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.
Enhanced deep CNNs improve cardiac abnormality diagnosis from ECGs.
Radiomics approach improves cardiac CVD diagnosis from cine-MRI.
This study benchmarks changepoint detection algorithms on cardiac time series data.
New method uncovers small but significant local activities in time-series data.
Heart diseases constitute a global health burden, and the problem is exacerbated by the error-prone nature of listening to and interpreting heart sounds. This motivates the development of automated classification to screen for abnormal heart sounds. Existing machine learning-based systems achieve accurate classificatio…
Early recognition of abnormal rhythms in ECG signals is crucial for monitoring and diagnosing patients' cardiac conditions, increasing the success rate of the treatment. Classifying abnormal rhythms into exact categories is very challenging due to the broad taxonomy of rhythms, noises and lack of large-scale real-world…
Study finds AI can predict diverse cardiac and non-cardiac diagnoses from a single ECG.
Study analyzes how blood pressure impacts cardiac health.
Cardiac motion modeling using LDDMM and shape splines.
Proposes a hybrid deep learning network for better heart failure survival prediction.
New method detects change points in quasi-periodic signals without supervision.
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.
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.
ODE trajectories become abnormal curves in Carnot groups.
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 …
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.
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…
The classification of time series data is a well-studied problem with numerous practical applications, such as medical diagnosis and speech recognition. A popular and effective approach is to classify new time series in the same way as their nearest neighbours, whereby proximity is defined using Dynamic Time Warping (D…
Study finds abnormal paths on specific Lie groups using algebraic structures.
Improved cardiac arrhythmia detection in wearable devices with neural networks.
Study on abnormal curves in sub-Riemannian manifolds, proving length-minimizing properties.
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.
The paper bounds abnormal and Goh-abnormal sets for metabelian Lie groups with polarizations.
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.
We prove the smoothness of abnormal minimizers of subriemannian manifolds of step 3 with a nilpotent basis. We prove that rank 2 Carnot groups of step 4 admit no strictly abnormal minimizers. For any subriemannian manifolds of step less than 7, we show all abnormal minimizers have no corner type singularities, which pa…
Researchers found abnormal extremals on specific Lie groups.
Causal analysis reveals regional discrepancies in TOPCAT trial results.
We address the problem of abnormal event detection from trajectory data. In this paper, a new adversarial approach is proposed for building a deep neural network binary classifier, trained in an unsupervised fashion, that can distinguish normal from abnormal trajectory-based events without the need for setting manual d…
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 …
New optimality conditions for sub-Riemannian geodesics derived.
Deep learning models trained on adult cardiac MRI data struggle to accurately segment rare congenital heart diseases.
Study on sub-Riemannian geometry in 4D, focusing on abnormal geodesics.
What happens when the Supreme Court of the United States decides a case impacting one or more publicly-traded firms? While many have observed anecdotal evidence linking decisions or oral arguments to abnormal stock returns, few have rigorously or systematically investigated the behavior of equities around Supreme Court…
Media tone around earnings announcements predicts stock returns.
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…