats, and reentrant arrhythmias. This review addresses the various ECGI methodologies, regularization methods, and post-processing techniques used in the atria, as well as the context in which they are used. The current advantages and limitations of ECGI in the fields of research and clinical diagnosis of atrial arrhythmias are outlined. In addition, areas where ECGI efforts should be concentrated to address the associated unsatisfied needs from the atrial perspective are discussed.