Arrhythmias

Identification of A0 minimum ablative margins for colorectal liver metastases: multicentre, retrospective study using deformable CT registration and artificial intelligence-based autosegmentation.

BACKGROUND: Several ablation confirmation software methods for minimum ablative margin assessment have recently been developed to improve local outcomes for patients undergoing thermal ablation of colorectal liver metastases. Previous assessments were limited to single institutions mostly at the place of development. The aim of this study was to validate the previously identified 5 mm minimum abla...

Deep Learning-Based Electrocardiogram Analysis Predicts Biventricular Dysfunction and Dilation in Congenital Heart Disease.

BACKGROUND: Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis shows promise to detect biventricular pathophysiology. However, AI-ECG analysis remains underexplored in congenital heart disease (CHD).

[Early classification and recognition algorithm for sudden cardiac arrest based on limited electrocardiogram data trained with a two-stages convolutional neural network].

Sudden cardiac arrest (SCA) is a lethal cardiac arrhythmia that poses a serious threat to human life and health. However, clinical records of sudden c...

[Detection model of atrial fibrillation based on multi-branch and multi-scale convolutional networks].

Atrial fibrillation (AF) is a life-threatening heart condition, and its early detection and treatment have garnered significant attention from physici...

Evaluating the Predictive Features of Person-Centric Knowledge Graph Embeddings: Unfolding Ablation Studies.

Developing novel predictive models with complex biomedical information is challenging due to various idiosyncrasies related to heterogeneity, standard...

A Novel Method and Python Library for ECG Signal Quality Assessment.

Electrocardiogram (ECG) is one of the reference cardiovascular diagnostic exams. However, the ECG signal is very prone to being distorted through diff...

AcousAF: Acoustic Sensing-Based Atrial Fibrillation Detection System for Mobile Phones

Atrial fibrillation (AF) is characterized by irregular electrical impulses originating in the atria, which can lead to severe complications and even...

VizECGNet: Visual ECG Image Network for Cardiovascular Diseases Classification with Multi-Modal Training and Knowledge Distillation

An electrocardiogram (ECG) captures the heart's electrical signal to assess various heart conditions. In practice, ECG data is stored as either digi...

Innovative approaches to atrial fibrillation prediction: should polygenic scores and machine learning be implemented in clinical practice?

Atrial fibrillation (AF) prediction and screening are of important clinical interest because of the potential to prevent serious adverse events. Devic...

Quantifying variabilities in cardiac digital twin models of the electrocardiogram

Cardiac digital twins (CDTs) of human cardiac electrophysiology (EP) are digital replicas of patient hearts that match like-for-like clinical observ...

Multi-Modal Dataset Creation for Federated Learning with DICOM Structured Reports

Purpose: Federated training is often hindered by heterogeneous datasets due to divergent data storage options, inconsistent naming schemes, varied a...

Transformer Circuit Faithfulness Metrics are not Robust

Mechanistic interpretability work attempts to reverse engineer the learned algorithms present inside neural networks. One focus of this work has bee...

MDDBranchNet: A Deep Learning Model for Detecting Major Depressive Disorder Using ECG Signal.

Major depressive disorder (MDD) is a chronic mental illness which affects people's well-being and is often detected at a later stage of depression wit...

Unlocking Hidden Risks: Harnessing Artificial Intelligence (AI) to Detect Subclinical Conditions from an Electrocardiogram (ECG).

Recent artificial intelligence (AI) advancements in cardiovascular medicine offer potential enhancements in diagnosis, prediction, treatment, and outc...

A CNN and Transformer Hybrid Network for Multi-Class Arrhythmia Detection from Photoplethysmography.

Photoplethysmography (PPG)-based arrhythmia detection methods have gained attention with wearable technology, enabling early detection of undiagnosed ...

ECG-based Daily Activity Recognition Using 1D Convolutional Neural Networks.

This study presents an approach to human activity recognition (HAR) using electrocardiogram (ECG) signals. We explore the application of ECG for not o...

Federated Learning for Enhanced ECG Signal Classification with Privacy Awareness.

This paper presents a novel approach for classifying electrocardiogram (ECG) signals in healthcare applications using federated learning and stacked c...

Explainable Multimodal Deep Learning for Heart Sounds and Electrocardiogram Classification.

We introduce a Gradient-weighted Class Activation Mapping (Grad-CAM) methodology to assess the performance of five distinct models for binary classifi...

A Hybrid GCN-LSTM Model for Ventricular Arrhythmia Classification Based on ECG Pattern Similarity.

Accurate differentiation between Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF) is essential in the field of cardiology. Recent advanc...

ECG Abnormality Detection Using MIMIC-IV-ECG Data Via Supervised Contrastive Learning.

Electrocardiogram data provide a tremendous opportunity for the detection of various types of cardiac arrhythmia. Recent advancement in ubiquitous wea...