Arrhythmias

Deep Learning Decodes Latent ECG Signatures of Stress Cardiomyopathy

Background Stress cardiomyopathy (SCM) shares features with acute myocardial infarction (AMI) which may lead to misdiagnosis and misaligned management decisions. We hypothesize that features derived from the 12-lead ECG can identify cases of SCM and differentiate them from AMI. Methods Using data from a large registry of critically ill patients, we trained a deep learning algorithm to perform two ...

Time-series ECG Imputation Using a Pattern-Based Masking Framework

The utilization of continuous ECG monitoring has become an integral part of modern hospital-based care. However, missing data presents significant challenges in deploying real-time ECG-based predictive systems. Research on the implementation of imputation techniques on time-series ECG is limited. Furthermore, the performance of imputation techniques is typically benchmarked using random masking, w...

Leveraging Explainable Temporal-Modelling Machine Learning to Identify Distinct Multimorbidity Trajectory Profiles in Acute Myocardial Infarction

IntroductionAcute myocardial infarction (AMI) remains a leading cause of mortality, with the coexistence of other conditions (i.e., multimorbidity) co...

Robust and Generalizable Atrial Fibrillation Detection from ECG Using Time-Frequency Fusion and Supervised Contrastive Learning

Atrial fibrillation (AF) is a common cardiac arrhythmia that significantly increases the risk of stroke and heart failure, necessitating reliable and ...

Deep learning enables diagnosis of atrial cardiomyopathy from routine 12-lead electrocardiogram

BackgroundAtrial cardiomyopathy (AtCM) is both a cause and a consequence of atrial fibrillation and flutter (AF) and can lead to ischemic stroke. Imag...

Formation and Regulation of Calcium Sparks on a Nonlinear Spatial Network of Ryanodine Receptors

Accurate regulation of calcium release is essential for cellular signaling, with the spatial distribution of ryanodine receptors (RyRs) playing a cr...

Detection of Intelligent Tampering in Wireless Electrocardiogram Signals Using Hybrid Machine Learning

With the proliferation of wireless electrocardiogram (ECG) systems for health monitoring and authentication, protecting signal integrity against tam...

Self-supervised learning for low-dose CT image denoising method based on guided image filtering.

low-dose computed tomography (LDCT) images suffer from severe noise due to reduced radiation exposure. Most existing deep learning-based denoising met...

Cardiorespiratory coupling improves cardiac pumping efficiency in heart failure

Recent trials of a neuronal pacemaker have shown that cardiac pumping efficiency increases when respiratory sinus arrhythmia (RSA) is artificially r...

Not All Attention Heads Are What You Need: Refining CLIP's Image Representation with Attention Ablation

This paper studies the role of attention heads in CLIP's image encoder. While CLIP has exhibited robust performance across diverse applications, we ...

Traditional Chinese Medicine for Anti-Arrhythmias: Mechanisms via Potassium Channels.

Cardiac arrhythmia is a common life-threatening cardiovascular disorder. Potassium channels play a crucial role in cardiac electrophysiology, and thei...

Myocardial Infarction Detection using Variational Mode Decomposition with Fuzzy Weight Particle Swarm Optimization and Depthwise Separable Convolutional Network.

The challenge of precisely recognizing myocardial infarction (MI) from electrocardiographic (ECG) readings stems from the complex nature of these sign...

Turbulence control in memristive neural network via adaptive magnetic flux based on DLS-ADMM technique.

High-voltage defibrillation for eliminating cardiac spiral waves has significant side effects, necessitating the pursuit of low-energy alternatives fo...

Interpretable AI for Time-Series: Multi-Model Heatmap Fusion with Global Attention and NLP-Generated Explanations

In this paper, we present a novel framework for enhancing model interpretability by integrating heatmaps produced separately by ResNet and a restruc...

Improving Myocardial Infarction Detection via Synthetic ECG Pretraining

Myocardial infarction is a major cause of death globally, and accurate early diagnosis from electrocardiograms (ECGs) remains a clinical priority. D...

Masked Autoencoders that Feel the Heart: Unveiling Simplicity Bias for ECG Analyses

The diagnostic value of electrocardiogram (ECG) lies in its dynamic characteristics, ranging from rhythm fluctuations to subtle waveform deformation...

InsertRank: LLMs can reason over BM25 scores to Improve Listwise Reranking

Large Language Models (LLMs) have demonstrated significant strides across various information retrieval tasks, particularly as rerankers, owing to t...

EXGnet: a single-lead explainable-AI guided multiresolution network with train-only quantitative features for trustworthy ECG arrhythmia classification

Background: Deep learning has significantly advanced ECG arrhythmia classification, enabling high accuracy in detecting various cardiac conditions. ...

Diffusion-Based Electrocardiography Noise Quantification via Anomaly Detection

Electrocardiography (ECG) signals are often degraded by noise, which complicates diagnosis in clinical and wearable settings. This study proposes a ...

Deep Learning-Based Digitization of Overlapping ECG Images with Open-Source Python Code

This paper addresses the persistent challenge of accurately digitizing paper-based electrocardiogram (ECG) recordings, with a particular focus on ro...