A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New method uncovers small but significant local activities in time-series data.
Performing inference over simulators is generally intractable as their runtime means we cannot compute a marginal likelihood. We develop a likelihood-free inference method to infer parameters for a cardiac simulator, which replicates electrical flow through the heart to the body surface. We improve the fit of a state-o…
This paper is an attempt to separate cardiac and respiratory signals from an electrical bio-impedance (EBI) dataset. For this two well-known algorithms, namely Principal Component Analysis (PCA) and Independent Component Analysis (ICA), were used to accomplish the task. The ability of the PCA and the ICA methods first …
Probabilistic method identifies Purkinje network from ECG data.
We propose a method based on deep learning to perform cardiac segmentation on short axis MRI image stacks iteratively from the top slice (around the base) to the bottom slice (around the apex). At each iteration, a novel variant of U-net is applied to propagate the segmentation of a slice to the adjacent slice below it…
Intricating cardiac complexities are the primary factor associated with healthcare costs and the highest cause of death rate in the world. However, preventive measures like the early detection of cardiac anomalies can prevent severe cardiovascular arrests of varying complexities and can impose a substantial impact on h…
The paper highlights the importance of model discrepancy in cardiac simulations.
Study finds AI can predict diverse cardiac and non-cardiac diagnoses from a single ECG.
AdaCGP learns dynamic graph topology from time series data, improving over existing methods.
Maintaining good cardiac function for as long as possible is a major concern for healthcare systems worldwide and there is much interest in learning more about the impact of different risk factors on cardiac health. The aim of this study is to analyze the impact of systolic blood pressure (SBP) on cardiac function whil…
This paper evaluates the impact of the power extent on price in the electricity market. The competitiveness extent of the electricity market during specific times in a day is considered to achieve this. Then, the effect of competitiveness extent on the forecasting precision of the daily power price is assessed. A price…
Cardiac motion modeling using LDDMM and shape splines.
The paper derives uncertainty quantification for ML models used in metrology.
In this work, a novel approach is proposed for joint analysis of high dimensional time-resolved cardiac motion features obtained from segmented cardiac MRI and low dimensional clinical risk factors to improve survival prediction in heart failure. Different methods are evaluated to find the optimal way to insert convent…
An electrocardiogram (EKG) is a common, non-invasive test that measures the electrical activity of a patient's heart. EKGs contain useful diagnostic information about patient health that may be absent from other electronic health record (EHR) data. As multi-dimensional waveforms, they could be modeled using generic mac…
Large prospective epidemiological studies acquire cardiovascular magnetic resonance (CMR) images for pre-symptomatic populations and follow these over time. To support this approach, fully automatic large-scale 3D analysis is essential. In this work, we propose a novel deep neural network using both CMR images and pati…
The exploitation of large-scale population data has the potential to improve healthcare by discovering and understanding patterns and trends within this data. To enable high throughput analysis of cardiac imaging data automatically, a pipeline should comprise quality monitoring of the input images, segmentation of the …
Novel framework monitors cardiac image segmentation models in real-time.
Method learns molecular Hamiltonian for accurate electron dynamics predictions.
We propose a method to classify cardiac pathology based on a novel approach to extract image derived features to characterize the shape and motion of the heart. An original semi-supervised learning procedure, which makes efficient use of a large amount of non-segmented images and a small amount of images segmented manu…
hyperSBINN improves drug cardiosafety assessment by efficiently modeling cardiac action potentials.
The segmentation of the left ventricle (LV) from CINE MRI images is essential to infer important clinical parameters. Typically, machine learning algorithms for automated LV segmentation use annotated contours from only two cardiac phases, diastole, and systole. In this work, we present an analysis work-flow for fully-…
Radiomics identifies subtle cardiac changes in hypertension.
GANs improve echo frame generation with labeled patches.
CardiacGen generates realistic ECG signals for training deep learning models.
Method identifies cardiac ectopic activity sites from 12-lead ECG.
Researchers use operator learning to predict cardiac activation and repolarization times.
We introduce a model-based reconstruction framework with deep learned (DL) and smoothness regularization on manifolds (STORM) priors to recover free breathing and ungated (FBU) cardiac MRI from highly undersampled measurements. The DL priors enable us to exploit the local correlations, while the STORM prior enables us …
We train an enhanced deep convolutional neural network in order to identify eight cardiac abnormalities from the standard 12-lead electrocardiograms (ECGs) using the dataset of 14000 ECGs. Instead of straightforwardly applying an end-to-end deep learning approach, we find that deep convolutional neural networks enhance…
Method synthesizes 4D CMR images from XCAT model using GAN and SPADE.
This study benchmarks algorithms for automatic segmentation of LGE-MRI images of the left atrium.
We compute the noncommutative de Rham cohomology for the finite-dimensional q-deformed coordinate ring at odd roots of unity and with its standard 4-dimensional differential structure. We find that and have three additional modes beyond the generic -case where they are 1-dimensional, while $H…
Improved cardiac arrhythmia detection in wearable devices with neural networks.
Objectives: Atrial fibrillation (AF) is a common heart rhythm disorder associated with deadly and debilitating consequences including heart failure, stroke, poor mental health, reduced quality of life and death. Having an automatic system that diagnoses various types of cardiac arrhythmias would assist cardiologists to…
CLOCS uses contrastive learning to improve cardiac signal representations.
Recent introduction of wearable single-lead ECG devices of diverse configurations has caught the intrigue of the medical community. While these devices provide a highly affordable support tool for the caregivers for continuous monitoring and to detect acute conditions, such as arrhythmia, their utility for cardiac diag…
Framework for imputing missing heart data to simulate brain-heart interactions.
Causal analysis reveals regional discrepancies in TOPCAT trial results.
Deep learning models trained on adult cardiac MRI data struggle to accurately segment rare congenital heart diseases.
Novel method embeds generative model into Bayesian optimization for HD cardiac model parameter estimation.
Cardiovascular disease (CVD) is the global leading cause of death. A strong risk factor for CVD events is the amount of coronary artery calcium (CAC). To meet demands of the increasing interest in quantification of CAC, i.e. coronary calcium scoring, especially as an unrequested finding for screening and research, auto…
DRUM transfers cardiac arrest models across registries with missing covariates.
Study converts echocardiography views using adversarial models.
Background: Cardiac MRI derived biventricular mass and function parameters, such as end-systolic volume (ESV), end-diastolic volume (EDV), ejection fraction (EF), stroke volume (SV), and ventricular mass (VM) are clinically well established. Image segmentation can be challenging and time-consuming, due to the complex a…
SoQal uses selective oracle questioning to improve active learning of cardiac signals.
Study electric field and potential of torus knots, focusing on z-axis.
LU-Net improves cardiac segmentation accuracy and robustness.
Study builds models to predict post-cardiac arrest outcomes using patient data.