Estimating Cardiac Tissue Conductivity from Electrograms with Fully Convolutional Networks

• URL: http://arxiv.org/abs/2212.03012v1
• Date: Tue, 6 Dec 2022 14:37:59 GMT
• Title: Estimating Cardiac Tissue Conductivity from Electrograms with Fully Convolutional Networks
• Authors: Konstantinos Ntagiantas (1), Eduardo Pignatelli (1), Nicholas S. Peters (2), Chris D. Cantwell (3), Rasheda A.Chowdhury (2), Anil A. Bharath (1) ((1) Department of Bioengineering, Imperial College London, (2) National Heart and Lung Institute, Imperial College London, (3) Department of Aeronautics, Imperial College London)
• Abstract summary: Atrial Fibrillation (AF) is characterized by disorganised electrical activity in the atria. Estimating the effective conductivity of myocardium and identifying regions of abnormal propagation is crucial for the effective treatment of AF.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Atrial Fibrillation (AF) is characterized by disorganised electrical activity in the atria and is known to be sustained by the presence of regions of fibrosis (scars) or functional cellular remodeling, both of which may lead to areas of slow conduction. Estimating the effective conductivity of the myocardium and identifying regions of abnormal propagation is therefore crucial for the effective treatment of AF. We hypothesise that the spatial distribution of tissue conductivity can be directly inferred from an array of concurrently acquired contact electrograms (EGMs). We generate a dataset of simulated cardiac AP propagation using randomised scar distributions and a phenomenological cardiac model and calculate contact electrograms at various positions on the field. A deep neural network, based on a modified U-net architecture, is trained to estimate the location of the scar and quantify conductivity of the tissue with a Jaccard index of $91$%. We adapt a wavelet-based surrogate testing analysis to confirm that the inferred conductivity distribution is an accurate representation of the ground truth input to the model. We find that the root mean square error (RMSE) between the ground truth and our predictions is significantly smaller ($p_{val}=0.007$) than the RMSE between the ground truth and surrogate samples.

Related papers

• Ensemble learning of the atrial fiber orientation with physics-informed neural networks [0.0]
  We propose Fibernet, a method for the automatic identification of the anisotropic conduction -- and thus fibers -- in the atria from local electrical recordings. We use an ensemble of neural networks to produce multiple samples, all fitting the observed data, and compute posterior statistics. Our approach can estimate the fiber orientation and conduction velocities in under 7 minutes with quantified uncertainty.
  arXiv  Detail & Related papers  (2024-10-30T18:45:19Z)
• Predicting Cascading Failures with a Hyperparametric Diffusion Model [66.89499978864741]
  We study cascading failures in power grids through the lens of diffusion models. Our model integrates viral diffusion principles with physics-based concepts. We show that this diffusion model can be learned from traces of cascading failures.
  arXiv  Detail & Related papers  (2024-06-12T02:34:24Z)
• EKGNet: A 10.96{\mu}W Fully Analog Neural Network for Intra-Patient Arrhythmia Classification [79.7946379395238]
  We present an integrated approach by combining analog computing and deep learning for electrocardiogram (ECG) arrhythmia classification. We propose EKGNet, a hardware-efficient and fully analog arrhythmia classification architecture that archives high accuracy with low power consumption.
  arXiv  Detail & Related papers  (2023-10-24T02:37:49Z)
• Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference [6.447210290674733]
  We present a novel deep computational model, comprising a dual-branch variational autoencoder and an inference model, to infer infarct location and distribution from the simulated QRS. The sensitivity analysis enhances our understanding of the complex relationship between infarct characteristics and electrophysiological features.
  arXiv  Detail & Related papers  (2023-07-10T08:54:12Z)
• Flexible Amortized Variational Inference in qBOLD MRI [56.4324135502282]
  Oxygen extraction fraction (OEF) and deoxygenated blood volume (DBV) are more ambiguously determined from the data. Existing inference methods tend to yield very noisy and underestimated OEF maps, while overestimating DBV. This work describes a novel probabilistic machine learning approach that can infer plausible distributions of OEF and DBV.
  arXiv  Detail & Related papers  (2022-03-11T10:47:16Z)
• Deep Spatio-temporal Sparse Decomposition for Trend Prediction and Anomaly Detection in Cardiac Electrical Conduction [11.076265159072229]
  We propose a deep-temporal decomposition (DSTSD) approach to bypass the time-consuming cardiac partial differential equations. This approach is validated from the data set generated from the Courtemanche-amirez-Nattel neuron (CRN) model.
  arXiv  Detail & Related papers  (2021-09-20T06:38:50Z)
• Localization of Cochlear Implant Electrodes from Cone Beam Computed Tomography using Particle Belief Propagation [23.934214668896157]
  Cochlear implants (CIs) are implantable medical devices that can restore the hearing sense of people suffering from profound hearing loss. The exact location of these electrodes may be an important parameter to improve and predict the performance with these devices. We propose a Markov random field (MRF) model for CI electrode localization for cone beam computed tomography (CBCT) datasets.
  arXiv  Detail & Related papers  (2021-03-18T15:39:23Z)
• Diffusion Earth Mover's Distance and Distribution Embeddings [61.49248071384122]
  Diffusion can be computed in $tildeO(n)$ time and is more accurate than similarly fast algorithms such as tree-baseds. We show Diffusion is fully differentiable, making it amenable to future uses in gradient-descent frameworks such as deep neural networks.
  arXiv  Detail & Related papers  (2021-02-25T13:18:32Z)
• Influence of segmentation accuracy in structural MR head scans on electric field computation for TMS and tES [2.750124853532831]
  In brain, sensitivity to segmentation accuracy is relatively high in cerebrospinal fluid (CSF), moderate in gray matter (GM) and low in white matter for transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES) A CSF segmentation accuracy reduction of 10% in terms of Dice coefficient (DC) lead to decrease up to 4% in normalized induced electric field in both applications. However, a GM segmentation accuracy reduction of 5.6% DC leads to increase of normalized induced electric field up to 6%.
  arXiv  Detail & Related papers  (2020-09-25T03:38:24Z)
• Network Diffusions via Neural Mean-Field Dynamics [52.091487866968286]
  We propose a novel learning framework for inference and estimation problems of diffusion on networks. Our framework is derived from the Mori-Zwanzig formalism to obtain an exact evolution of the node infection probabilities. Our approach is versatile and robust to variations of the underlying diffusion network models.
  arXiv  Detail & Related papers  (2020-06-16T18:45:20Z)
• Simultaneous Skull Conductivity and Focal Source Imaging from EEG Recordings with the help of Bayesian Uncertainty Modelling [77.34726150561087]
  We propose a statistical method based on the Bayesian approximation error approach to compensate for source imaging errors due to the unknown skull conductivity. Results indicate clear improvements in the source localization accuracy and feasible skull conductivity estimates.
  arXiv  Detail & Related papers  (2020-01-31T21:33:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.